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# Awesome-LogCodeSecurity-LLMs

Welcome to **Awesome-LogCodeSecurity-LLMs**, a curated list of resources dedicated to the intersection of code, logs, and security with large language models (LLMs). This repository aims to serve as a comprehensive guide for researchers, developers, and enthusiasts who are exploring the innovative applications of LLMs in these areas.

## What You'll Find Here:

- **Code LLMs**: Resources focused on the application of LLMs in code-related tasks, enhancing areas like code generation, analysis, and comprehension.
- **Security LLMs**: Advanced methodologies that illustrate how LLMs can improve security measures, including vulnerability detection and threat analysis.
- **Log LLMs**: Insights into leveraging LLMs for log management, facilitating tasks such as log parsing, anomaly detection, and system monitoring.

Whether you're a beginner looking to get started or an expert seeking to stay updated with the latest advancements, this repository is your go-to resource.

Feel free to explore, contribute, and be inspired!

# Code LLMs
## Surveys
- [2022-12] [ACL 2023] Large Language Models Meet NL2Code: A Survey [[paper](https://arxiv.org/abs/2212.09420)]
- [2022-12] A Survey on Pretrained Language Models for Neural Code Intelligence [[paper](https://arxiv.org/abs/2212.10079)]
- [2023-02] [ICSE 2023] An Empirical Comparison of Pre-Trained Models of Source Code [[paper](https://arxiv.org/abs/2302.04026)]
- [2023-08] Large Language Models for Software Engineering: A Systematic Literature Review [[paper](https://arxiv.org/abs/2308.10620)]
- [2023-08] Towards an Understanding of Large Language Models in Software Engineering Tasks [[paper](https://arxiv.org/abs/2308.11396)]
- [2023-10] Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey [[paper](https://arxiv.org/abs/2310.17903)]
- [2023-12] A Survey on Large Language Models for Software Engineering [[paper](https://arxiv.org/abs/2401.00288)]
- [2023-12] Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit [[paper](https://arxiv.org/abs/2401.00288)]
- [2024-03] A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond [[paper](https://arxiv.org/abs/2403.14734)]
- [2024-04] Tasks People Prompt: A Taxonomy of LLM Downstream Tasks in Software Verification and Falsification Approaches [[paper](https://arxiv.org/abs/2404.09384)]
- [2024-05] Automatic Programming: Large Language Models and Beyond [[paper](https://arxiv.org/abs/2405.02213)]
- [2024-10] Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models [[paper](https://arxiv.org/abs/2410.09012)]
- [2024-10] Deep Learning-based Software Engineering: Progress, Challenges, and Opportunities [[paper](https://arxiv.org/abs/2410.13110)]

## Models

### Base LLMs and Pretraining Strategies
> not specifically trained but have coding capability
- **LaMDA**: "LaMDA: Language Models for Dialog Applications" [2022-01] [[paper](https://arxiv.org/abs/2201.08239)]

- **PaLM**: "PaLM: Scaling Language Modeling with Pathways" [2022-04] [JMLR] [[paper](https://arxiv.org/abs/2204.02311)]

- **GPT-NeoX**: "GPT-NeoX-20B: An Open-Source Autoregressive Language Model" [2022-04] [ACL 2022 Workshop on Challenges & Perspectives in Creating LLMs] [[paper](https://arxiv.org/abs/2204.06745)] [[repo](https://github.com/EleutherAI/gpt-neox)]

- **BLOOM**: "BLOOM: A 176B-Parameter Open-Access Multilingual Language Model" [2022-11] [[paper](https://arxiv.org/abs/2211.05100)] [[model](https://huggingface.co/models?search=bigscience/bloom)]

- **LLaMA**: "LLaMA: Open and Efficient Foundation Language Models" [2023-02] [[paper](https://arxiv.org/abs/2302.13971)]

- **GPT-4**: "GPT-4 Technical Report" [2023-03] [[paper](https://arxiv.org/abs/2303.08774)]

- **LLaMA 2**: "Llama 2: Open Foundation and Fine-Tuned Chat Models" [2023-07] [[paper](https://arxiv.org/abs/2307.09288)] [[repo](https://github.com/facebookresearch/llama)]

- **Phi-1.5**: "Textbooks Are All You Need II: phi-1.5 technical report" [2023-09] [[paper](https://arxiv.org/abs/2309.05463)] [[model](https://huggingface.co/microsoft/phi-1_5)]

- **Baichuan 2**: "Baichuan 2: Open Large-scale Language Models" [2023-09] [[paper](https://arxiv.org/abs/2309.10305)] [[repo](https://github.com/baichuan-inc/Baichuan2)]

- **Qwen**: "Qwen Technical Report" [2023-09] [[paper](https://arxiv.org/abs/2309.16609)] [[repo](https://github.com/QwenLM/Qwen)]

- **Mistral**: "Mistral 7B" [2023-10] [[paper](https://arxiv.org/abs/2310.06825)] [[repo](https://github.com/mistralai/mistral-src)]

- **Gemini**: "Gemini: A Family of Highly Capable Multimodal Models" [2023-12] [[paper](https://arxiv.org/abs/2312.11805)]

- **Phi-2**: "Phi-2: The surprising power of small language models" [2023-12] [[blog](https://www.microsoft.com/en-us/research/blog/phi-2-the-surprising-power-of-small-language-models/)]

- **YAYI2**: "YAYI 2: Multilingual Open-Source Large Language Models" [2023-12] [[paper](https://arxiv.org/abs/2312.14862)] [[repo](https://github.com/wenge-research/YAYI2)]

- **DeepSeek**: "DeepSeek LLM: Scaling Open-Source Language Models with Longtermism" [2024-01] [[paper](https://arxiv.org/abs/2401.02954)] [[repo](https://github.com/deepseek-ai/DeepSeek-LLM)]

- **Mixtral**: "Mixtral of Experts" [2024-01] [[paper](https://arxiv.org/abs/2401.04088)] [[blog](https://mistral.ai/news/mixtral-of-experts/)]

- **DeepSeekMoE**: "DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models" [2024-01] [[paper](https://arxiv.org/abs/2401.12246)] [[repo](https://github.com/deepseek-ai/DeepSeek-MoE)]

- **Orion**: "Orion-14B: Open-source Multilingual Large Language Models" [2024-01] [[paper](https://arxiv.org/abs/2401.06066)] [[repo](https://github.com/OrionStarAI/Orion)]

- **OLMo**: "OLMo: Accelerating the Science of Language Models" [2024-02] [[paper](https://arxiv.org/abs/2402.00838)] [[repo](https://github.com/allenai/OLMo)]

- **Gemma**: "Gemma: Open Models Based on Gemini Research and Technology" [2024-02] [[paper](https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf)] [[blog](https://blog.google/technology/developers/gemma-open-models/)]

- **Claude 3**: "The Claude 3 Model Family: Opus, Sonnet, Haiku" [2024-03] [[paper](https://www-cdn.anthropic.com/de8ba9b01c9ab7cbabf5c33b80b7bbc618857627/Model_Card_Claude_3.pdf)] [[blog](https://www.anthropic.com/news/claude-3-family)]

- **Yi**: "Yi: Open Foundation Models by 01.AI" [2024-03] [[paper](https://arxiv.org/abs/2403.04652)] [[repo](https://github.com/01-ai/Yi)]

- **Poro**: "Poro 34B and the Blessing of Multilinguality" [2024-04] [[paper](https://arxiv.org/abs/2404.01856)] [[model](https://huggingface.co/LumiOpen/Poro-34B)]

- **JetMoE**: "JetMoE: Reaching Llama2 Performance with 0.1M Dollars" [2024-04] [[paper](https://arxiv.org/abs/2404.07413)] [[repo](https://github.com/myshell-ai/JetMoE)]

- **LLaMA 3**: "The Llama 3 Herd of Models" [2024-04] [[blog](https://ai.meta.com/blog/meta-llama-3/)] [[repo](https://github.com/meta-llama/llama3)] [[paper](https://arxiv.org/abs/2407.21783)]

- **Reka Core**: "Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models" [2024-04] [[paper](https://arxiv.org/abs/2404.12387)]

- **Phi-3**: "Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone" [2024-04] [[paper](https://arxiv.org/abs/2404.14219)]

- **OpenELM**: "OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework" [2024-04] [[paper](https://arxiv.org/abs/2404.14619)] [[repo](https://github.com/apple/corenet/tree/main/projects/openelm)]

- **Tele-FLM**: "Tele-FLM Technical Report" [2024-04] [[paper](https://arxiv.org/abs/2404.16645)] [[model](https://huggingface.co/CofeAI/Tele-FLM)]

- **DeepSeek-V2**: "DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model" [2024-05] [[paper](https://arxiv.org/abs/2405.04434)] [[repo](https://github.com/deepseek-ai/DeepSeek-V2)]

- **GECKO**: "GECKO: Generative Language Model for English, Code and Korean" [2024-05] [[paper](https://arxiv.org/abs/2405.15640)] [[model](https://huggingface.co/kifai/GECKO-7B)]

- **MAP-Neo**: "MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series" [2024-05] [[paper](https://arxiv.org/abs/2405.19327)] [[repo](https://github.com/multimodal-art-projection/MAP-NEO)]

- **Skywork-MoE**: "Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models" [2024-06] [[paper](https://arxiv.org/abs/2406.06563)]

- **Xmodel-LM**: "Xmodel-LM Technical Report" [2024-06] [[paper](https://arxiv.org/abs/2406.02856)]

- **GEB**: "GEB-1.3B: Open Lightweight Large Language Model" [2024-06] [[paper](https://arxiv.org/abs/2406.09900)]

- **HARE**: "HARE: HumAn pRiors, a key to small language model Efficiency" [2024-06] [[paper](https://arxiv.org/abs/2406.11410)]

- **DCLM**: "DataComp-LM: In search of the next generation of training sets for language models" [2024-06] [[paper](https://arxiv.org/abs/2406.11794)]

- **Nemotron-4**: "Nemotron-4 340B Technical Report" [2024-06] [[paper](https://arxiv.org/abs/2406.11704)]

- **ChatGLM**: "ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools" [2024-06] [[paper](https://arxiv.org/abs/2406.12793)]

- **YuLan**: "YuLan: An Open-source Large Language Model" [2024-06] [[paper](https://arxiv.org/abs/2406.19853)]

- **Gemma 2**: "Gemma 2: Improving Open Language Models at a Practical Size" [2024-06] [[paper](https://storage.googleapis.com/deepmind-media/gemma/gemma-2-report.pdf)]

- **H2O-Danube3**: "H2O-Danube3 Technical Report" [2024-07] [[paper](https://arxiv.org/abs/2407.09276)]

- **Qwen2**: "Qwen2 Technical Report" [2024-07] [[paper](https://arxiv.org/abs/2407.10671)]

- **ALLaM**: "ALLaM: Large Language Models for Arabic and English" [2024-07] [[paper](https://arxiv.org/abs/2407.15390)]

- **SeaLLMs 3**: "SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages" [2024-07] [[paper](https://arxiv.org/abs/2407.19672)]

- **AFM**: "Apple Intelligence Foundation Language Models" [2024-07] [[paper](https://arxiv.org/abs/2407.21075)]

- "To Code, or Not To Code? Exploring Impact of Code in Pre-training" [2024-08] [[paper](https://arxiv.org/abs/2408.10914)]

- **OLMoE**: "OLMoE: Open Mixture-of-Experts Language Models" [2024-09] [[paper](https://arxiv.org/abs/2409.02060)]

- "How Does Code Pretraining Affect Language Model Task Performance?" [2024-09] [[paper](https://arxiv.org/abs/2409.04556)]

- **EuroLLM**: "EuroLLM: Multilingual Language Models for Europe" [2024-09] [[paper](https://arxiv.org/abs/2409.16235)]

- "Which Programming Language and What Features at Pre-training Stage Affect Downstream Logical Inference Performance?" [2024-10] [[paper](https://arxiv.org/abs/2410.06735)]

- **GPT-4o**: "GPT-4o System Card" [2024-10] [[paper](https://arxiv.org/abs/2410.21276)]

- **Hunyuan-Large**: "Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent" [2024-11] [[paper](https://arxiv.org/abs/2411.02265)]

- **Crystal**: "Crystal: Illuminating LLM Abilities on Language and Code" [2024-11] [[paper](https://arxiv.org/abs/2411.04156)]

### Existing LLM Adapted to Code 
> further pretrained on code-related data

- **Codex** (GPT-3): "Evaluating Large Language Models Trained on Code" [2021-07] [[paper](https://arxiv.org/abs/2107.03374)]

- **PaLM Coder** (PaLM): "PaLM: Scaling Language Modeling with Pathways" [2022-04] [JMLR] [[paper](https://arxiv.org/abs/2204.02311)]

- **Minerva** (PaLM): "Solving Quantitative Reasoning Problems with Language Models" [2022-06] [[paper](https://arxiv.org/abs/2206.14858)]

- **PaLM 2 \*** (PaLM 2): "PaLM 2 Technical Report" [2023-05] [[paper](https://arxiv.org/abs/2305.10403)]

- **Code LLaMA** (LLaMA 2): "Code Llama: Open Foundation Models for Code" [2023-08] [[paper](https://arxiv.org/abs/2308.12950)] [[repo](https://github.com/facebookresearch/codellama)]

- **Lemur** (LLaMA 2): "Lemur: Harmonizing Natural Language and Code for Language Agents" [2023-10] [ICLR 2024 Spotlight] [[paper](https://arxiv.org/abs/2310.06830)]

- **BTX** (LLaMA 2): "Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM" [2024-03] [[paper](https://arxiv.org/abs/2403.07816)]

- **HiRoPE**: "HiRoPE: Length Extrapolation for Code Models Using Hierarchical Position" [2024-03] [ACL 2024] [[paper](https://arxiv.org/abs/2403.19115)]

- "Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models" [2024-03] [[paper](https://arxiv.org/abs/2403.08281)]

- **CodeGemma**: "CodeGemma: Open Code Models Based on Gemma" [2024-04] [[paper](https://storage.googleapis.com/deepmind-media/gemma/codegemma_report.pdf)] [[model](https://huggingface.co/models?search=google/codegemma)]

- **DeepSeek-Coder-V2**: "DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence" [2024-06] [[paper](https://arxiv.org/abs/2406.11931)]

- "Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization" [2024-09] [[paper](https://arxiv.org/abs/2409.12020)]

- **Qwen2.5-Coder**: "Qwen2.5-Coder Technical Report" [2024-09] [[paper](https://arxiv.org/abs/2409.12186)]

- **Lingma SWE-GPT**: "Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement" [2024-11] [[paper](https://arxiv.org/abs/2411.00622)]

### General Pretraining on Code

#### Encoder

- **CuBERT** (MLM + NSP): "Learning and Evaluating Contextual Embedding of Source Code" [2019-12] [ICML 2020] [[paper](https://arxiv.org/abs/2001.00059)] [[repo](https://github.com/google-research/google-research/tree/master/cubert)]

- **CodeBERT** (MLM + RTD): "CodeBERT: A Pre-Trained Model for Programming and Natural Languages" [2020-02] [EMNLP 2020 findings] [[paper](https://arxiv.org/abs/2002.08155)] [[repo](https://github.com/microsoft/CodeBERT)]

- **GraphCodeBERT** (MLM + DFG Edge Prediction + DFG Node Alignment): "GraphCodeBERT: Pre-training Code Representations with Data Flow" [2020-09] [ICLR 2021] [[paper](https://arxiv.org/abs/2009.08366)] [[repo](https://github.com/microsoft/CodeBERT)]

- **SynCoBERT** (MLM + Identifier Prediction + AST Edge Prediction + Contrastive Learning): "SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation" [2021-08] [[paper](https://arxiv.org/abs/2108.04556)]

- **DISCO** (MLM + Node Type MLM + Contrastive Learning): "Towards Learning (Dis)-Similarity of Source Code from Program Contrasts" [2021-10] [ACL 2022] [[paper](https://arxiv.org/abs/2110.03868)]

- **Code-MVP** (MLM + Type Inference + Contrastive Learning): "CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training" [2022-05] [NAACL 2022 Technical Track] [[paper](https://arxiv.org/abs/2205.02029)]

- **CodeSage** (MLM + Deobfuscation + Contrastive Learning): "Code Representation Learning At Scale" [2024-02] [ICLR 2024] [[paper](https://arxiv.org/abs/2402.01935)]

- **CoLSBERT** (MLM): "Scaling Laws Behind Code Understanding Model" [2024-02] [[paper](https://arxiv.org/abs/2402.12813)]

#### Decoder

- **GPT-C** (CLM): "IntelliCode Compose: Code Generation Using Transformer" [2020-05] [ESEC/FSE 2020] [[paper](https://arxiv.org/abs/2005.08025)]

- **CodeGPT** (CLM): "CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" [2021-02] [NeurIPS Datasets and Benchmarks 2021] [[paper](https://arxiv.org/abs/2102.04664)] [[repo](https://github.com/microsoft/CodeXGLUE)]

- **CodeParrot** (CLM) [2021-12] [[blog](https://huggingface.co/blog/codeparrot)]

- **PolyCoder** (CLM): "A Systematic Evaluation of Large Language Models of Code" [2022-02] [DL4C@ICLR 2022] [[paper](https://arxiv.org/abs/2202.13169)] [[repo](https://github.com/VHellendoorn/Code-LMs)]

- **CodeGen** (CLM): "CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis" [2022-03] [ICLR 2023] [[paper](https://arxiv.org/abs/2203.13474)] [[repo](https://github.com/salesforce/CodeGen)]

- **InCoder** (Causal Masking): "InCoder: A Generative Model for Code Infilling and Synthesis" [2022-04] [ICLR 2023] [[paper](https://arxiv.org/abs/2204.05999)] [[repo](https://github.com/dpfried/incoder)]

- **PyCodeGPT** (CLM): "CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generation" [2022-06] [IJCAI-ECAI 2022] [[paper](https://arxiv.org/abs/2206.06888)] [[repo](https://github.com/microsoft/PyCodeGPT)]

- **PanGu-Coder** (CLM): "PanGu-Coder: Program Synthesis with Function-Level Language Modeling" [2022-07] [[paper](https://arxiv.org/abs/2207.11280)]

- **SantaCoder** (FIM): "SantaCoder: don't reach for the stars!" [2023-01] [[paper](https://arxiv.org/abs/2301.03988)] [[model](https://huggingface.co/bigcode/santacoder)]

- **CodeGeeX** (CLM): "CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X" [2023-03] [[paper](https://arxiv.org/abs/2303.17568)] [[repo](https://github.com/THUDM/CodeGeeX)]

- **StarCoder** (FIM): "StarCoder: may the source be with you!" [2023-05] [[paper](https://arxiv.org/abs/2305.06161)] [[model](https://huggingface.co/bigcode/starcoder)]

- **Phi-1** (CLM): "Textbooks Are All You Need" [2023-06] [[paper](https://arxiv.org/abs/2306.11644)] [[model](https://huggingface.co/microsoft/phi-1)]

- **CodeFuse** (CLM): "CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model" [2023-10] [[paper](https://arxiv.org/abs/2310.06266)] [[model](https://huggingface.co/codefuse-ai/CodeFuse-13B)]

- **DeepSeek Coder** (CLM+FIM): "DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence" [2024-01] [[paper](https://arxiv.org/abs/2401.14196)] [[repo](https://github.com/deepseek-ai/DeepSeek-Coder)]

- **StarCoder2** (CLM+FIM): "StarCoder 2 and The Stack v2: The Next Generation" [2024-02] [[paper](https://arxiv.org/abs/2402.19173)] [[repo](https://github.com/bigcode-project/starcoder2)]

- **CodeShell** (CLM+FIM): "CodeShell Technical Report" [2024-03] [[paper](https://arxiv.org/abs/2403.15747)] [[repo](https://github.com/WisdomShell/codeshell)]

- **CodeQwen1.5** [2024-04] [[blog](https://qwenlm.github.io/blog/codeqwen1.5/)]

- **Granite**: "Granite Code Models: A Family of Open Foundation Models for Code Intelligence" [2024-05] [[paper](https://arxiv.org/abs/2405.04324)] "Scaling Granite Code Models to 128K Context" [2024-07] [[paper](https://arxiv.org/abs/2407.13739)]

- **NT-Java**: "Narrow Transformer: Starcoder-Based Java-LM For Desktop" [2024-07] [[paper](https://arxiv.org/abs/2407.03941)]

- **Arctic-SnowCoder**: "Arctic-SnowCoder: Demystifying High-Quality Data in Code Pretraining" [2024-09] [[paper](https://arxiv.org/abs/2409.02326)]

- **aiXcoder**: "aiXcoder-7B: A Lightweight and Effective Large Language Model for Code Completion" [2024-10] [[paper](https://arxiv.org/abs/2410.13187)]

- **OpenCoder**: "OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models" [2024-11] [[paper](https://arxiv.org/abs/2411.04905)]

#### Encoder-Decoder

- **PyMT5** (Span Corruption): "PyMT5: multi-mode translation of natural language and Python code with transformers" [2020-10] [EMNLP 2020] [[paper](https://arxiv.org/abs/2010.03150)]

- **Mastropaolo et al.** (MLM + Deobfuscation): "DOBF: A Deobfuscation Pre-Training Objective for Programming Languages" [2021-02] [ICSE 2021] [[paper](https://arxiv.org/abs/2102.02017)] [[repo](https://github.com/antonio-mastropaolo/TransferLearning4Code)]

- **DOBF** (Span Corruption): "Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks" [2021-02] [NeurIPS 2021] [[paper](https://arxiv.org/abs/2102.07492)] [[repo](https://github.com/facebookresearch/CodeGen/blob/main/docs/dobf.md)]

- **PLBART** (DAE): "Unified Pre-training for Program Understanding and Generation" [2021-03] [NAACL 2021] [[paper](https://arxiv.org/abs/2103.06333)] [[repo](https://github.com/wasiahmad/PLBART)]

- **CodeT5** (Span Corruption + Identifier Tagging + Masked Identifier Prediction + Text2Code + Code2Text): "CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation" [2021-09] [EMNLP 2021] [[paper](https://arxiv.org/abs/2109.00859)] [[repo](https://github.com/salesforce/CodeT5)]

- **SPT-Code** (Span Corruption + NSP + Method Name Prediction): "SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations" [2022-01] [ICSE 2022 Technical Track] [[paper](https://arxiv.org/abs/2201.01549)]

- **AlphaCode** (MLM + CLM): "Competition-Level Code Generation with AlphaCode" [2022-02] [Science] [[paper](https://arxiv.org/abs/2203.07814)] [[blog](https://deepmind.google/discover/blog/competitive-programming-with-alphacode/)]

- **NatGen** (Code Naturalization): "NatGen: Generative pre-training by "Naturalizing" source code" [2022-06] [ESEC/FSE 2022] [[paper](https://arxiv.org/abs/2206.07585)] [[repo](https://github.com/saikat107/NatGen)]

- **ERNIE-Code** (Span Corruption + Pivot-based Translation LM): "ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages" [2022-12] [ACL23 (Findings)] [[paper](https://aclanthology.org/2023.findings-acl.676.pdf)][[repo](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-code)]

- **CodeT5+** (Span Corruption + CLM + Text-Code Contrastive Learning + Text-Code Translation): "CodeT5+: Open Code Large Language Models for Code Understanding and Generation" [2023-05] [EMNLP 2023] [[paper](https://arxiv.org/abs/2305.07922)] [[repo](https://github.com/salesforce/CodeT5)]

- **AST-T5** (Span Corruption): "AST-T5: Structure-Aware Pretraining for Code Generation and Understanding" [2024-01] [ICML 2024] [[paper](https://arxiv.org/abs/2401.03003)]

#### UniLM

- **CugLM** (MLM + NSP + CLM): "Multi-task Learning based Pre-trained Language Model for Code Completion" [2020-12] [ASE 2020] [[paper](https://arxiv.org/abs/2012.14631)]

- **UniXcoder** (MLM + NSP + CLM + Span Corruption + Contrastive Learning + Code2Text): "UniXcoder: Unified Cross-Modal Pre-training for Code Representation" [2022-03] [ACL 2022] [[paper](https://arxiv.org/abs/2203.03850)] [[repo](https://github.com/microsoft/CodeBERT)]

### Instruction on Code

- **WizardCoder** (StarCoder + Evol-Instruct): "WizardCoder: Empowering Code Large Language Models with Evol-Instruct" [2023-06] [ICLR 2024] [[paper](https://arxiv.org/abs/2306.08568)] [[repo](https://github.com/nlpxucan/WizardLM)]

- **PanGu-Coder 2** (StarCoder + Evol-Instruct + RRTF): "PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback" [2023-07] [[paper](https://arxiv.org/abs/2307.14936)]

- **OctoCoder** (StarCoder) / **OctoGeeX** (CodeGeeX2): "OctoPack: Instruction Tuning Code Large Language Models" [2023-08] [ICLR 2024 Spotlight] [[paper](https://arxiv.org/abs/2308.07124)] [[repo](https://github.com/bigcode-project/octopack)]

- "At Which Training Stage Does Code Data Help LLMs Reasoning" [2023-09] [ICLR 2024 Spotlight] [[paper](https://arxiv.org/abs/2309.16298)]

- **InstructCoder**: "InstructCoder: Instruction Tuning Large Language Models for Code Editing" [[paper](https://arxiv.org/abs/2310.20329)] [[repo](https://github.com/qishenghu/CodeInstruct)]

- **MFTCoder**: "MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning" [2023-11] [KDD 2024] [[paper](https://arxiv.org/abs/2311.02303)] [[repo](https://github.com/codefuse-ai/MFTCoder)]

- "LLM-Assisted Code Cleaning For Training Accurate Code Generators" [2023-11] [ICLR 2024] [[paper](https://arxiv.org/abs/2311.14904)]

- **Magicoder**: "Magicoder: Empowering Code Generation with OSS-Instruct" [2023-12] [ICML 2024] [[paper](https://arxiv.org/abs/2312.02120)]

- **WaveCoder**: "WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning" [2023-12] [ACL 2024] [[paper](https://arxiv.org/abs/2312.14187)]

- **Astraios**: "Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models" [2024-01] [[paper](https://arxiv.org/abs/2401.00788)]

- **DolphCoder**: "DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning" [2024-02] [ACL 2024] [[paper](https://arxiv.org/abs/2402.09136)]

- **SafeCoder**: "Instruction Tuning for Secure Code Generation" [2024-02] [ICML 2024] [[paper](https://arxiv.org/abs/2402.09497)]

- "Code Needs Comments: Enhancing Code LLMs with Comment Augmentation" [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2402.13013)]

- **CCT**: "Code Comparison Tuning for Code Large Language Models" [2024-03] [[paper](https://arxiv.org/abs/2403.19121)]

- **SAT**: "Structure-aware Fine-tuning for Code Pre-trained Models" [2024-04] [[paper](https://arxiv.org/abs/2404.07471)]

- **CodeFort**: "CodeFort: Robust Training for Code Generation Models" [2024-04] [[paper](https://arxiv.org/abs/2405.01567)]

- **XFT**: "XFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts" [2024-04] [ACL 2024] [[paper](https://arxiv.org/abs/2404.15247)] [[repo](https://github.com/ise-uiuc/xft)]

- **AIEV-Instruct**: "AutoCoder: Enhancing Code Large Language Model with AIEV-Instruct" [2024-05] [[paper](https://arxiv.org/abs/2405.14906)]

- **AlchemistCoder**: "AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data" [2024-05] [[paper](https://arxiv.org/abs/2405.19265)]

- "From Symbolic Tasks to Code Generation: Diversification Yields Better Task Performers" [2024-05] [[paper](https://arxiv.org/abs/2405.19787)]

- "Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning" [2024-05] [[paper](https://arxiv.org/abs/2405.20535)]

- **PLUM**: "PLUM: Preference Learning Plus Test Cases Yields Better Code Language Models" [2024-06] [[paper](https://arxiv.org/abs/2406.06887)]

- **mCoder**: "McEval: Massively Multilingual Code Evaluation" [2024-06] [[paper](https://arxiv.org/abs/2406.07436)]

- "Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models" [2024-06] [[paper](https://arxiv.org/abs/2406.10305)]

- **Code-Optimise**: "Code-Optimise: Self-Generated Preference Data for Correctness and Efficiency" [2024-06] [[paper](https://arxiv.org/abs/2406.12502)]

- **UniCoder**: "UniCoder: Scaling Code Large Language Model via Universal Code" [2024-06] [ACL 2024] [[paper](https://arxiv.org/abs/2406.16441)]

- "Brevity is the soul of wit: Pruning long files for code generation" [2024-06] [[paper](https://arxiv.org/abs/2407.00434)]

- "Code Less, Align More: Efficient LLM Fine-tuning for Code Generation with Data Pruning" [2024-07] [[paper](https://arxiv.org/abs/2407.05040)]

- **InverseCoder**: "InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct" [2024-07] [[paper](https://arxiv.org/abs/2407.05700)]

- "Curriculum Learning for Small Code Language Models" [2024-07] [[paper](https://arxiv.org/abs/2407.10194)]

- **Genetic-Instruct**: "Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions for Large Language Models" [2024-07] [[paper](https://arxiv.org/abs/2407.21077)]

- **DataScope**: "API-guided Dataset Synthesis to Finetune Large Code Models" [2024-08] [[paper](https://arxiv.org/abs/2408.08343)]

- ** XCoder**: "How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data" [2024-09] [[paper](https://arxiv.org/abs/2409.03810)]

- **GALLa**: "GALLa: Graph Aligned Large Language Models for Improved Source Code Understanding" [2024-09] [[paper](https://arxiv.org/abs/2409.04183)]

- **HexaCoder**: "HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data" [2024-09] [[paper](https://arxiv.org/abs/2409.06446)]

- **AMR-Evol**: "AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation" [2024-10] [[paper](https://arxiv.org/abs/2410.00558)]

- **LintSeq**: "Training Language Models on Synthetic Edit Sequences Improves Code Synthesis" [2024-10] [[paper](https://arxiv.org/abs/2410.02749)]

- **CoBa**: "CoBa: Convergence Balancer for Multitask Finetuning of Large Language Models" [2024-10] [EMNLP 2024] [[paper](https://arxiv.org/abs/2410.06741)]

- **CursorCore**: "CursorCore: Assist Programming through Aligning Anything" [2024-10] [[paper](https://arxiv.org/abs/2410.07002)]

- **SelfCodeAlign**: "SelfCodeAlign: Self-Alignment for Code Generation" [2024-10] [[paper](https://arxiv.org/abs/2410.24198)]

- "Mastering the Craft of Data Synthesis for CodeLLMs" [2024-10] [[paper](https://arxiv.org/abs/2411.00005)]

- **CodeLutra**: "CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement" [2024-11] [[paper](https://arxiv.org/abs/2411.05199)]

## Code And Reasoning

### Coding for Reasoning

- **PAL**: "PAL: Program-aided Language Models" [2022-11] [ICML 2023] [[paper](https://arxiv.org/abs/2211.10435)] [[repo](https://github.com/reasoning-machines/pal)]

- **PoT**: "Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks" [2022-11] [TMLR 2023] [[paper](https://arxiv.org/abs/2211.12588)] [[repo](https://github.com/wenhuchen/Program-of-Thoughts)]

- **PaD**: "PaD: Program-aided Distillation Can Teach Small Models Reasoning Better than Chain-of-thought Fine-tuning" [2023-05] [NAACL 2024] [[paper](https://arxiv.org/abs/2305.13888)]

- **CSV**: "Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification" [2023-08] [ICLR 2024] [[paper](https://arxiv.org/abs/2308.07921)]

- **MathCoder**: "MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning" [2023-10] [ICLR 2024] [[paper](https://arxiv.org/abs/2310.03731)]

- **CoC**: "Chain of Code: Reasoning with a Language Model-Augmented Code Emulator" [2023-12] [ICML 2024] [[paper](https://arxiv.org/abs/2312.04474)]

- **MARIO**: "MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline" [2024-01] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2401.08190)]

- **ReGAL**: "ReGAL: Refactoring Programs to Discover Generalizable Abstractions" [2024-01] [ICML 2024] [[paper](https://arxiv.org/abs/2401.16467)]

- "Executable Code Actions Elicit Better LLM Agents" [2024-02] [ICML 2024] [[paper](https://arxiv.org/abs/2402.01030)]

- **HProPro**: "Exploring Hybrid Question Answering via Program-based Prompting" [2024-02] [ACL 2024] [[paper](https://arxiv.org/abs/2402.10812)]

- **xSTREET**: "Eliciting Better Multilingual Structured Reasoning from LLMs through Code" [2024-03] [ACL 2024] [[paper](https://arxiv.org/abs/2403.02567)]

- **FlowMind**: "FlowMind: Automatic Workflow Generation with LLMs" [2024-03] [[paper](https://arxiv.org/abs/2404.13050)]

- **Think-and-Execute**: "Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models" [2024-04] [[paper](https://arxiv.org/abs/2404.02575)]

- **CoRE**: "CoRE: LLM as Interpreter for Natural Language Programming, Pseudo-Code Programming, and Flow Programming of AI Agents" [2024-05] [[paper](https://arxiv.org/abs/2405.06907)]

- **MuMath-Code**: "MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning" [2024-05] [[paper](https://arxiv.org/abs/2405.07551)]

- **COGEX**: "Learning to Reason via Program Generation, Emulation, and Search" [2024-05] [[paper](https://arxiv.org/abs/2405.16337)]

- "Arithmetic Reasoning with LLM: Prolog Generation & Permutation" [2024-05] [[paper](https://arxiv.org/abs/2405.17893)]

- "Can LLMs Reason in the Wild with Programs?" [2024-06] [[paper](https://arxiv.org/abs/2406.13764)]

- **DotaMath**: "DotaMath: Decomposition of Thought with Code Assistance and Self-correction for Mathematical Reasoning" [2024-07] [[paper](https://arxiv.org/abs/2407.04078)]

- **CIBench**: "CIBench: Evaluating Your LLMs with a Code Interpreter Plugin" [2024-07] [[paper](https://arxiv.org/abs/2407.10499)]

- **PyBench**: "PyBench: Evaluating LLM Agent on various real-world coding tasks" [2024-07] [[paper](https://arxiv.org/abs/2407.16732)]

- **AdaCoder**: "AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answering" [2024-07] [[paper](https://arxiv.org/abs/2407.19410)]

- **PyramidCoder**: "Pyramid Coder: Hierarchical Code Generator for Compositional Visual Question Answering" [2024-07] [[paper](https://arxiv.org/abs/2407.20563)]

- **CodeGraph**: "CodeGraph: Enhancing Graph Reasoning of LLMs with Code" [2024-08] [[paper](https://arxiv.org/abs/2408.13863)]

- **SIaM**: "SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models" [2024-08] [[paper](https://arxiv.org/abs/2408.15565)]

- **CodePlan**: "CodePlan: Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning" [2024-09] [[paper](https://arxiv.org/abs/2409.12452)]

- **PoT**: "Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning" [2024-09] [[paper](https://arxiv.org/abs/2409.17270)]

- **MetaMath**: "MetaMath: Integrating Natural Language and Code for Enhanced Mathematical Reasoning in Large Language Models" [2024-09] [[paper](https://arxiv.org/abs/2409.19381)]

- "BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data" [2024-10] [[paper](https://arxiv.org/abs/2410.00773)]

- **CodeSteer**: "Steering Large Language Models between Code Execution and Textual Reasoning" [2024-10] [[paper](https://arxiv.org/abs/2410.03524)]

- **MathCoder2**: "MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code" [2024-10] [[paper](https://arxiv.org/abs/2410.08196)]

- **LLMFP**: "Planning Anything with Rigor: General-Purpose Zero-Shot Planning with LLM-based Formalized Programming" [2024-10] [[paper](https://arxiv.org/abs/2410.12112)]

- **Prove**: "Not All Votes Count! Programs as Verifiers Improve Self-Consistency of Language Models for Math Reasoning" [2024-10] [[paper](https://arxiv.org/abs/2410.12608)]

- **PROVE**: "Trust but Verify: Programmatic VLM Evaluation in the Wild" [2024-10] [[paper](https://arxiv.org/abs/2410.13121)]

- **GeoCoder**: "GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models" [2024-10] [[paper](https://arxiv.org/abs/2410.13510)]

- **ReasonAgain**: "ReasonAgain: Using Extractable Symbolic Programs to Evaluate Mathematical Reasoning" [2024-10] [[paper](https://arxiv.org/abs/2410.19056)]

- **GFP**: "Gap-Filling Prompting Enhances Code-Assisted Mathematical Reasoning" [2024-11] [[paper](https://arxiv.org/abs/2411.05407)]

- **UTMath**: "UTMath: Math Evaluation with Unit Test via Reasoning-to-Coding Thoughts" [2024-11] [[paper](https://arxiv.org/abs/2411.07240)]

### Code Simulation

- "Code Simulation Challenges for Large Language Models" [2024-01] [[paper](https://arxiv.org/abs/2401.09074)]

- "CodeMind: A Framework to Challenge Large Language Models for Code Reasoning" [2024-02] [[paper](https://arxiv.org/abs/2402.09664)]

- "Executing Natural Language-Described Algorithms with Large Language Models: An Investigation" [2024-02] [[paper](https://arxiv.org/abs/2403.00795)]

- "Can Language Models Pretend Solvers? Logic Code Simulation with LLMs" [2024-03] [[paper](https://arxiv.org/abs/2403.16097)]

- "Evaluating Large Language Models with Runtime Behavior of Program Execution" [2024-03] [[paper](https://arxiv.org/abs/2403.16437)]

- "NExT: Teaching Large Language Models to Reason about Code Execution" [2024-04] [ICML 2024] [[paper](https://arxiv.org/abs/2404.14662)]

- "SelfPiCo: Self-Guided Partial Code Execution with LLMs" [2024-07] [[paper](https://arxiv.org/abs/2407.16974)]

- "Large Language Models as Code Executors: An Exploratory Study" [2024-10] [[paper](https://arxiv.org/abs/2410.06667)]

- "VISUALCODER: Guiding Large Language Models in Code Execution with Fine-grained Multimodal Chain-of-Thought Reasoning" [2024-10] [[paper](https://arxiv.org/abs/2410.23402)]

### Code Agents

- **Self-collaboration**: "Self-collaboration Code Generation via ChatGPT" [2023-04] [[paper](https://arxiv.org/abs/2304.07590)]

- **ChatDev**: "Communicative Agents for Software Development" [2023-07] [[paper](https://arxiv.org/abs/2307.07924)] [[repo](https://github.com/OpenBMB/ChatDev)]

- **MetaGPT**: "MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework" [2023-08] [[paper](https://arxiv.org/abs/2308.00352)] [[repo](https://github.com/geekan/MetaGPT)]

- **CodeChain**: "CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules" [2023-10] [ICLR 2024] [[paper](https://arxiv.org/abs/2310.08992)]

- **CodeAgent**: "CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges" [2024-01] [ACL 2024] [[paper](https://arxiv.org/abs/2401.07339)]

- **CONLINE**: "CONLINE: Complex Code Generation and Refinement with Online Searching and Correctness Testing" [2024-03] [[paper](https://arxiv.org/abs/2403.13583)]

- **LCG**: "When LLM-based Code Generation Meets the Software Development Process" [2024-03] [[paper](https://arxiv.org/abs/2403.15852)]

- **RepairAgent**: "RepairAgent: An Autonomous, LLM-Based Agent for Program Repair" [2024-03] [[paper](https://arxiv.org/abs/2403.17134)]

- **MAGIS:**: "MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution" [2024-03] [[paper](https://arxiv.org/abs/2403.17927)]

- **SoA**: "Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization" [2024-04] [[paper](https://arxiv.org/abs/2404.02183)]

- **AutoCodeRover**: "AutoCodeRover: Autonomous Program Improvement" [2024-04] [[paper](https://arxiv.org/abs/2404.05427)]

- **SWE-agent**: "SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering" [2024-05] [[paper](https://arxiv.org/abs/2405.15793)]

- **MapCoder**: "MapCoder: Multi-Agent Code Generation for Competitive Problem Solving" [2024-05] [ACL 2024] [[paper](https://arxiv.org/abs/2405.11403)]

- "Fight Fire with Fire: How Much Can We Trust ChatGPT on Source Code-Related Tasks?" [2024-05] [[paper](https://arxiv.org/abs/2405.12641)]

- **FunCoder**: "Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation" [2024-05] [[paper](https://arxiv.org/abs/2405.20092)]

- **CTC**: "Multi-Agent Software Development through Cross-Team Collaboration" [2024-06] [[paper](https://arxiv.org/abs/2406.08979)]

- **MASAI**: "MASAI: Modular Architecture for Software-engineering AI Agents" [2024-06] [[paper](https://arxiv.org/abs/2406.11638)]

- **AgileCoder**: "AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology" [2024-06] [[paper](https://arxiv.org/abs/2406.11912)]

- **CodeNav**: "CodeNav: Beyond tool-use to using real-world codebases with LLM agents" [2024-06] [[paper](https://arxiv.org/abs/2406.12276)]

- **INDICT**: "INDICT: Code Generation with Internal Dialogues of Critiques for Both Security and Helpfulness" [2024-06] [[paper](https://arxiv.org/abs/2407.02518)]

- **AppWorld**: "AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents" [2024-07] [[paper](https://arxiv.org/abs/2407.18901)]

- **CortexCompile**: "CortexCompile: Harnessing Cortical-Inspired Architectures for Enhanced Multi-Agent NLP Code Synthesis" [2024-08] [[paper](https://arxiv.org/abs/2409.02938)]

- **Survey**: "Large Language Model-Based Agents for Software Engineering: A Survey" [2024-09] [[paper](https://arxiv.org/abs/2409.02977)]

- **AutoSafeCoder**: "AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing" [2024-09] [[paper](https://arxiv.org/abs/2409.10737)]

- **SuperCoder2.0**: "SuperCoder2.0: Technical Report on Exploring the feasibility of LLMs as Autonomous Programmer" [2024-09] [[paper](https://arxiv.org/abs/2409.11190)]

- **Survey**: "Agents in Software Engineering: Survey, Landscape, and Vision" [2024-09] [[paper](https://arxiv.org/abs/2409.09030)]

- **MOSS**: "MOSS: Enabling Code-Driven Evolution and Context Management for AI Agents" [2024-09] [[paper](https://arxiv.org/abs/2409.16120)]

- **HyperAgent**: "HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale" [2024-09] [[paper](https://arxiv.org/abs/2409.16299)]

- "Compositional Hardness of Code in Large Language Models -- A Probabilistic Perspective" [2024-09] [[paper](https://arxiv.org/abs/2409.18028)]

- **RGD**: "RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance" [2024-10] [[paper](https://arxiv.org/abs/2410.01242)]

- **AutoML-Agent**: "AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML" [2024-10] [[paper](https://arxiv.org/abs/2410.02958)]

- **Seeker**: "Seeker: Enhancing Exception Handling in Code with LLM-based Multi-Agent Approach" [2024-10] [[paper](https://arxiv.org/abs/2410.06949)]

- **REDO**: "REDO: Execution-Free Runtime Error Detection for COding Agents" [2024-10] [[paper](https://arxiv.org/abs/2410.09117)]

- "Evaluating Software Development Agents: Patch Patterns, Code Quality, and Issue Complexity in Real-World GitHub Scenarios" [2024-10] [[paper](https://arxiv.org/abs/2410.12468)]

- **EvoMAC**: "Self-Evolving Multi-Agent Collaboration Networks for Software Development" [2024-10] [[paper](https://arxiv.org/abs/2410.16946)]

- **VisionCoder**: "VisionCoder: Empowering Multi-Agent Auto-Programming for Image Processing with Hybrid LLMs" [2024-10] [[paper](https://arxiv.org/abs/2410.19245)]

- **AutoKaggle**: "AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions" [2024-10] [[paper](https://arxiv.org/abs/2410.20424)]

- **Watson**: "Watson: A Cognitive Observability Framework for the Reasoning of Foundation Model-Powered Agents" [2024-11] [[paper](https://arxiv.org/abs/2411.03455)]

- **CodeTree**: "CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models" [2024-11] [[paper](https://arxiv.org/abs/2411.04329)]

### Interactive Coding

- "Interactive Program Synthesis" [2017-03] [[paper](https://arxiv.org/abs/1703.03539)]

- "Question selection for interactive program synthesis" [2020-06] [PLDI 2020] [[paper](https://dl.acm.org/doi/10.1145/3385412.3386025)]

- "Interactive Code Generation via Test-Driven User-Intent Formalization" [2022-08] [[paper](https://arxiv.org/abs/2208.05950)]

- "Improving Code Generation by Training with Natural Language Feedback" [2023-03] [TMLR] [[paper](https://arxiv.org/abs/2303.16749)]

- "Self-Refine: Iterative Refinement with Self-Feedback" [2023-03] [NeurIPS 2023] [[paper](https://arxiv.org/abs/2303.17651)]

- "Teaching Large Language Models to Self-Debug" [2023-04] [[paper](https://arxiv.org/abs/2304.05128)]

- "Self-Edit: Fault-Aware Code Editor for Code Generation" [2023-05] [ACL 2023] [[paper](https://arxiv.org/abs/2305.04087)]

- "LeTI: Learning to Generate from Textual Interactions" [2023-05] [[paper](https://arxiv.org/abs/2305.10314)]

- "Is Self-Repair a Silver Bullet for Code Generation?" [2023-06] [ICLR 2024] [[paper](https://arxiv.org/abs/2306.09896)]

- "InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback" [2023-06] [NeurIPS 2023] [[paper](https://arxiv.org/abs/2306.14898)]

- "INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair" [2023-11] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2311.09868)]

- "OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement" [2024-02] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2402.14658)]

- "Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback" [2024-03] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2403.16792)]

- "CYCLE: Learning to Self-Refine the Code Generation" [2024-03] [[paper](https://arxiv.org/abs/2403.18746)]

- "LLM-based Test-driven Interactive Code Generation: User Study and Empirical Evaluation" [2024-04] [[paper](https://arxiv.org/abs/2404.10100)]

- "SOAP: Enhancing Efficiency of Generated Code via Self-Optimization" [2024-05] [[paper](https://arxiv.org/abs/2405.15189)]

- "Code Repair with LLMs gives an Exploration-Exploitation Tradeoff" [2024-05] [[paper](https://arxiv.org/abs/2405.17503)]

- "ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation" [2024-05] [[paper](https://arxiv.org/abs/2405.17057)]

- "Training LLMs to Better Self-Debug and Explain Code" [2024-05] [[paper](https://arxiv.org/abs/2405.18649)]

- "Requirements are All You Need: From Requirements to Code with LLMs" [2024-06] [[paper](https://arxiv.org/abs/2406.10101)]

- "I Need Help! Evaluating LLM's Ability to Ask for Users' Support: A Case Study on Text-to-SQL Generation" [2024-07] [[paper](https://arxiv.org/abs/2407.14767)]

- "An Empirical Study on Self-correcting Large Language Models for Data Science Code Generation" [2024-08] [[paper](https://arxiv.org/abs/2408.15658)]

- "RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation" [2024-09] [[paper](https://arxiv.org/abs/2409.09584)]

- "From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging" [2024-10] [[paper](https://arxiv.org/abs/2410.01215)] [[repo](https://github.com/YerbaPage/MGDebugger)]

- "What Makes Large Language Models Reason in (Multi-Turn) Code Generation?" [2024-10] [[paper](https://arxiv.org/abs/2410.08105)]

- "The First Prompt Counts the Most! An Evaluation of Large Language Models on Iterative Example-based Code Generation" [2024-11] [[paper](https://arxiv.org/abs/2411.06774)]

### Frontend Navigation

- "MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding" [2021-10] [ACL 2022] [[paper](https://arxiv.org/abs/2110.08518)]

- "WebKE: Knowledge Extraction from Semi-structured Web with Pre-trained Markup Language Model" [2021-10] [CIKM 2021] [[paper](https://dl.acm.org/doi/abs/10.1145/3459637.3482491)]

- "WebGPT: Browser-assisted question-answering with human feedback" [2021-12] [[paper](https://arxiv.org/abs/2112.09332)]

- "CM3: A Causal Masked Multimodal Model of the Internet" [2022-01] [[paper](https://arxiv.org/abs/2201.07520)]

- "DOM-LM: Learning Generalizable Representations for HTML Documents" [2022-01] [[paper](https://arxiv.org/abs/2201.10608)]

- "WebFormer: The Web-page Transformer for Structure Information Extraction" [2022-02] [WWW 2022] [[paper](https://arxiv.org/abs/2202.00217)]

- "A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility" [2022-02] [ECCV 2022] [[paper](https://arxiv.org/abs/2202.02312)]

- "WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents" [2022-07] [NeurIPS 2022] [[paper](https://arxiv.org/abs/2207.01206)]

- "Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding" [2022-10] [ICML 2023] [[paper](https://arxiv.org/abs/2210.03347)]

- "Understanding HTML with Large Language Models" [2022-10] [EMNLP 2023 findings] [[paper](https://arxiv.org/abs/2210.03945)]

- "WebUI: A Dataset for Enhancing Visual UI Understanding with Web Semantics" [2023-01] [CHI 2023] [[paper](https://arxiv.org/abs/2301.13280)]

- "Mind2Web: Towards a Generalist Agent for the Web" [2023-06] [NeurIPS 2023] [[paper](https://arxiv.org/abs/2306.06070)]

- "A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis", [2023-07] [ICLR 2024] [[paper](https://arxiv.org/abs/2307.12856)]

- "WebArena: A Realistic Web Environment for Building Autonomous Agents" [2023-07] [[paper](https://arxiv.org/abs/2307.13854)]

- "CogAgent: A Visual Language Model for GUI Agents" [2023-12] [[paper](https://arxiv.org/abs/2312.08914)]

- "GPT-4V(ision) is a Generalist Web Agent, if Grounded" [2024-01] [[paper](https://arxiv.org/abs/2401.01614)]

- "WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models" [2024-01] [[paper](https://arxiv.org/abs/2401.13919)]

- "WebLINX: Real-World Website Navigation with Multi-Turn Dialogue" [2024-02] [[paper](https://arxiv.org/abs/2402.05930)]

- "OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web" [2024-02] [[paper](https://arxiv.org/abs/2402.17553)]

- "AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent" [2024-04] [[paper](https://arxiv.org/abs/2404.03648)]

- "WILBUR: Adaptive In-Context Learning for Robust and Accurate Web Agents" [2024-04] [[paper](https://arxiv.org/abs/2404.05902)]

- "AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation" [2024-04] [[paper](https://arxiv.org/abs/2404.12753)]

- "GUICourse: From General Vision Language Models to Versatile GUI Agents" [2024-06] [[paper](https://arxiv.org/abs/2406.11317)]

- "NaviQAte: Functionality-Guided Web Application Navigation" [2024-09] [[paper](https://arxiv.org/abs/2409.10741)]

- "MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding" [2024-09] [[paper](https://arxiv.org/abs/2409.14818)]

- "Multimodal Auto Validation For Self-Refinement in Web Agents" [2024-10] [[paper](https://arxiv.org/abs/2410.00689)]

- "Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents" [2024-10] [[paper](https://arxiv.org/abs/2410.05243)]

- "Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation" [2024-10] [[paper](https://arxiv.org/abs/2410.13232)]

- "Harnessing Webpage UIs for Text-Rich Visual Understanding" [2024-10] [[paper](https://arxiv.org/abs/2410.13824)]

- "AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents" [2024-10] [[paper](https://arxiv.org/abs/2410.13825)]

- "Beyond Browsing: API-Based Web Agents" [2024-10] [[paper](https://arxiv.org/abs/2410.16464)]

- "Large Language Models Empowered Personalized Web Agents" [2024-10] [[paper](https://arxiv.org/abs/2410.17236)]

- "AdvWeb: Controllable Black-box Attacks on VLM-powered Web Agents" [2024-10] [[paper](https://arxiv.org/abs/2410.17401)]

- "Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents" [2024-10] [[paper](https://arxiv.org/abs/2410.22552)]

- "OS-ATLAS: A Foundation Action Model for Generalist GUI Agents" [2024-10] [[paper](https://arxiv.org/abs/2410.23218)]

- "From Context to Action: Analysis of the Impact of State Representation and Context on the Generalization of Multi-Turn Web Navigation Agents" [2024-10] [[paper](https://arxiv.org/abs/2410.23555)]

- "AutoGLM: Autonomous Foundation Agents for GUIs" [2024-10] [[paper](https://arxiv.org/abs/2411.00820)]

- "WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning" [2024-11] [[paper](https://arxiv.org/abs/2411.02337)]

### Code LLM for Low-Resource, Low-Level, and Domain-Specific Languages

- [**Ruby**] "On the Transferability of Pre-trained Language Models for Low-Resource Programming Languages" [2022-04] [ICPC 2022] [[paper](https://arxiv.org/abs/2204.09653)]

- [**Verilog**] "Benchmarking Large Language Models for Automated Verilog RTL Code Generation" [2022-12] [DATE 2023] [[paper](https://arxiv.org/abs/2212.11140)]

- [**OCL**] "On Codex Prompt Engineering for OCL Generation: An Empirical Study" [2023-03] [MSR 2023] [[paper](https://arxiv.org/abs/2303.16244)]

- [**Ansible-YAML**] "Automated Code generation for Information Technology Tasks in YAML through Large Language Models" [2023-05] [DAC 2023] [[paper](https://arxiv.org/abs/2305.02783)]

- [**Hansl**] "The potential of LLMs for coding with low-resource and domain-specific programming languages" [2023-07] [[paper](https://arxiv.org/abs/2307.13018)]

- [**Verilog**] "VeriGen: A Large Language Model for Verilog Code Generation" [2023-07] [[paper](https://arxiv.org/abs/2308.00708)]

- [**Verilog**] "RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model" [2023-08] [[paper](https://arxiv.org/abs/2308.05345)]

- [**Racket, OCaml, Lua, R, Julia**] "Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs" [2023-08] [[paper](https://arxiv.org/abs/2308.09895)]

- [**Verilog**] "VerilogEval: Evaluating Large Language Models for Verilog Code Generation" [2023-09] [ICCAD 2023] [[paper](https://arxiv.org/abs/2309.07544)]

- [**Verilog**] "RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models" [2023-11] [[paper](https://arxiv.org/abs/2311.16543)]

- [**Verilog**] "Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis" [2023-12] [[paper](https://arxiv.org/abs/2312.01022)]

- [**Verilog**] "RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution" [2023-12] [[paper](https://arxiv.org/abs/2312.08617)]

- [**Verilog**] "BetterV: Controlled Verilog Generation with Discriminative Guidance" [2024-02] [ICML 2024] [[paper](https://arxiv.org/abs/2402.03375)]

- [**R**] "Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R" [2024-03] [[paper](https://arxiv.org/abs/2405.01553)]

- [**Haskell**] "Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case Study" [2024-03] [[paper](https://arxiv.org/abs/2403.15185)]

- [**Verilog**] "A Multi-Expert Large Language Model Architecture for Verilog Code Generation" [2024-04] [[paper](https://arxiv.org/abs/2404.08029)]

- [**Verilog**] "CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation" [2024-04] [[paper](https://arxiv.org/abs/2404.08806)]

- [**Alloy**] "An Empirical Evaluation of Pre-trained Large Language Models for Repairing Declarative Formal Specifications" [2024-04] [[paper](https://arxiv.org/abs/2404.11050)]

- [**Verilog**] "Evaluating LLMs for Hardware Design and Test" [2024-04] [[paper](https://arxiv.org/abs/2405.02326)]

- [**Kotlin, Swift, and Rust**] "Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT" [2024-04] [[paper](https://arxiv.org/abs/2404.17110)]

- [**Verilog**] "MEIC: Re-thinking RTL Debug Automation using LLMs" [2024-05] [[paper](https://arxiv.org/abs/2405.06840)]

- [**Bash**] "Tackling Execution-Based Evaluation for NL2Bash" [2024-05] [[paper](https://arxiv.org/abs/2405.06807)]

- [**Fortran, Julia, Matlab, R, Rust**] "Evaluating AI-generated code for C++, Fortran, Go, Java, Julia, Matlab, Python, R, and Rust" [2024-05] [[paper](https://arxiv.org/abs/2405.13101)]

- [**OpenAPI**] "Optimizing Large Language Models for OpenAPI Code Completion" [2024-05] [[paper](https://arxiv.org/abs/2405.15729)]

- [**Kotlin**] "Kotlin ML Pack: Technical Report" [2024-05] [[paper](https://arxiv.org/abs/2405.19250)]

- [**Verilog**] "VerilogReader: LLM-Aided Hardware Test Generation" [2024-06] [[paper](https://arxiv.org/abs/2406.04373)]

- "Benchmarking Generative Models on Computational Thinking Tests in Elementary Visual Programming" [2024-06] [[paper](https://arxiv.org/abs/2406.09891)]

- [**Logo**] "Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment" [2024-06] [[paper](https://arxiv.org/abs/2406.11334)]

- [**Ansible YAML, Bash**] "DocCGen: Document-based Controlled Code Generation" [2024-06] [[paper](https://arxiv.org/abs/2406.11925)]

- [**Qiskit**] "Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models" [2024-06] [[paper](https://arxiv.org/abs/2406.14712)]

- [**Perl, Golang, Swift**] "DistiLRR: Transferring Code Repair for Low-Resource Programming Languages" [2024-06] [[paper](https://arxiv.org/abs/2406.14867)]

- [**Verilog**] "AssertionBench: A Benchmark to Evaluate Large-Language Models for Assertion Generation" [2024-06] [[paper](https://arxiv.org/abs/2406.18627)]

- "A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation" [2024-07] [[paper](https://arxiv.org/abs/2407.02742)]

- [**Json, XLM, YAML**] "ConCodeEval: Evaluating Large Language Models for Code Constraints in Domain-Specific Languages" [2024-07] [[paper](https://arxiv.org/abs/2407.03387)]

- [**Verilog**] "AutoBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design" [2024-07] [[paper](https://arxiv.org/abs/2407.03891)]

- [**Verilog**] "CodeV: Empowering LLMs for Verilog Generation through Multi-Level Summarization" [2024-07] [[paper](https://arxiv.org/abs/2407.10424)]

- [**Verilog**] "ITERTL: An Iterative Framework for Fine-tuning LLMs for RTL Code Generation" [2024-07] [[paper](https://arxiv.org/abs/2407.12022)]

- [**Verilog**] "OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection" [2024-07] [[paper](https://arxiv.org/abs/2407.16237)]

- [**Verilog**] "Large Language Model for Verilog Generation with Golden Code Feedback" [2024-07] [[paper](https://arxiv.org/abs/2407.18271)]

- [**Verilog**] "AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs" [2024-07] [[paper](https://arxiv.org/abs/2407.18333)]

- [**RPA**] "Plan with Code: Comparing approaches for robust NL to DSL generation" [2024-08] [[paper](https://arxiv.org/abs/2408.08335)]

- [**Verilog**] "VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool" [2024-08] [[paper](https://arxiv.org/abs/2408.08927)]

- [**Verilog**] "Revisiting VerilogEval: Newer LLMs, In-Context Learning, and Specification-to-RTL Tasks" [2024-08] [[paper](https://arxiv.org/abs/2408.11053)]

- [**MaxMSP, Web Audio**] "Benchmarking LLM Code Generation for Audio Programming with Visual Dataflow Languages" [2024-09] [[paper](https://arxiv.org/abs/2409.00856)]

- [**Verilog**] "RTLRewriter: Methodologies for Large Models aided RTL Code Optimization" [2024-09] [[paper](https://arxiv.org/abs/2409.11414)]

- [**Verilog**] "CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair" [2024-09] [[paper](https://arxiv.org/abs/2409.12993)]

- [**Bash**] "ScriptSmith: A Unified LLM Framework for Enhancing IT Operations via Automated Bash Script Generation, Assessment, and Refinement" [2024-09] [[paper](https://arxiv.org/abs/2409.17166)]

- [**Survey**] "Survey on Code Generation for Low resource and Domain Specific Programming Languages" [2024-10] [[paper](https://arxiv.org/abs/2410.03981)]

- [**R**] "Do Current Language Models Support Code Intelligence for R Programming Language?" [2024-10] [[paper](https://arxiv.org/abs/2410.07793)]

- "Can Large Language Models Generate Geospatial Code?" [2024-10] [[paper](https://arxiv.org/abs/2410.09738)]

- [**PLC**] "Agents4PLC: Automating Closed-loop PLC Code Generation and Verification in Industrial Control Systems using LLM-based Agents" [2024-10] [[paper](https://arxiv.org/abs/2410.14209)]

- [**Lua**] "Evaluating Quantized Large Language Models for Code Generation on Low-Resource Language Benchmarks" [2024-10] [[paper](https://arxiv.org/abs/2410.14766)]

- "Improving Parallel Program Performance Through DSL-Driven Code Generation with LLM Optimizers" [2024-10] [[paper](https://arxiv.org/abs/2410.15625)]

- "GeoCode-GPT: A Large Language Model for Geospatial Code Generation Tasks" [2024-10] [[paper](https://arxiv.org/abs/2410.17031)]

- [**R, D, Racket, Bash**]: "Bridge-Coder: Unlocking LLMs' Potential to Overcome Language Gaps in Low-Resource Code" [2024-10] [[paper](https://arxiv.org/abs/2410.18957)]

- [**SPICE**]: "SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance" [2024-10] [[paper](https://arxiv.org/abs/2410.20553)]

- [**IEC 61131-3 ST**]: "Training LLMs for Generating IEC 61131-3 Structured Text with Online Feedback" [2024-10] [[paper](https://arxiv.org/abs/2410.22159)]

- [**Verilog**] "MetRex: A Benchmark for Verilog Code Metric Reasoning Using LLMs" [2024-11] [[paper](https://arxiv.org/abs/2411.03471)]

- [**Verilog**] "CorrectBench: Automatic Testbench Generation with Functional Self-Correction using LLMs for HDL Design" [2024-11] [[paper](https://arxiv.org/abs/2411.08510)]

## Code LLMs Downstream Task
### Code Generation

- "Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency" [2023-09] [ACL 2024] [[paper](https://arxiv.org/abs/2309.17272)]

- "Self-Infilling Code Generation" [2023-11] [ICML 2024] [[paper](https://arxiv.org/abs/2311.17972)]

- "JumpCoder: Go Beyond Autoregressive Coder via Online Modification" [2024-01] [ACL 2024] [[paper](https://arxiv.org/abs/2401.07870)]

- "Unsupervised Evaluation of Code LLMs with Round-Trip Correctness" [2024-02] [ICML 2024] [[paper](https://arxiv.org/abs/2402.08699)]

- "The Larger the Better? Improved LLM Code-Generation via Budget Reallocation" [2024-03] [[paper](https://arxiv.org/abs/2404.00725)]

- "Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models" [2024-03] [ACL 2024] [[paper](https://arxiv.org/abs/2403.04811)]

- "Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective" [2024-04] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2404.07549)]

- "Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs" [2024-04] [[paper](https://arxiv.org/abs/2404.08148)]

- "Quality Assessment of Prompts Used in Code Generation" [2024-04] [[paper](https://arxiv.org/abs/2404.10155)]

- "Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation" [2024-04] [[paper](https://arxiv.org/abs/2404.14370)]

- "Large Language Models Synergize with Automated Machine Learning" [2024-05] [[paper](https://arxiv.org/abs/2405.03727)]

- "Model Cascading for Code: Reducing Inference Costs with Model Cascading for LLM Based Code Generation" [2024-05] [[paper](https://arxiv.org/abs/2405.15842)]

- "A Survey on Large Language Models for Code Generation" [2024-06] [[paper](https://arxiv.org/abs/2406.00515)]

- "Is Programming by Example solved by LLMs?" [2024-06] [[paper](https://arxiv.org/abs/2406.08316)]

- "Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review" [2024-06] [[paper](https://arxiv.org/abs/2406.12655)]

- "MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning" [2024-06] [ACL 2024] [[paper](https://arxiv.org/abs/2406.17255)]

- "Revisiting the Impact of Pursuing Modularity for Code Generation" [2024-07] [[paper](https://arxiv.org/abs/2407.11406)]

- "Evaluating Long Range Dependency Handling in Code Generation Models using Multi-Step Key Retrieval" [2024-07] [[paper](https://arxiv.org/abs/2407.21049)]

- "When to Stop? Towards Efficient Code Generation in LLMs with Excess Token Prevention" [2024-07] [[paper](https://arxiv.org/abs/2407.20042)]

- "Assessing Programming Task Difficulty for Efficient Evaluation of Large Language Models" [2024-07] [[paper](https://arxiv.org/abs/2407.21227)]

- "ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models" [2024-08] [ACL 2024] [[paper](https://www.arxiv.org/abs/2408.00994)]

- "Fine-tuning Language Models for Joint Rewriting and Completion of Code with Potential Bugs" [2024-08] [ACL 2024 Findings] [[paper](https://aclanthology.org/2024.findings-acl.938/)]

- "Selective Prompt Anchoring for Code Generation" [2024-08] [[paper](https://arxiv.org/abs/2408.09121)]

- "Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer" [2024-08] [[paper](https://arxiv.org/abs/2408.09701)]

- "Optimizing Large Language Model Hyperparameters for Code Generation" [2024-08] [[paper](https://arxiv.org/abs/2408.10577)]

- "EPiC: Cost-effective Search-based Prompt Engineering of LLMs for Code Generation" [2024-08] [[paper](https://arxiv.org/abs/2408.11198)]

- "CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers" [2024-08] [[paper](https://arxiv.org/abs/2408.13366)]

- "No Man is an Island: Towards Fully Automatic Programming by Code Search, Code Generation and Program Repair" [2024-09] [[paper](https://arxiv.org/abs/2409.03267)]

- "Planning In Natural Language Improves LLM Search For Code Generation" [2024-09] [[paper](https://arxiv.org/abs/2409.03733)]

- "Multi-Programming Language Ensemble for Code Generation in Large Language Model" [2024-09] [[paper](https://arxiv.org/abs/2409.04114)]

- "A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinement" [2024-09] [[paper](https://arxiv.org/abs/2409.05001)]

- "USCD: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding" [2024-09] [[paper](https://arxiv.org/abs/2409.05923)]

- "Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation" [2024-09] [[paper](https://arxiv.org/abs/2409.13928)]

- "Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity" [2024-09] [[paper](https://arxiv.org/abs/2409.16416)]

- "Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for Code Generation with Lookahead Planning" [2024-10] [[paper](https://arxiv.org/abs/2410.03103)]

- "Showing LLM-Generated Code Selectively Based on Confidence of LLMs" [2024-10] [[paper](https://arxiv.org/abs/2410.03234)]

- "AutoFeedback: An LLM-based Framework for Efficient and Accurate API Request Generation" [2024-10] [[paper](https://arxiv.org/abs/2410.06943)]

- "Enhancing LLM Agents for Code Generation with Possibility and Pass-rate Prioritized Experience Replay" [2024-10] [[paper](https://arxiv.org/abs/2410.12236)]

- "From Solitary Directives to Interactive Encouragement! LLM Secure Code Generation by Natural Language Prompting" [2024-10] [[paper](https://arxiv.org/abs/2410.14321)]

- "Self-Explained Keywords Empower Large Language Models for Code Generation" [2024-10] [[paper](https://arxiv.org/abs/2410.15966)]

- "Context-Augmented Code Generation Using Programming Knowledge Graphs" [2024-10] [[paper](https://arxiv.org/abs/2410.18251)]

- "In-Context Code-Text Learning for Bimodal Software Engineering" [2024-10] [[paper](https://arxiv.org/abs/2410.18107)]

- "Combining LLM Code Generation with Formal Specifications and Reactive Program Synthesis" [2024-10] [[paper](https://arxiv.org/abs/2410.19736)]

- "Less is More: DocString Compression in Code Generation" [2024-10] [[paper](https://arxiv.org/abs/2410.22793)]

- "Multi-Programming Language Sandbox for LLMs" [2024-10] [[paper](https://arxiv.org/abs/2410.23074)]

- "Personality-Guided Code Generation Using Large Language Models" [2024-10] [[paper](https://arxiv.org/abs/2411.00006)]

- "Do Advanced Language Models Eliminate the Need for Prompt Engineering in Software Engineering?" [2024-11] [[paper](https://arxiv.org/abs/2411.02093)]

- "Scattered Forest Search: Smarter Code Space Exploration with LLMs" [2024-11] [[paper](https://arxiv.org/abs/2411.05010)]

- "Anchor Attention, Small Cache: Code Generation with Large Language Models" [2024-11] [[paper](https://arxiv.org/abs/2411.06680)]

- "ROCODE: Integrating Backtracking Mechanism and Program Analysis in Large Language Models for Code Generation" [2024-11] [[paper](https://arxiv.org/abs/2411.07112)]

### Code RAG

- "CodeGRAG: Extracting Composed Syntax Graphs for Retrieval Augmented Cross-Lingual Code Generation" [2024-05] [[paper](https://arxiv.org/abs/2405.02355)]

- "Prompt-based Code Completion via Multi-Retrieval Augmented Generation" [2024-05] [[paper](https://arxiv.org/abs/2405.07530)]

- "A Lightweight Framework for Adaptive Retrieval In Code Completion With Critique Model" [2024-06] [[papaer](https://arxiv.org/abs/2406.10263)]

- "Preference-Guided Refactored Tuning for Retrieval Augmented Code Generation" [2024-09] [[paper](https://arxiv.org/abs/2409.15895)]

- "Building A Coding Assistant via the Retrieval-Augmented Language Model" [2024-10] [[paper](https://arxiv.org/abs/2410.16229)]

- "DroidCoder: Enhanced Android Code Completion with Context-Enriched Retrieval-Augmented Generation" [2024-10] [ASE 2024] [[paper](https://dl.acm.org/doi/10.1145/3691620.3695063)]

- "Assessing the Answerability of Queries in Retrieval-Augmented Code Generation" [2024-11] [[paper](https://arxiv.org/abs/2411.05547)]

### Code Ranking

- "Fault-Aware Neural Code Rankers" [2022-06] [NeurIPS 2022] [[paper](https://arxiv.org/abs/2206.03865)]

- "Functional Overlap Reranking for Neural Code Generation" [2023-10] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2311.03366)]

- "Top Pass: Improve Code Generation by Pass@k-Maximized Code Ranking" [2024-08] [[paper](https://arxiv.org/abs/2408.05715)]

- "DOCE: Finding the Sweet Spot for Execution-Based Code Generation" [2024-08] [[paper](https://arxiv.org/abs/2408.13745)]

- "Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates" [2024-08] [[paper](https://arxiv.org/abs/2408.13976)]

- "B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests" [2024-09] [[paper](https://arxiv.org/abs/2409.08692)]

- "Learning Code Preference via Synthetic Evolution" [2024-10] [[paper](https://arxiv.org/abs/2410.03837)]

### Code Translation

- "Tree-to-tree Neural Networks for Program Translation" [2018-02] [NeurIPS 2018] [[paper](https://arxiv.org/abs/1802.03691)]

- "Program Language Translation Using a Grammar-Driven Tree-to-Tree Model" [2018-07] [[paper](https://arxiv.org/abs/1807.01784)]

- "Unsupervised Translation of Programming Languages" [2020-06] [NeurIPS 2020] [[paper](https://arxiv.org/abs/2006.03511)]

- "Leveraging Automated Unit Tests for Unsupervised Code Translation" [2021-10] [ICLR 2022] [paper](https://arxiv.org/abs/2110.06773)]

- "Code Translation with Compiler Representations" [2022-06] [ICLR 2023] [[paper](https://arxiv.org/abs/2207.03578)]

- "Multilingual Code Snippets Training for Program Translation" [2022-06] [AAAI 2022] [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/21434)]

- "BabelTower: Learning to Auto-parallelized Program Translation" [2022-07] [ICML 2022] [[paper](https://proceedings.mlr.press/v162/wen22b.html)]

- "Syntax and Domain Aware Model for Unsupervised Program Translation" [2023-02] [ICSE 2023] [[paper](https://arxiv.org/abs/2302.03908)]

- "CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Execution" [2023-06] [[paper](https://arxiv.org/abs/2306.06755)]

- "Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code" [2023-08] [ICSE 2024] [[paper](https://arxiv.org/abs/2308.03109)]

- "On the Evaluation of Neural Code Translation: Taxonomy and Benchmark", 2023-08, ASE 2023, [[paper](https://arxiv.org/abs/2308.08961)]

- "Program Translation via Code Distillation" [2023-10] [EMNLP 2023] [[paper](https://arxiv.org/abs/2310.11476)]

- "Explain-then-Translate: An Analysis on Improving Program Translation with Self-generated Explanations" [2023-11] [EMNLP 2023 Findings] [[paper](https://arxiv.org/abs/2311.07070)]

- "Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation" [2024-03] [[paper](https://arxiv.org/abs/2403.17214)]

- "Exploring and Unleashing the Power of Large Language Models in Automated Code Translation" [2024-04] [[paper](https://arxiv.org/abs/2404.14646)]

- "VERT: Verified Equivalent Rust Transpilation with Few-Shot Learning" [2024-04] [[paper](https://arxiv.org/abs/2404.18852)]

- "Towards Translating Real-World Code with LLMs: A Study of Translating to Rust" [2024-05] [[paper](https://arxiv.org/abs/2405.11514)]

- "An interpretable error correction method for enhancing code-to-code translation" [2024-05] [ICLR 2024] [[paper](https://openreview.net/forum?id=fVxIEHGnVT&noteId=CyxZE2UbHF)]

- "LASSI: An LLM-based Automated Self-Correcting Pipeline for Translating Parallel Scientific Codes" [2024-06] [[paper](https://arxiv.org/abs/2407.01638)]

- "Rectifier: Code Translation with Corrector via LLMs" [2024-07] [[paper](https://arxiv.org/abs/2407.07472)]

- "Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation" [2024-07] [[paper](https://arxiv.org/abs/2407.19619)]

- "A Joint Learning Model with Variational Interaction for Multilingual Program Translation" [2024-08] [[paper](https://arxiv.org/abs/2408.14515)]

- "Automatic Library Migration Using Large Language Models: First Results" [2024-08] [[paper](https://arxiv.org/abs/2408.16151)]

- "Context-aware Code Segmentation for C-to-Rust Translation using Large Language Models" [2024-09] [[paper](https://arxiv.org/abs/2409.10506)]

- "TRANSAGENT: An LLM-Based Multi-Agent System for Code Translation" [2024-10] [[paper](https://arxiv.org/abs/2409.19894)]

- "Unraveling the Potential of Large Language Models in Code Translation: How Far Are We?" [2024-10] [[paper](https://arxiv.org/abs/2410.09812)]

- "CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming" [2024-10] [[paper](https://arxiv.org/abs/2410.20527)]

- "A test-free semantic mistakes localization framework in Neural Code Translation" [2024-10] [[paper](https://arxiv.org/abs/2410.22818)]

- "Repository-Level Compositional Code Translation and Validation" [2024-10] [[paper](https://arxiv.org/abs/2410.24117)]

- "Leveraging Large Language Models for Code Translation and Software Development in Scientific Computing" [2024-10] [[paper](https://arxiv.org/abs/2410.24119)]

- "InterTrans: Leveraging Transitive Intermediate Translations to Enhance LLM-based Code Translation" [2024-11] [[paper](https://arxiv.org/abs/2411.01063)]

### Code Commenting and Summarization

- "A Transformer-based Approach for Source Code Summarization" [2020-05] [ACL 2020] [[paper](https://arxiv.org/abs/2005.00653)]

- "Code Summarization with Structure-induced Transformer" [2020-12] [ACL 2021 Findings] [[paper](https://arxiv.org/abs/2012.14710)]

- "Code Structure Guided Transformer for Source Code Summarization" [2021-04] [ACM TSEM] [[paper](https://arxiv.org/abs/2104.09340)]

- "M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization" [2022-03] [ICPC 2022] [[paper](https://arxiv.org/abs/2203.09707)]

- "AST-trans: code summarization with efficient tree-structured attention" [2022-05] [ICSE 2022] [[paper](https://dl.acm.org/doi/abs/10.1145/3510003.3510224)]

- "CoSS: Leveraging Statement Semantics for Code Summarization" [2023-03] [IEEE TSE] [[paper](https://ieeexplore.ieee.org/document/10068264)]

- "Automatic Code Summarization via ChatGPT: How Far Are We?" [2023-05] [[paper](https://arxiv.org/abs/2305.12865)]

- "Semantic Similarity Loss for Neural Source Code Summarization" [2023-08] [[paper](https://arxiv.org/abs/2308.07429)]

- "Distilled GPT for Source Code Summarization" [2023-08] [ASE] [[paper](https://arxiv.org/abs/2308.14731)]

- "CSA-Trans: Code Structure Aware Transformer for AST" [2024-04] [[paper](https://arxiv.org/abs/2404.05767)]

- "Analyzing the Performance of Large Language Models on Code Summarization" [2024-04] [[paper](https://arxiv.org/abs/2404.08018)]

- "Enhancing Trust in LLM-Generated Code Summaries with Calibrated Confidence Scores" [2024-04] [[paper](https://arxiv.org/abs/2404.19318)]

- "DocuMint: Docstring Generation for Python using Small Language Models" [2024-05] [[paper](https://arxiv.org/abs/2405.10243)] [[repo](https://github.com/Docu-Mint/DocuMint)]

- "Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models" [2024-05] [[paper](https://arxiv.org/abs/2405.11196)]

- "Large Language Models for Code Summarization" [2024-05] [[paper](https://arxiv.org/abs/2405.19032)]

- "Exploring the Efficacy of Large Language Models (GPT-4) in Binary Reverse Engineering" [2024-06] [[paper](https://arxiv.org/abs/2406.06637)]

- "Identifying Inaccurate Descriptions in LLM-generated Code Comments via Test Execution" [2024-06] [[paper](https://arxiv.org/abs/2406.14836)]

- "MALSIGHT: Exploring Malicious Source Code and Benign Pseudocode for Iterative Binary Malware Summarization" [2024-06] [[paper](https://arxiv.org/abs/2406.18379)]

- "ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization" [2024-07] [[paper](https://arxiv.org/abs/2407.01646)]

- "Source Code Summarization in the Era of Large Language Models" [2024-07] [[paper](https://arxiv.org/abs/2407.07959)]

- "Natural Language Outlines for Code: Literate Programming in the LLM Era" [2024-08] [[paper](https://arxiv.org/abs/2408.04820)]

- "Context-aware Code Summary Generation" [2024-08] [[paper](https://arxiv.org/abs/2408.09006)]

- "AUTOGENICS: Automated Generation of Context-Aware Inline Comments for Code Snippets on Programming Q&A Sites Using LLM" [2024-08] [[paper](https://arxiv.org/abs/2408.15411)]

- "LLMs as Evaluators: A Novel Approach to Evaluate Bug Report Summarization" [2024-09] [[paper](https://arxiv.org/abs/2409.00630)]

- "Evaluating the Quality of Code Comments Generated by Large Language Models for Novice Programmers" [2024-09] [[paper](https://arxiv.org/abs/2409.14368)]

- "Generating Equivalent Representations of Code By A Self-Reflection Approach" [2024-10] [[paper](https://arxiv.org/abs/2410.03351)]

- "A review of automatic source code summarization" [2024-10] [Empirical Software Engineering] [[paper](https://link.springer.com/article/10.1007/s10664-024-10553-6)]

### Program Repair

- "DeepDebug: Fixing Python Bugs Using Stack Traces, Backtranslation, and Code Skeletons" [2021-05] [[paper](https://arxiv.org/abs/2105.09352)]

- "Break-It-Fix-It: Unsupervised Learning for Program Repair" [2021-06] [ICML 2021] [[paper](https://arxiv.org/abs/2106.06600)]

- "TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer" [2021-07] [ICML 2021] [[paper](https://proceedings.mlr.press/v139/berabi21a.html)]

- "Automated Repair of Programs from Large Language Models" [2022-05] [ICSE 2023] [[paper](https://arxiv.org/abs/2205.10583)]

- "Less Training, More Repairing Please: Revisiting Automated Program Repair via Zero-shot Learning" [2022-07] [ESEC/FSE 2022] [[paper](https://arxiv.org/abs/2207.08281)]

- "Repair Is Nearly Generation: Multilingual Program Repair with LLMs" [2022-08] [AAAI 2023] [[paper](https://arxiv.org/abs/2208.11640)]

- "Practical Program Repair in the Era of Large Pre-trained Language Models" [2022-10] [[paper](https://arxiv.org/abs/2210.14179)]

- "VulRepair: a T5-based automated software vulnerability repair" [2022-11] [ESEC/FSE 2022] [[paper](https://dl.acm.org/doi/abs/10.1145/3540250.3549098)]

- "Conversational Automated Program Repair" [2023-01] [[paper](https://arxiv.org/abs/2301.13246)]

- "Impact of Code Language Models on Automated Program Repair" [2023-02] [ICSE 2023] [[paper](https://arxiv.org/abs/2302.05020)]

- "InferFix: End-to-End Program Repair with LLMs" [2023-03] [ESEC/FSE 2023] [[paper](https://arxiv.org/abs/2303.07263)]

- "Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering" [2023-04] [[paper](https://arxiv.org/abs/2304.07840)]

- "A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repair" [2023-04] [[paper](https://arxiv.org/abs/2304.08191)]

- "Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors" [2023-06] [ICSE 2024] [[paper](https://arxiv.org/abs/2306.01394)]

- "RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair" [2023-12] [[paper](https://arxiv.org/abs/2312.15698)]

- "The Fact Selection Problem in LLM-Based Program Repair" [2024-04] [[paper](https://arxiv.org/abs/2404.05520)]

- "Aligning LLMs for FL-free Program Repair" [2024-04] [[paper](https://arxiv.org/abs/2404.08877)]

- "A Deep Dive into Large Language Models for Automated Bug Localization and Repair" [2024-04] [[paper](https://arxiv.org/abs/2404.11595)]

- "Multi-Objective Fine-Tuning for Enhanced Program Repair with LLMs" [2024-04] [[paper](https://arxiv.org/abs/2404.12636)]

- "How Far Can We Go with Practical Function-Level Program Repair?" [2024-04] [[paper](https://arxiv.org/abs/2404.12833)]

- "Revisiting Unnaturalness for Automated Program Repair in the Era of Large Language Models" [2024-04] [[paper](https://arxiv.org/abs/2404.15236)]

- "A Unified Debugging Approach via LLM-Based Multi-Agent Synergy" [2024-04] [[paper](https://arxiv.org/abs/2404.17153)]

- "A Systematic Literature Review on Large Language Models for Automated Program Repair" [2024-05] [[paper](https://arxiv.org/abs/2405.01466)]

- "NAVRepair: Node-type Aware C/C++ Code Vulnerability Repair" [2024-05] [[paper](https://arxiv.org/abs/2405.04994)]

- "Automated Program Repair: Emerging trends pose and expose problems for benchmarks" [2024-05] [[paper](https://arxiv.org/abs/2405.05455)]

- "Automated Repair of AI Code with Large Language Models and Formal Verification" [2024-05] [[paper](https://arxiv.org/abs/2405.08848)]

- "A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback" [2024-05] [[paper](https://arxiv.org/abs/2405.15690)]

- "CREF: An LLM-based Conversational Software Repair Framework for Programming Tutors" [2024-06] [[paper](https://arxiv.org/abs/2406.13972)]

- "Towards Practical and Useful Automated Program Repair for Debugging" [2024-07] [[paper](https://arxiv.org/abs/2407.08958)]

- "ThinkRepair: Self-Directed Automated Program Repair" [2024-07] [[paper](https://arxiv.org/abs/2407.20898)]

- "MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair" [2024-08] [[paper](https://arxiv.org/abs/2408.09568)]

- "RePair: Automated Program Repair with Process-based Feedback" [2024-08] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2408.11296)]

- "Enhancing LLM-Based Automated Program Repair with Design Rationales" [2024-08] [[paper](https://arxiv.org/abs/2408.12056)]

- "Automated Software Vulnerability Patching using Large Language Models" [2024-08] [[paper](https://arxiv.org/abs/2408.13597)]

- "Enhancing Source Code Security with LLMs: Demystifying The Challenges and Generating Reliable Repairs" [2024-09] [[paper](https://arxiv.org/abs/2409.00571)]

- "MarsCode Agent: AI-native Automated Bug Fixing" [2024-09] [[paper](https://arxiv.org/abs/2409.00899)]

- "Co-Learning: Code Learning for Multi-Agent Reinforcement Collaborative Framework with Conversational Natural Language Interfaces" [2024-09] [[paper](https://arxiv.org/abs/2409.00985)]

- "Debugging with Open-Source Large Language Models: An Evaluation" [2024-09] [[paper](https://arxiv.org/abs/2409.03031)]

- "VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching" [2024-09] [[paper](https://arxiv.org/abs/2409.10756)]

- "ContractTinker: LLM-Empowered Vulnerability Repair for Real-World Smart Contracts" [2024-09] [[paper](https://arxiv.org/abs/2409.09661)]

- "Can GPT-O1 Kill All Bugs? An Evaluation of GPT-Family LLMs on QuixBugs" [2024-09] [[paper](https://arxiv.org/abs/2409.10033)]

- "Exploring and Lifting the Robustness of LLM-powered Automated Program Repair with Metamorphic Testing" [2024-10] [[paper](https://arxiv.org/abs/2410.07516)]

- "LecPrompt: A Prompt-based Approach for Logical Error Correction with CodeBERT" [2024-10] [[paper](https://arxiv.org/abs/2410.08241)]

- "Semantic-guided Search for Efficient Program Repair with Large Language Models" [2024-10] [[paper](https://arxiv.org/abs/2410.16655)]

- "A Comprehensive Survey of AI-Driven Advancements and Techniques in Automated Program Repair and Code Generation" [2024-11] [[paper](https://arxiv.org/abs/2411.07586)]

### Code Similarity and Embedding (Clone Detection, Code Search)

- "Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations" [2020-09] [SIGIR 2021] [[paper](https://arxiv.org/abs/2009.02731)]

- "REINFOREST: Reinforcing Semantic Code Similarity for Cross-Lingual Code Search Models" [2023-05] [[paper](https://arxiv.org/abs/2305.03843)]

- "Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search" [2024-01] [ACL 2024] [[paper](https://arxiv.org/abs/2401.04514)]

- "Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance" [2024-04] [ACL 2024 short] [[paper](https://arxiv.org/abs/2404.08817)]

- "Is Next Token Prediction Sufficient for GPT? Exploration on Code Logic Comprehension" [2024-04] [[paper](https://arxiv.org/abs/2404.08885)]

- "Refining Joint Text and Source Code Embeddings for Retrieval Task with Parameter-Efficient Fine-Tuning" [2024-05] [[paper](https://arxiv.org/abs/2405.04126)]

- "Typhon: Automatic Recommendation of Relevant Code Cells in Jupyter Notebooks" [2024-05] [[paper](https://arxiv.org/abs/2405.09075)]

- "Toward Exploring the Code Understanding Capabilities of Pre-trained Code Generation Models" [2024-06] [[paper](https://arxiv.org/abs/2406.12326)]

- "Aligning Programming Language and Natural Language: Exploring Design Choices in Multi-Modal Transformer-Based Embedding for Bug Localization" [2024-06] [[paper](https://arxiv.org/abs/2406.17615)]

- "Assessing the Code Clone Detection Capability of Large Language Models" [2024-07] [[paper](https://arxiv.org/abs/2407.02402)]

- "CodeCSE: A Simple Multilingual Model for Code and Comment Sentence Embeddings" [2024-07] [[paper](https://arxiv.org/abs/2407.06360)]

- "Large Language Models for cross-language code clone detection" [2024-08] [[paper](https://arxiv.org/abs/2408.04430)]

- "Coding-PTMs: How to Find Optimal Code Pre-trained Models for Code Embedding in Vulnerability Detection?" [2024-08] [[paper](https://arxiv.org/abs/2408.04863)]

- "You Augment Me: Exploring ChatGPT-based Data Augmentation for Semantic Code Search" [2024-08] [[paper](https://arxiv.org/abs/2408.05542)]

- "Improving Source Code Similarity Detection Through GraphCodeBERT and Integration of Additional Features" [2024-08] [[paper](https://arxiv.org/abs/2408.08903)]

- "LLM Agents Improve Semantic Code Search" [2024-08] [[paper](https://arxiv.org/abs/2408.11058)]

- "zsLLMCode: An Effective Approach for Functional Code Embedding via LLM with Zero-Shot Learning" [2024-09] [[paper](https://arxiv.org/abs/2409.14644)]

- "Exploring Demonstration Retrievers in RAG for Coding Tasks: Yeas and Nays!" [2024-10] [[paper](https://arxiv.org/abs/2410.09662)]

- "Instructive Code Retriever: Learn from Large Language Model's Feedback for Code Intelligence Tasks" [2024-10] [[paper](https://arxiv.org/abs/2410.11300)]

- "Binary Code Similarity Detection via Graph Contrastive Learning on Intermediate Representations" [2024-10] [[paper](https://arxiv.org/abs/2410.18561)]

- "Are Decoder-Only Large Language Models the Silver Bullet for Code Search?" [2024-10] [[paper](https://arxiv.org/abs/2410.22240)]

### Code Refactoring

- "An Empirical Study on the Code Refactoring Capability of Large Language Models" [2024-11] [[paper](https://arxiv.org/abs/2411.02320)]

- "Automated Update of Android Deprecated API Usages with Large Language Models" [2024-11] [[paper](https://arxiv.org/abs/2411.04387)]

- "An Empirical Study on the Potential of LLMs in Automated Software Refactoring" [2024-11] [[paper](https://arxiv.org/abs/2411.04444)]

### Type Prediction

- "Learning type annotation: is big data enough?" [2021-08] [ESEC/FSE 2021] [[paper](https://dl.acm.org/doi/10.1145/3468264.3473135)]

- "Do Machine Learning Models Produce TypeScript Types That Type Check?" [2023-02] [ECOOP 2023] [[paper](https://arxiv.org/abs/2302.12163)]

- "TypeT5: Seq2seq Type Inference using Static Analysis" [2023-03] [ICLR 2023] [[paper](https://arxiv.org/abs/2303.09564)]

- "Type Prediction With Program Decomposition and Fill-in-the-Type Training" [2023-05] [[paper](https://arxiv.org/abs/2305.17145)]

- "Generative Type Inference for Python" [2023-07] [ASE 2023] [[paper](https://arxiv.org/abs/2307.09163)]

- "Activation Steering for Robust Type Prediction in CodeLLMs" [2024-04] [[paper](https://arxiv.org/abs/2404.01903)]

- "An Empirical Study of Large Language Models for Type and Call Graph Analysis" [2024-10] [[paper](https://arxiv.org/abs/2410.00603)]

### Repository-Level Coding

- "Repository-Level Prompt Generation for Large Language Models of Code" [2022-06] [ICML 2023] [[paper](https://arxiv.org/abs/2206.12839)]

- "CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context" [2022-12] [[paper](https://arxiv.org/abs/2212.10007)]

- "RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation" [2023-03] [EMNLP 2023] [[paper](https://arxiv.org/abs/2303.12570)]

- "Coeditor: Leveraging Repo-level Diffs for Code Auto-editing" [2023-05] [ICLR 2024 Spotlight] [[paper](https://arxiv.org/abs/2305.18584)]

- "RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems" [2023-06] [ICLR 2024] [[paper](https://arxiv.org/abs/2306.03091)]

- "Guiding Language Models of Code with Global Context using Monitors" [2023-06] [[paper](https://arxiv.org/abs/2306.10763)]

- "RepoFusion: Training Code Models to Understand Your Repository" [2023-06] [[paper](https://arxiv.org/abs/2306.10998)]

- "CodePlan: Repository-level Coding using LLMs and Planning" [2023-09] [[paper](https://arxiv.org/abs/2306.10998)]

- "SWE-bench: Can Language Models Resolve Real-World GitHub Issues?" [2023-10] [ICLR 2024] [[paper](https://arxiv.org/abs/2310.06770)]

- "CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion" [2023-10] [NeurIPS 2023] [[paper](https://arxiv.org/abs/2310.11248)]

- "A^3-CodGen: A Repository-Level Code Generation Framework for Code Reuse with Local-Aware, Global-Aware, and Third-Party-Library-Aware" [2023-12] [[paper](https://arxiv.org/abs/2312.05772)]

- "Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation" [2024-01] [[paper](https://arxiv.org/abs/2401.06391)]

- "RepoHyper: Better Context Retrieval Is All You Need for Repository-Level Code Completion" [2024-03] [[paper](https://arxiv.org/abs/2403.06095)]

- "Repoformer: Selective Retrieval for Repository-Level Code Completion" [2024-03] [ICML 2024] [[paper](https://arxiv.org/abs/2403.10059)]

- "CodeS: Natural Language to Code Repository via Multi-Layer Sketch" [2024-03] [[paper](https://arxiv.org/abs/2403.16443)]

- "Class-Level Code Generation from Natural Language Using Iterative, Tool-Enhanced Reasoning over Repository" [2024-04] [[paper](https://arxiv.org/abs/2405.01573)]

- "Contextual API Completion for Unseen Repositories Using LLMs" [2024-05] [[paper](https://arxiv.org/abs/2405.04600)]

- "Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion" [2024-05][ACL 2024] [[paper](https://arxiv.org/abs/2405.19782)]

- "How to Understand Whole Software Repository?" [2024-06] [[paper](https://arxiv.org/abs/2406.01422)]

- "R2C2-Coder: Enhancing and Benchmarking Real-world Repository-level Code Completion Abilities of Code Large Language Models" [2024-06] [[paper](https://arxiv.org/abs/2406.01359)]

- "CodeR: Issue Resolving with Multi-Agent and Task Graphs" [2024-06] [[paper](https://arxiv.org/abs/2406.01304)]

- "Enhancing Repository-Level Code Generation with Integrated Contextual Information" [2024-06] [[paper](https://arxiv.org/abs/2406.03283)]

- "On The Importance of Reasoning for Context Retrieval in Repository-Level Code Editing" [2024-06] [[paper](https://arxiv.org/abs/2406.04464)]

- "GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model" [2024-06] [ASE 2024] [[paper](https://arxiv.org/abs/2406.07003)]

- "STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis" [2024-06] [[paper](https://arxiv.org/abs/2406.10018)]

- "Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs" [2024-06] [[paper](https://arxiv.org/abs/2406.18294)]

- "Agentless: Demystifying LLM-based Software Engineering Agents" [2024-07] [[paper](https://arxiv.org/abs/2407.01489)]

- "RLCoder: Reinforcement Learning for Repository-Level Code Completion" [2024-07] [[paper](https://arxiv.org/abs/2407.19487)]

- "CoEdPilot: Recommending Code Edits with Learned Prior Edit Relevance, Project-wise Awareness, and Interactive Nature" [2024-08] [[paper](https://arxiv.org/abs/2408.01733)] [[repo](https://github.com/code-philia/CoEdPilot)]

- "RAMBO: Enhancing RAG-based Repository-Level Method Body Completion" [2024-09] [[paper](https://arxiv.org/abs/2409.15204)]

- "Exploring the Potential of Conversational Test Suite Based Program Repair on SWE-bench" [2024-10] [[paper](https://arxiv.org/abs/2410.04485)]

- "RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph" [2024-10] [[paper](https://arxiv.org/abs/2410.14684)]

### Frontend Development

- "Seeking the user interface", 2014-09, ASE 2014, [[paper](https://dl.acm.org/doi/10.1145/2642937.2642976)]

- "pix2code: Generating Code from a Graphical User Interface Screenshot", 2017-05, EICS 2018, [[paper](https://arxiv.org/abs/1705.07962)]

- "Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps", 2018-02, TSE 2020, [[paper](https://arxiv.org/abs/1802.02312)]

- "Automatic HTML Code Generation from Mock-Up Images Using Machine Learning Techniques", 2019-04, EBBT 2019, [[paper](https://ieeexplore.ieee.org/abstract/document/8741736)]

- "Sketch2code: Generating a website from a paper mockup", 2019-05, [[paper](https://arxiv.org/abs/1905.13750)]

- "HTLM: Hyper-Text Pre-Training and Prompting of Language Models", 2021-07, ICLR 2022, [[paper](https://arxiv.org/abs/2107.06955)]

- "Learning UI-to-Code Reverse Generator Using Visual Critic Without Rendering", 2023-05, [[paper](https://arxiv.org/abs/2305.14637)]

- "Design2Code: How Far Are We From Automating Front-End Engineering?" [2024-03] [[paper](https://arxiv.org/abs/2403.03163)]

- "Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset" [2024-03] [[paper](https://arxiv.org/abs/2403.09029)]

- "VISION2UI: A Real-World Dataset with Layout for Code Generation from UI Designs" [2024-04] [[paper](https://arxiv.org/abs/2404.06369)]

- "LogoMotion: Visually Grounded Code Generation for Content-Aware Animation" [2024-05] [[paper](https://arxiv.org/abs/2405.07065)]

- "PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLM" [2024-06] [[paper](https://arxiv.org/abs/2406.02884)]

- "UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback" [2024-06] [[paper](https://arxiv.org/abs/2406.07739)]

- "On AI-Inspired UI-Design" [2024-06] [[paper](https://arxiv.org/abs/2406.13631)]

- "Identifying User Goals from UI Trajectories" [2024-06] [[paper](https://arxiv.org/abs/2406.14314)]

- "Automatically Generating UI Code from Screenshot: A Divide-and-Conquer-Based Approach" [2024-06] [[paper](https://arxiv.org/abs/2406.16386)]

- "Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs" [2024-06] [[paper](https://arxiv.org/abs/2406.20098)]

- "Vision-driven Automated Mobile GUI Testing via Multimodal Large Language Model" [2024-07] [[paper](https://arxiv.org/abs/2407.03037)]

- "AUITestAgent: Automatic Requirements Oriented GUI Function Testing" [2024-07] [[paper](https://arxiv.org/abs/2407.09018)]

- "LLM-based Abstraction and Concretization for GUI Test Migration" [2024-09] [[paper](https://arxiv.org/abs/2409.05028)]

- "Enabling Cost-Effective UI Automation Testing with Retrieval-Based LLMs: A Case Study in WeChat" [2024-09] [[paper](https://arxiv.org/abs/2409.07829)]

- "Self-Elicitation of Requirements with Automated GUI Prototyping" [2024-09] [[paper](https://arxiv.org/abs/2409.16388)]

- "Infering Alt-text For UI Icons With Large Language Models During App Development" [2024-09] [[paper](https://arxiv.org/abs/2409.18060)]

- "Leveraging Large Vision Language Model For Better Automatic Web GUI Testing" [2024-10] [[paper](https://arxiv.org/abs/2410.12157)]

- "Sketch2Code: Evaluating Vision-Language Models for Interactive Web Design Prototyping" [2024-10] [[paper](https://arxiv.org/abs/2410.16232)]

- "WAFFLE: Multi-Modal Model for Automated Front-End Development" [2024-10] [[paper](https://arxiv.org/abs/2410.18362)]

- "DesignRepair: Dual-Stream Design Guideline-Aware Frontend Repair with Large Language Models" [2024-11] [[paper](https://arxiv.org/abs/2411.01606)]

- "Interaction2Code: How Far Are We From Automatic Interactive Webpage Generation?" [2024-11] [[paper](https://arxiv.org/abs/2411.03292)]

- "A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs" [2024-11] [[paper](https://arxiv.org/abs/2411.07098)]

### Text-To-SQL

- "PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models" [2021-09] [EMNLP 2021] [[paper](https://arxiv.org/abs/2109.05093)]

- "CodexDB: Generating Code for Processing SQL Queries using GPT-3 Codex" [2022-04] [[paper](https://arxiv.org/abs/2204.08941)]

- "T5QL: Taming language models for SQL generation" [2022-09] [[paper](https://arxiv.org/abs/2209.10254)]

- "Towards Generalizable and Robust Text-to-SQL Parsing" [2022-10] [EMNLP 2022 Findings] [[paper](https://arxiv.org/abs/2210.12674)]

- "XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing" [2022-10] [EMNLP 2022 Findings] [[paper](https://arxiv.org/abs/2210.13693)]

- "A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability" [2023-03] [[paper](https://arxiv.org/abs/2303.13547)]

- "DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction" [2023-04] [NeurIPS 2023] [[paper](https://arxiv.org/abs/2304.11015)]

- "How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings" [2023-05] [[paper](https://arxiv.org/abs/2305.11853)]

- "Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies" [2023-05] [[paper](https://arxiv.org/abs/2305.12586)]

- "SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL" [2023-05] [[paper](https://arxiv.org/abs/2306.00739)]

- "Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain" [2023-07] [ICONIP 2023] [[paper](https://arxiv.org/abs/2307.05074)]

- "Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation" [2023-08] [[paper](https://arxiv.org/abs/2308.15363)]

- "MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL" [2023-12] [[paper](https://arxiv.org/abs/2312.11242)]

- "Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation" [2024-02] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2402.08100)]

- "Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm" [2024-02] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2402.10671)]

- "Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM" [2024-02] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2402.11517)]

- "Understanding the Effects of Noise in Text-to-SQL: An Examination of the BIRD-Bench Benchmark" [2024-02] [ACL 2024 short] [[paper](https://arxiv.org/abs/2402.12243)]

- "SQL-Encoder: Improving NL2SQL In-Context Learning Through a Context-Aware Encoder" [2024-03] [[paper](https://arxiv.org/abs/2403.16204)]

- "LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency" [2024-04] [[paper](https://arxiv.org/abs/2404.12872)]

- "Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQL" [2024-04] [[paper](https://arxiv.org/abs/2404.12560)]

- "EPI-SQL: Enhancing Text-to-SQL Translation with Error-Prevention Instructions" [2024-04] [[paper](https://arxiv.org/abs/2404.14453)]

- "ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error Handling" [2024-04] [[paper](https://arxiv.org/abs/2404.16659)]

- "CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions" [2024-05] [[paper](https://arxiv.org/abs/2405.02712)]

- "Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models" [2024-05] [[paper](https://arxiv.org/abs/2405.06674)]

- "MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation" [2024-05] [[paper](https://arxiv.org/abs/2405.07467)]

- "PromptMind Team at EHRSQL-2024: Improving Reliability of SQL Generation using Ensemble LLMs" [2024-05] [[paper](https://arxiv.org/abs/2405.08839)]

- "LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRs" [2024-05] [[paper](https://arxiv.org/abs/2405.11162)]

- "Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation" [2024-05] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2405.15307)]

- "CHESS: Contextual Harnessing for Efficient SQL Synthesis" [2024-05] [[paper](https://arxiv.org/abs/2405.16755)]

- "DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning" [2024-06] [[paper](https://arxiv.org/abs/2406.07913)]

- "Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL" [2024-06] [[paper](https://arxiv.org/abs/2406.08426)]

- "RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQL" [2024-06] [[paper](https://arxiv.org/abs/2406.09133)]

- "QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL" [2024-06] [[paper](https://arxiv.org/abs/2406.10593)]

- "End-to-end Text-to-SQL Generation within an Analytics Insight Engine" [2024-06] [[paper](https://arxiv.org/abs/2406.12104)]

- "MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQL" [2024-06] [[paper](https://arxiv.org/abs/2406.12692)]

- "SQLFixAgent: Towards Semantic-Accurate SQL Generation via Multi-Agent Collaboration" [2024-06] [[paper](https://arxiv.org/abs/2406.13408)]

- "Unmasking Database Vulnerabilities: Zero-Knowledge Schema Inference Attacks in Text-to-SQL Systems" [2024-06] [[paper](https://arxiv.org/abs/2406.14545)]

- "Lucy: Think and Reason to Solve Text-to-SQL" [2024-07] [[paper](https://arxiv.org/abs/2407.05153)]

- "ESM+: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models" [2024-07] [[paper](https://arxiv.org/abs/2407.07313)]

- "RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL" [2024-07] [[paper](https://arxiv.org/abs/2407.08273)]

- "AI-Assisted SQL Authoring at Industry Scale" [2024-07] [[paper](https://arxiv.org/abs/2407.13280)]

- "SQLfuse: Enhancing Text-to-SQL Performance through Comprehensive LLM Synergy" [2024-07] [[paper](https://arxiv.org/abs/2407.14568)]

- "A Survey on Employing Large Language Models for Text-to-SQL Tasks" [2024-07] [[paper](https://arxiv.org/abs/2407.15186)]

- "Towards Automated Data Sciences with Natural Language and SageCopilot: Practices and Lessons Learned" [2024-07] [[paper](https://arxiv.org/abs/2407.21040)]

- "Evaluating LLMs for Text-to-SQL Generation With Complex SQL Workload" [2024-07] [[paper](https://arxiv.org/abs/2407.19517)]

- "Synthesizing Text-to-SQL Data from Weak and Strong LLMs" [2024-08] [ACL 2024] [[paper](https://arxiv.org/abs/2408.03256)]

- "Improving Relational Database Interactions with Large Language Models: Column Descriptions and Their Impact on Text-to-SQL Performance" [2024-08] [[paper](https://arxiv.org/abs/2408.04691)]

- "The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models" [2024-08] [[paper](https://arxiv.org/abs/2408.07702)]

- "MAG-SQL: Multi-Agent Generative Approach with Soft Schema Linking and Iterative Sub-SQL Refinement for Text-to-SQL" [2024-08] [[paper](https://arxiv.org/abs/2408.07930)]

- "Enhancing Text-to-SQL Parsing through Question Rewriting and Execution-Guided Refinement" [2024-08] [ACL 2024 Findings] [[paper](https://aclanthology.org/2024.findings-acl.120/)]

- "DAC: Decomposed Automation Correction for Text-to-SQL" [2024-08] [[paper](https://arxiv.org/abs/2408.08779)]

- "Interactive-T2S: Multi-Turn Interactions for Text-to-SQL with Large Language Models" [2024-08] [[paper](https://arxiv.org/abs/2408.11062)]

- "SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging" [2024-08] [[paper](https://arxiv.org/abs/2408.12733)]

- "Enhancing SQL Query Generation with Neurosymbolic Reasoning" [2024-08] [[paper](https://arxiv.org/abs/2408.13888)]

- "Text2SQL is Not Enough: Unifying AI and Databases with TAG" [2024-08] [[paper](https://arxiv.org/abs/2408.14717)]

- "Tool-Assisted Agent on SQL Inspection and Refinement in Real-World Scenarios" [2024-08] [[paper](https://arxiv.org/abs/2408.16991)]

- "SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQL" [2024-09] [[paper](https://arxiv.org/abs/2409.10007)]

- "You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL" [2024-09] [[paper](https://arxiv.org/abs/2409.12172)]

- "PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL" [2024-09] [[paper](https://arxiv.org/abs/2409.14082)]

- "Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection" [2024-09] [[paper](https://arxiv.org/abs/2409.15907)]

- "DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQL" [2024-09] [[paper](https://arxiv.org/abs/2409.15985)]

- "E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL" [2024-09] [[paper](https://arxiv.org/abs/2409.16751)]

- "FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL Benchmark" [2024-09] [[paper](https://arxiv.org/abs/2409.19014)]

- "Enhancing LLM Fine-tuning for Text-to-SQLs by SQL Quality Measurement" [2024-10] [[paper](https://arxiv.org/abs/2410.01869)]

- "From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems" [2024-10] [[paper](https://arxiv.org/abs/2410.01066)]

- "CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL" [2024-10] [[paper](https://arxiv.org/abs/2410.01943)]

- "Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similarity" [2024-10] [[paper](https://arxiv.org/abs/2410.09174)]

- "Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL" [2024-10] [[paper](https://arxiv.org/abs/2410.11371)]

- "LR-SQL: A Supervised Fine-Tuning Method for Text2SQL Tasks under Low-Resource Scenarios" [2024-10] [[paper](https://arxiv.org/abs/2410.11457)]

- "MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation" [2024-10] [[paper](https://arxiv.org/abs/2410.12916)]

- "Learning Metadata-Agnostic Representations for Text-to-SQL In-Context Example Selection" [2024-10] [[paper](https://arxiv.org/abs/2410.14049)]

- "An Actor-Critic Approach to Boosting Text-to-SQL Large Language Model" [2024-10] [[paper](https://arxiv.org/abs/2410.22082)]

- "RSL-SQL: Robust Schema Linking in Text-to-SQL Generation" [2024-10] [[paper](https://arxiv.org/abs/2411.00073)]

- "KeyInst: Keyword Instruction for Improving SQL Formulation in Text-to-SQL" [2024-10] [[paper](https://arxiv.org/abs/2411.00788)]

- "Grounding Natural Language to SQL Translation with Data-Based Self-Explanations" [2024-11] [[paper](https://arxiv.org/abs/2411.02948)]

- "PDC & DM-SFT: A Road for LLM SQL Bug-Fix Enhancing" [2024-11] [[paper](https://arxiv.org/abs/2411.06767)]

- "XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL" [2024-11] [[paper](https://arxiv.org/abs/2411.08599)]

### Program Proof

- "Baldur: Whole-Proof Generation and Repair with Large Language Models" [2023-03] [FSE 2023] [[paper](https://arxiv.org/abs/2303.04910)]

- "An In-Context Learning Agent for Formal Theorem-Proving" [2023-10] [[paper](https://arxiv.org/abs/2310.04353)]

- "Towards AI-Assisted Synthesis of Verified Dafny Methods" [2024-02] [FSE 2024] [[paper](https://arxiv.org/abs/2402.00247)]

- "Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming" [2024-05] [[paper](https://arxiv.org/abs/2405.01787)]

- "Laurel: Generating Dafny Assertions Using Large Language Models" [2024-05] [[paper](https://arxiv.org/abs/2405.16792)]

- "AutoVerus: Automated Proof Generation for Rust Code" [2024-09] [[paper](https://arxiv.org/abs/2409.13082)]

- "Proof Automation with Large Language Models" [2024-09] [[paper](https://arxiv.org/abs/2409.14274)]

- "Automated Proof Generation for Rust Code via Self-Evolution" [2024-10] [[paper](https://arxiv.org/abs/2410.15756)]

- "CoqPilot, a plugin for LLM-based generation of proofs" [2024-10] [[paper](https://arxiv.org/abs/2410.19605)]

### Test Generation

- "Unit Test Case Generation with Transformers and Focal Context" [2020-09] [AST@ICSE 2022] [[paper](https://arxiv.org/abs/2009.05617)]

- "An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation" [2023-02] [IEEE TSE] [[paper](https://arxiv.org/abs/2302.06527)]

- "A3Test: Assertion-Augmented Automated Test Case Generation" [2023-02] [[paper](https://arxiv.org/abs/2302.10352)]

- "Learning Deep Semantics for Test Completion" [2023-02] [ICSE 2023] [[paper](https://arxiv.org/abs/2302.10166)]

- "Using Large Language Models to Generate JUnit Tests: An Empirical Study" [2023-04] [EASE 2024] [[paper](https://arxiv.org/abs/2305.00418)]

- "CodaMosa: Escaping Coverage Plateaus in Test Generation with Pre-Trained Large Language Models" [2023-05] [ICSE 2023] [[paper](https://dl.acm.org/doi/abs/10.1109/ICSE48619.2023.00085)]

- "No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation" [2023-05] [[paper](https://arxiv.org/abs/2305.04207)]

- "ChatUniTest: a ChatGPT-based automated unit test generation tool" [2023-05] [[paper](https://arxiv.org/abs/2305.04764)]

- "ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation" [2023-07] [[paper](https://arxiv.org/abs/2307.00588)]

- "Can Large Language Models Write Good Property-Based Tests?" [2023-07] [[paper](https://arxiv.org/abs/2307.04346)]

- "Domain Adaptation for Deep Unit Test Case Generation" [2023-08] [[paper](https://arxiv.org/abs/2308.08033)]

- "Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing" [2023-08] [[paper](https://arxiv.org/abs/2308.16557)]

- "How well does LLM generate security tests?" [2023-10] [[paper](https://arxiv.org/abs/2310.00710)]

- "Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation" [2023-10] [[paper](https://arxiv.org/abs/2310.02368)]

- "An initial investigation of ChatGPT unit test generation capability" [2023-10] [SAST 2023] [[paper](https://dl.acm.org/doi/abs/10.1145/3624032.3624035)]

- "CoverUp: Coverage-Guided LLM-Based Test Generation" [2024-03] [[paper](https://arxiv.org/abs/2403.16218)]

- "Enhancing LLM-based Test Generation for Hard-to-Cover Branches via Program Analysis" [2024-04] [[paper](https://arxiv.org/abs/2404.04966)]

- "Large Language Models for Mobile GUI Text Input Generation: An Empirical Study" [2024-04] [[paper](https://arxiv.org/abs/2404.08948)]

- "Test Code Generation for Telecom Software Systems using Two-Stage Generative Model" [2024-04] [[paper](https://arxiv.org/abs/2404.09249)]

- "LLM-Powered Test Case Generation for Detecting Tricky Bugs" [2024-04] [[paper](https://arxiv.org/abs/2404.10304)]

- "Generating Test Scenarios from NL Requirements using Retrieval-Augmented LLMs: An Industrial Study" [2024-04] [[paper](https://arxiv.org/abs/2404.12772)]

- "Large Language Models as Test Case Generators: Performance Evaluation and Enhancement" [2024-04] [[paper](https://arxiv.org/abs/2404.13340)]

- "Leveraging Large Language Models for Automated Web-Form-Test Generation: An Empirical Study" [2024-05] [[paper](https://arxiv.org/abs/2405.09965)]

- "DLLens: Testing Deep Learning Libraries via LLM-aided Synthesis" [2024-06] [[paper](https://arxiv.org/abs/2406.07944)]

- "Exploring Fuzzing as Data Augmentation for Neural Test Generation" [2024-06] [[paper](https://arxiv.org/abs/2406.08665)]

- "Mokav: Execution-driven Differential Testing with LLMs" [2024-06] [[paper](https://arxiv.org/abs/2406.10375)]

- "Code Agents are State of the Art Software Testers" [2024-06] [[paper](https://arxiv.org/abs/2406.12952)]

- "CasModaTest: A Cascaded and Model-agnostic Self-directed Framework for Unit Test Generation" [2024-06] [[paper](https://arxiv.org/abs/2406.15743)]

- "An Empirical Study of Unit Test Generation with Large Language Models" [2024-06] [[paper](https://arxiv.org/abs/2406.18181)]

- "Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation" [2024-06] [[paper](https://arxiv.org/abs/2407.00225)]

- "Augmenting LLMs to Repair Obsolete Test Cases with Static Collector and Neural Reranker" [2024-07] [[paper](https://arxiv.org/abs/2407.03625)]

- "Harnessing the Power of LLMs: Automating Unit Test Generation for High-Performance Computing" [2024-07] [[paper](https://arxiv.org/abs/2407.05202)]

- "An LLM-based Readability Measurement for Unit Tests' Context-aware Inputs" [2024-07] [[paper](https://arxiv.org/abs/2407.21369)]

- "A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suites" [2024-08] [[paper](https://arxiv.org/abs/2408.07846)]

- "Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests" [2024-08] [[paper](https://arxiv.org/abs/2408.11710)]

- "Multi-language Unit Test Generation using LLMs" [2024-09] [[paper](https://arxiv.org/abs/2409.03093)]

- "Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes" [2024-09] [[paper](https://arxiv.org/abs/2409.06416)]

- "Python Symbolic Execution with LLM-powered Code Generation" [2024-09] [[paper](https://arxiv.org/abs/2409.09271)]

- "Rethinking the Influence of Source Code on Test Case Generation" [2024-09] [[paper](https://arxiv.org/abs/2409.09464)]

- "On the Effectiveness of LLMs for Manual Test Verifications" [2024-09] [[paper](https://arxiv.org/abs/2409.12405)]

- "Retrieval-Augmented Test Generation: How Far Are We?" [2024-09] [[paper](https://arxiv.org/abs/2409.12682)]

- "Context-Enhanced LLM-Based Framework for Automatic Test Refactoring" [2024-09] [[paper](https://arxiv.org/abs/2409.16739)]

- "TestBench: Evaluating Class-Level Test Case Generation Capability of Large Language Models" [2024-09] [[paper](https://arxiv.org/abs/2409.17561)]

- "Advancing Bug Detection in Fastjson2 with Large Language Models Driven Unit Test Generation" [2024-10] [[paper](https://arxiv.org/abs/2410.09414)]

- "Test smells in LLM-Generated Unit Tests" [2024-10] [[paper](https://arxiv.org/abs/2410.10628)]

- "LLM-based Unit Test Generation via Property Retrieval" [2024-10] [[paper](https://arxiv.org/abs/2410.13542)]

- "Disrupting Test Development with AI Assistants" [2024-11] [[paper](https://arxiv.org/abs/2411.02328)]

- "Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study" [2024-11] [[paper](https://arxiv.org/abs/2411.02462)]

- "VALTEST: Automated Validation of Language Model Generated Test Cases" [2024-11] [[paper](https://arxiv.org/abs/2411.08254)]

### Oracle Generation

- "Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers" [2020-09] [[paper](https://arxiv.org/abs/2009.05634)]

- "TOGA: A Neural Method for Test Oracle Generation" [2021-09] [ICSE 2022] [[paper](https://arxiv.org/abs/2109.09262)]

- "TOGLL: Correct and Strong Test Oracle Generation with LLMs" [2024-05] [[paper](https://arxiv.org/abs/2405.03786)]

- "Test Oracle Automation in the era of LLMs" [2024-05] [[paper](https://arxiv.org/abs/2405.12766)]

- "Beyond Code Generation: Assessing Code LLM Maturity with Postconditions" [2024-07] [[paper](https://arxiv.org/abs/2407.14118)]

- "Chat-like Asserts Prediction with the Support of Large Language Model" [2024-07] [[paper](https://arxiv.org/abs/2407.21429)]

- "Do LLMs generate test oracles that capture the actual or the expected program behaviour?" [2024-10] [[paper](https://arxiv.org/abs/2410.21136)]

- "Generating executable oracles to check conformance of client code to requirements of JDK Javadocs using LLMs" [2024-11] [[paper](https://arxiv.org/abs/2411.01789)]

- "Automatically Write Code Checker: An LLM-based Approach with Logic-guided API Retrieval and Case by Case Iteration" [2024-11] [[paper](https://arxiv.org/abs/2411.06796)]

### Mutation Testing

- "μBERT: Mutation Testing using Pre-Trained Language Models" [2022-03] [[paper](https://arxiv.org/abs/2203.03289)]

- "Efficient Mutation Testing via Pre-Trained Language Models" [2023-01] [[paper](https://arxiv.org/abs/2301.03543)]

- "LLMorpheus: Mutation Testing using Large Language Models" [2024-04] [[paper](https://arxiv.org/abs/2404.09952)]

- "An Exploratory Study on Using Large Language Models for Mutation Testing" [2024-06] [[paper](https://arxiv.org/abs/2406.09843)]

- "Fine-Tuning LLMs for Code Mutation: A New Era of Cyber Threats" [2024-10] [[paper](https://arxiv.org/abs/2410.22293)]

### Fuzz Testing

- "Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models" [2022-12] [[paper](https://arxiv.org/abs/2212.14834)]

- "Fuzz4All: Universal Fuzzing with Large Language Models" [2023-08] [[paper](https://arxiv.org/abs/2308.04748)]

- "WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models" [2023-10] [[paper](https://arxiv.org/abs/2310.15991)]

- "LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing" [2024-06] [[paper](https://arxiv.org/abs/2406.07714)]

- "FuzzCoder: Byte-level Fuzzing Test via Large Language Model" [2024-09] [[paper](https://arxiv.org/abs/2409.01944)]

- "ISC4DGF: Enhancing Directed Grey-box Fuzzing with LLM-Driven Initial Seed Corpus Generation" [2024-09] [[paper](https://arxiv.org/abs/2409.14329)]

- "Large Language Models Based JSON Parser Fuzzing for Bug Discovery and Behavioral Analysis" [2024-10] [[paper](https://arxiv.org/abs/2410.21806)]

- "Fixing Security Vulnerabilities with AI in OSS-Fuzz" [2024-11] [[paper](https://arxiv.org/abs/2411.03346)]

### Vulnerability Detection

- "VulDeePecker: A Deep Learning-Based System for Vulnerability Detection" [2018-01] [NDSS 2018] [[paper](https://arxiv.org/abs/1801.01681)]

- "DeepBugs: A Learning Approach to Name-based Bug Detection" [2018-04] [Proc. ACM Program. Lang.] [[paper](https://arxiv.org/abs/1805.11683)]

- "Automated Vulnerability Detection in Source Code Using Deep Representation Learning" [2018-07] [ICMLA 2018] [[paper](https://arxiv.org/abs/1807.04320)]

- "SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities" [2018-07] [IEEE TDSC] [[paper](https://arxiv.org/abs/1807.06756)]

- "Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks" [2019-09] [NeurIPS 2019] [[paper](https://arxiv.org/abs/1909.03496)]

- "Improving bug detection via context-based code representation learning and attention-based neural networks" [2019-10] [Proc. ACM Program. Lang.] [[paper](https://dl.acm.org/doi/10.1145/3360588)]

- "Global Relational Models of Source Code" [2019-12] [ICLR 2020] [[paper](https://openreview.net/forum?id=B1lnbRNtwr)]

- "VulDeeLocator: A Deep Learning-based Fine-grained Vulnerability Detector" [2020-01] [IEEE TDSC] [[paper](https://arxiv.org/abs/2001.02350)]

- "Deep Learning based Vulnerability Detection: Are We There Yet?" [2020-09] [IEEE TSE] [[paper](https://arxiv.org/abs/2009.07235)]

- "Security Vulnerability Detection Using Deep Learning Natural Language Processing" [2021-05] [INFOCOM Workshops 2021] [[paper](https://arxiv.org/abs/2105.02388)]

- "Self-Supervised Bug Detection and Repair" [2021-05] [NeurIPS 2021] [[paper](https://arxiv.org/abs/2105.12787)]

- "Vulnerability Detection with Fine-grained Interpretations" [2021-06] [ESEC/SIGSOFT FSE 2021] [[paper](https://arxiv.org/abs/2106.10478)]

- "ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection" [2021-10] [ICSE Companion 2022] [[paper](https://arxiv.org/abs/2110.07317)]

- "VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python" [2022-01] [Inf. Softw. Technol] [[paper](https://arxiv.org/abs/2201.08441)]

- "Transformer-Based Language Models for Software Vulnerability Detection" [222-04] [ACSAC 2022] [[paper](https://arxiv.org/abs/2204.03214)]

- "LineVul: A Transformer-based Line-Level Vulnerability Prediction" [2022-05] [MSR 2022] [[paper](https://ieeexplore.ieee.org/document/9796256)]

- "VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection" [2022-05] [IJCNN 2022] [[paper](https://arxiv.org/abs/2205.12424)]

- "Open Science in Software Engineering: A Study on Deep Learning-Based Vulnerability Detection" [2022-09] [IEEE TSE] [[paper](https://ieeexplore.ieee.org/document/9894099)]

- "An Empirical Study of Deep Learning Models for Vulnerability Detection" [2022-12] [ICSE 2023] [[paper](https://arxiv.org/abs/2212.08109)]

- "CSGVD: A deep learning approach combining sequence and graph embedding for source code vulnerability detection" [2023-01] [J. Syst. Softw.] [[paper](https://www.sciencedirect.com/science/article/pii/S0164121223000183)]

- "Benchmarking Software Vulnerability Detection Techniques: A Survey" [2023-03] [[paper](https://arxiv.org/abs/2303.16362)]

- "Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning?" [2023-05] [[paper](https://arxiv.org/abs/2306.01754)]

- "A Survey on Automated Software Vulnerability Detection Using Machine Learning and Deep Learning" [2023-06] [[paper](https://arxiv.org/abs/2306.11673)]

- "Limits of Machine Learning for Automatic Vulnerability Detection" [2023-06] [[paper](https://arxiv.org/abs/2306.17193)]

- "Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation" [2023-08] [[paper](https://arxiv.org/abs/2308.01240)]

- "Prompt-Enhanced Software Vulnerability Detection Using ChatGPT" [2023-08] [[paper](https://arxiv.org/abs/2308.12697)]

- "Towards Causal Deep Learning for Vulnerability Detection" [2023-10] [[paper](https://arxiv.org/abs/2310.07958)]

- "Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities" [2023-11] [[paper](https://arxiv.org/abs/2311.16169)]

- "How Far Have We Gone in Vulnerability Detection Using Large Language Models" [2023-11] [[paper](https://arxiv.org/abs/2311.12420)]

- "Can Large Language Models Identify And Reason About Security Vulnerabilities? Not Yet" [2023-12] [[paper](https://arxiv.org/abs/2312.12575)]

- "LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning" [2024-01] [[paper](https://arxiv.org/abs/2401.16185)]

- "Security Code Review by LLMs: A Deep Dive into Responses" [2024-01] [[paper](https://arxiv.org/abs/2401.16310)]

- "Chain-of-Thought Prompting of Large Language Models for Discovering and Fixing Software Vulnerabilities" [2024-02] [[paper](https://arxiv.org/abs/2402.17230)]

- "Multi-role Consensus through LLMs Discussions for Vulnerability Detection" [2024-03] [[paper](https://arxiv.org/abs/2403.14274)]

- "A Comprehensive Study of the Capabilities of Large Language Models for Vulnerability Detection" [2024-03] [[paper](https://arxiv.org/abs/2403.17218)]

- "Vulnerability Detection with Code Language Models: How Far Are We?" [2024-03] [[paper](https://arxiv.org/abs/2403.18624)]

- "Multitask-based Evaluation of Open-Source LLM on Software Vulnerability" [2024-04] [[paper](https://arxiv.org/abs/2404.02056)]

- "Large Language Model for Vulnerability Detection and Repair: Literature Review and Roadmap" [2024-04] [[paper](https://arxiv.org/abs/2404.02525)]

- "Pros and Cons! Evaluating ChatGPT on Software Vulnerability" [2024-04] [[paper](https://arxiv.org/abs/2404.03994)]

- "VulEval: Towards Repository-Level Evaluation of Software Vulnerability Detection" [2024-04] [[paper](https://arxiv.org/abs/2404.15596)]

- "DLAP: A Deep Learning Augmented Large Language Model Prompting Framework for Software Vulnerability Detection" [2024-05] [[paper](https://arxiv.org/abs/2405.01202)]

- "Bridging the Gap: A Study of AI-based Vulnerability Management between Industry and Academia" [2024-05] [[paper](https://arxiv.org/abs/2405.02435)]

- "Bridge and Hint: Extending Pre-trained Language Models for Long-Range Code" [2024-05] [[paper](https://arxiv.org/abs/2405.11233)]

- "Harnessing Large Language Models for Software Vulnerability Detection: A Comprehensive Benchmarking Study" [2024-05] [[paper](https://arxiv.org/abs/2405.15614)]

- "LLM-Assisted Static Analysis for Detecting Security Vulnerabilities" [2024-05] [[paper](https://arxiv.org/abs/2405.17238)]

- "Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning" [2024-06] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2406.03718)]

- "Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models" [2024-06] [[paper](https://arxiv.org/abs/2406.05892)]

- "M2CVD: Multi-Model Collaboration for Code Vulnerability Detection" [2024-06] [[paper](https://arxiv.org/abs/2406.05940)]

- "Towards Effectively Detecting and Explaining Vulnerabilities Using Large Language Models" [2024-06] [[paper](https://arxiv.org/abs/2406.09701)]

- "Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG" [2024-06] [[paper](https://arxiv.org/abs/2406.11147)]

- "Bug In the Code Stack: Can LLMs Find Bugs in Large Python Code Stacks" [2024-06] [[paper](https://arxiv.org/abs/2406.15325)]

- "Supporting Cross-language Cross-project Bug Localization Using Pre-trained Language Models" [2024-07] [[paper](https://arxiv.org/abs/2407.02732)]

- "ALPINE: An adaptive language-agnostic pruning method for language models for code" [2024-07] [[paper](https://arxiv.org/abs/2407.04147)]

- "SCoPE: Evaluating LLMs for Software Vulnerability Detection" [2024-07] [[paper](https://arxiv.org/abs/2407.14372)]

- "Comparison of Static Application Security Testing Tools and Large Language Models for Repo-level Vulnerability Detection" [2024-07] [[paper](https://arxiv.org/abs/2407.16235)]

- "Code Structure-Aware through Line-level Semantic Learning for Code Vulnerability Detection" [2024-07] [[paper](https://arxiv.org/abs/2407.18877)]

- "A Study of Using Multimodal LLMs for Non-Crash Functional Bug Detection in Android Apps" [2024-07] [[paper](https://arxiv.org/abs/2407.19053)]

- "EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection" [2024-07] [[paper](https://arxiv.org/abs/2407.19216)]

- "Evaluating Large Language Models in Detecting Test Smells" [2024-07] [[paper](https://arxiv.org/abs/2407.19261)]

- "Automated Software Vulnerability Static Code Analysis Using Generative Pre-Trained Transformer Models" [2024-07] [[paper](https://arxiv.org/abs/2408.00197)]

- "A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality" [2024-08] [[paper](https://arxiv.org/abs/2408.00435)]

- "Large Language Models for Secure Code Assessment: A Multi-Language Empirical Study" [2024-08] [[paper](https://arxiv.org/abs/2408.06428)]

- "VulCatch: Enhancing Binary Vulnerability Detection through CodeT5 Decompilation and KAN Advanced Feature Extraction" [2024-08] [[paper](https://arxiv.org/abs/2408.07181)]

- "Impact of Large Language Models of Code on Fault Localization" [2024-08] [[paper](https://arxiv.org/abs/2408.09657)]

- "Better Debugging: Combining Static Analysis and LLMs for Explainable Crashing Fault Localization" [2024-08] [[paper](https://arxiv.org/abs/2408.12070)]

- "Beyond ChatGPT: Enhancing Software Quality Assurance Tasks with Diverse LLMs and Validation Techniques" [2024-09] [[paper](https://arxiv.org/abs/2409.01001)]

- "CLNX: Bridging Code and Natural Language for C/C++ Vulnerability-Contributing Commits Identification" [2024-09] [[paper](https://arxiv.org/abs/2409.07407)]

- "Code Vulnerability Detection: A Comparative Analysis of Emerging Large Language Models" [2024-09] [[paper](https://arxiv.org/abs/2409.10490)]

- "Program Slicing in the Era of Large Language Models" [2024-09] [[paper](https://arxiv.org/abs/2409.12369)]

- "Generating API Parameter Security Rules with LLM for API Misuse Detection" [2024-09] [[paper](https://arxiv.org/abs/2409.09288)]

- "Enhancing Fault Localization Through Ordered Code Analysis with LLM Agents and Self-Reflection" [2024-09] [[paper](https://arxiv.org/abs/2409.13642)]

- "Comparing Unidirectional, Bidirectional, and Word2vec Models for Discovering Vulnerabilities in Compiled Lifted Code" [2024-09] [[paper](https://arxiv.org/abs/2409.17513)]

- "Enhancing Pre-Trained Language Models for Vulnerability Detection via Semantic-Preserving Data Augmentation" [2024-10] [[paper](https://arxiv.org/abs/2410.00249)]

- "StagedVulBERT: Multi-Granular Vulnerability Detection with a Novel Pre-trained Code Model" [2024-10] [[paper](https://arxiv.org/abs/2410.05766)]

- "Understanding the AI-powered Binary Code Similarity Detection" [2024-10] [[paper](https://arxiv.org/abs/2410.07537)]

- "RealVul: Can We Detect Vulnerabilities in Web Applications with LLM?" [2024-10] [[paper](https://arxiv.org/abs/2410.07573)]

- "Just-In-Time Software Defect Prediction via Bi-modal Change Representation Learning" [2024-10] [[paper](https://arxiv.org/abs/2410.12107)]

- "DFEPT: Data Flow Embedding for Enhancing Pre-Trained Model Based Vulnerability Detection" [2024-10] [[paper](https://arxiv.org/abs/2410.18479)]

- "Utilizing Precise and Complete Code Context to Guide LLM in Automatic False Positive Mitigation" [2024-11] [[paper](https://arxiv.org/abs/2411.03079)]

- "Smart-LLaMA: Two-Stage Post-Training of Large Language Models for Smart Contract Vulnerability Detection and Explanation" [2024-11] [[paper](https://arxiv.org/abs/2411.06221)]

### Malicious Code Detection

- "Deep Android Malware Detection", 2017-03, CODASPY 2017, [[paper](https://dl.acm.org/doi/10.1145/3029806.3029823)]

- "A Multimodal Deep Learning Method for Android Malware Detection Using Various Features", 2018-08, IEEE Trans. Inf. Forensics Secur. 2019, [[paper](https://ieeexplore.ieee.org/document/8443370)]

- "Portable, Data-Driven Malware Detection using Language Processing and Machine Learning Techniques on Behavioral Analysis Reports", 2018-12, Digit. Investig. 2019, [[paper](https://arxiv.org/abs/1812.10327)]

- "I-MAD: Interpretable Malware Detector Using Galaxy Transformer", 2019-09, Comput. Secur. 2021, [[paper](https://arxiv.org/abs/1909.06865)]

- "Droidetec: Android Malware Detection and Malicious Code Localization through Deep Learning", 2020-02, [[paper](https://arxiv.org/abs/2002.03594)]

- "Malicious Code Detection: Run Trace Output Analysis by LSTM", 2021-01, IEEE Access 2021, [[paper](https://arxiv.org/abs/2101.05646)]

- "Intelligent malware detection based on graph convolutional network", 2021-08, J. Supercomput. 2021, [[paper](https://link.springer.com/article/10.1007/s11227-021-04020-y)]

- "Malbert: A novel pre-training method for malware detection", 2021-09, Comput. Secur. 2021, [[paper](https://www.sciencedirect.com/science/article/pii/S0167404821002820)]

- "Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach", 2021-12, ISI 2021, [[paper](https://arxiv.org/abs/2112.01724)]

- "M2VMapper: Malware-to-Vulnerability mapping for Android using text processing", 2021-12, Expert Syst. Appl. 2022, [[paper](https://www.sciencedirect.com/science/article/pii/S0957417421016572)]

- "Malware Detection and Prevention using Artificial Intelligence Techniques", 2021-12, IEEE BigData 2021, [[paper](https://arxiv.org/abs/2206.12770)]

- "An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification", 2021-12, Comput. Secur. 2022, [[paper](https://arxiv.org/abs/2112.13236)]

- "EfficientNet convolutional neural networks-based Android malware detection", 2022-01, Comput. Secur. 2022, [[paper](https://www.sciencedirect.com/science/article/pii/S0167404822000219)]

- "Static Malware Detection Using Stacked BiLSTM and GPT-2", 2022-05, IEEE Access 2022, [[paper](https://ieeexplore.ieee.org/document/9785789)]

- "APT Malicious Sample Organization Traceability Based on Text Transformer Model", 2022-07, PRML 2022, [[paper](https://ieeexplore.ieee.org/document/9882232)]

- "Self-Supervised Vision Transformers for Malware Detection", 2022-08, IEEE Access 2022, [[paper](https://arxiv.org/abs/2208.07049)]

- "A Survey of Recent Advances in Deep Learning Models for Detecting Malware in Desktop and Mobile Platforms", 2022-09, ACM Computing Surveys, [[paper](https://dl.acm.org/doi/abs/10.1145/3638240)]

- "Malicious Source Code Detection Using Transformer", 2022-09, [[paper](https://arxiv.org/abs/2209.07957)]

- "Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor", 2022-10, CyberC 2022, [[paper](https://arxiv.org/abs/2210.14225)]

- "MalBERTv2: Code Aware BERT-Based Model for Malware Identification" [2023-03] [Big Data Cogn. Comput. 2023] [[paper](https://www.mdpi.com/2504-2289/7/2/60)]

- "GPThreats-3: Is Automatic Malware Generation a Threat?" [2023-05] [SPW 2023] [[paper](https://ieeexplore.ieee.org/abstract/document/10188649)]

- "GitHub Copilot: A Threat to High School Security? Exploring GitHub Copilot's Proficiency in Generating Malware from Simple User Prompts" [2023-08] [ETNCC 2023] [[paper](https://ieeexplore.ieee.org/abstract/document/10284976)]

- "An Attacker’s Dream? Exploring the Capabilities of ChatGPT for Developing Malware" [2023-08] [CSET 2023] [[paper](https://dl.acm.org/doi/abs/10.1145/3607505.3607513)]

- "Malicious code detection in android: the role of sequence characteristics and disassembling methods" [2023-12] [Int. J. Inf. Sec. 2023] [[paper](https://arxiv.org/abs/2312.01113)]

- "Prompt Engineering-assisted Malware Dynamic Analysis Using GPT-4" [2023-12] [[paper](https://arxiv.org/abs/2312.08317)]

- "Shifting the Lens: Detecting Malware in npm Ecosystem with Large Language Models" [2024-03] [[paper](https://arxiv.org/abs/2403.12196)]

- "AppPoet: Large Language Model based Android malware detection via multi-view prompt engineering" [2024-04] [[paper](https://arxiv.org/abs/2404.18816)]

- "Tactics, Techniques, and Procedures (TTPs) in Interpreted Malware: A Zero-Shot Generation with Large Language Models" [2024-07] [[paper](https://arxiv.org/abs/2407.08532)]

- "DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware" [2024-08] [[paper](https://arxiv.org/abs/2408.16353)]

- "PackageIntel: Leveraging Large Language Models for Automated Intelligence Extraction in Package Ecosystems" [2024-09] [[paper](https://arxiv.org/abs/2409.15049)]

### Compiler Optimization

- "Learning Performance-Improving Code Edits" [2023-06] [ICLR 2024 Spotlight] [[paper](https://arxiv.org/abs/2302.07867)]

- "Large Language Models for Compiler Optimization" [2023-09] [[paper](https://arxiv.org/abs/2309.07062)]

- "Refining Decompiled C Code with Large Language Models" [2023-10] [[paper](https://arxiv.org/abs/2310.06530)]

- "Priority Sampling of Large Language Models for Compilers" [2024-02] [[paper](https://arxiv.org/abs/2402.18734)]

- "Should AI Optimize Your Code? A Comparative Study of Current Large Language Models Versus Classical Optimizing Compilers" [2024-06] [[paper](https://arxiv.org/abs/2406.12146)]

- "Iterative or Innovative? A Problem-Oriented Perspective for Code Optimization" [2024-06] [[paper](https://arxiv.org/abs/2406.11935)]

- "Meta Large Language Model Compiler: Foundation Models of Compiler Optimization" [2024-06] [[paper](https://arxiv.org/abs/2407.02524)]

- "ViC: Virtual Compiler Is All You Need For Assembly Code Search" [2024-08] [[paper](https://arxiv.org/abs/2408.06385)]

- "Search-Based LLMs for Code Optimization" [2024-08] [[paper](https://arxiv.org/abs/2408.12159)]

- "E-code: Mastering Efficient Code Generation through Pretrained Models and Expert Encoder Group" [2024-08] [[paper](https://arxiv.org/abs/2408.12948)]

- "Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions" [2024-10] [[paper](https://arxiv.org/abs/2410.09241)]

### Decompilation

- "Using recurrent neural networks for decompilation" [2018-03] [SANER 2018] [[paper](https://ieeexplore.ieee.org/document/8330222)]

- "Evolving Exact Decompilation" [2018] [[paper](https://eschulte.github.io/data/bed.pdf)]

- "Towards Neural Decompilation" [2019-05] [[paper](https://arxiv.org/abs/1905.08325)]

- "Coda: An End-to-End Neural Program Decompiler" [2019-06] [NeurIPS 2019] [[paper](https://arxiv.org/abs/1906.12029)]

- "N-Bref : A High-fidelity Decompiler Exploiting Programming Structures" [2020-09] [[paper](https://openreview.net/forum?id=6GkL6qM3LV)]

- "Neutron: an attention-based neural decompiler" [2021-03] [Cybersecurity 2021] [[paper](https://cybersecurity.springeropen.com/articles/10.1186/s42400-021-00070-0)]

- "Beyond the C: Retargetable Decompilation using Neural Machine Translation" [2022-12] [[paper](https://arxiv.org/abs/2212.08950)]

- "Boosting Neural Networks to Decompile Optimized Binaries" [2023-01] [ACSAC 2022] [[paper](https://arxiv.org/abs/2301.00969)]

- "SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly" [2023-05] [[paper](https://arxiv.org/abs/2305.12520)]

- "Nova+: Generative Language Models for Binaries" [2023-11] [[paper](https://arxiv.org/abs/2311.13721)]

- "LLM4Decompile: Decompiling Binary Code with Large Language Models" [2024-03] [[paper](https://arxiv.org/abs/2403.05286)]

- "WaDec: Decompile WebAssembly Using Large Language Model" [2024-06] [[paper](https://arxiv.org/abs/2406.11346)]

- "MAD: Move AI Decompiler to Improve Transparency and Auditability on Non-Open-Source Blockchain Smart Contract" [2024-10] [[paper](https://arxiv.org/abs/2410.15275)]

### Commit Message Generation

- "Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond" [2024-04] [[paper](https://arxiv.org/abs/2404.14824)]

- "Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settings" [2024-10] [[paper](https://arxiv.org/abs/2410.12046)]

### Code Review

- "Using Pre-Trained Models to Boost Code Review Automation" [2022-01] [ICSE 2022] [[paper](https://arxiv.org/abs/2201.06850)]

- "AUGER: Automatically Generating Review Comments with Pre-training Models" [2022-08] [ESEC/FSE 2022] [[paper](https://arxiv.org/abs/2208.08014)]

- "Automatic Code Review by Learning the Structure Information of Code Graph" [2023-02] [Sensors] [[paper](https://www.mdpi.com/1424-8220/23/5/2551)]

- "LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning" [2023-08] [ISSRE 2023] [[paper](https://arxiv.org/abs/2308.11148)]

- "AI-powered Code Review with LLMs: Early Results" [2024-04] [[paper](https://arxiv.org/abs/2404.18496)]

- "AI-Assisted Assessment of Coding Practices in Modern Code Review" [2024-05] [[paper](https://arxiv.org/abs/2405.13565)]

- "A GPT-based Code Review System for Programming Language Learning" [2024-07] [[paper](https://arxiv.org/abs/2407.04722)]

- "LLM Critics Help Catch LLM Bugs" [2024-06] [[paper](https://arxiv.org/abs/2407.00215)]

- "Exploring the Capabilities of LLMs for Code Change Related Tasks" [2024-07] [[paper](https://arxiv.org/abs/2407.02824)]

- "Evaluating Language Models for Generating and Judging Programming Feedback" [2024-07] [[paper](https://arxiv.org/abs/2407.04873)]

- "Can LLMs Replace Manual Annotation of Software Engineering Artifacts?" [2024-08] [[paper](https://arxiv.org/abs/2408.05534)]

- "Leveraging Reviewer Experience in Code Review Comment Generation" [2024-09] [[paper](https://arxiv.org/abs/2409.10959)]

- "CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells" [2024-09] [[paper](https://arxiv.org/abs/2409.19801)]

- "Enhancing Code Annotation Reliability: Generative AI's Role in Comment Quality Assessment Models" [2024-10] [[paper](https://arxiv.org/abs/2410.22323)]

- "Knowledge-Guided Prompt Learning for Request Quality Assurance in Public Code Review" [2024-10] [[paper](https://arxiv.org/abs/2410.21673)]

- "Impact of LLM-based Review Comment Generation in Practice: A Mixed Open-/Closed-source User Study" [2024-11] [[paper](https://arxiv.org/abs/2411.07091)]

### Log Analysis

- "LogStamp: Automatic Online Log Parsing Based on Sequence Labelling" [2022-08] [[paper](https://arxiv.org/abs/2208.10282)]

- "Log Parsing with Prompt-based Few-shot Learning" [2023-02] [ICSE 2023] [[paper](https://arxiv.org/abs/2302.07435)]

- "Log Parsing: How Far Can ChatGPT Go?" [2023-06] [ASE 2023] [[paper](https://arxiv.org/abs/2306.01590)]

- "LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log Analysis" [2023-08] [[paper](https://arxiv.org/abs/2308.07610)]

- "LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection" [2023-09] [[paper](https://arxiv.org/abs/2309.01189)]

- "An Assessment of ChatGPT on Log Data" [2023-09] [[paper](https://arxiv.org/abs/2309.07938)]

- "LILAC: Log Parsing using LLMs with Adaptive Parsing Cache" [2023-10] [[paper](https://arxiv.org/abs/2310.01796)]

- "LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing" [2024-04] [[paper](https://arxiv.org/abs/2404.18001)]

- "On the Influence of Data Resampling for Deep Learning-Based Log Anomaly Detection: Insights and Recommendations" [2024-05] [[paper](https://arxiv.org/abs/2405.03489)]

- "Log Parsing with Self-Generated In-Context Learning and Self-Correction" [2024-06] [[paper](https://arxiv.org/abs/2406.03376)]

- "Stronger, Faster, and Cheaper Log Parsing with LLMs" [2024-06] [[paper](https://arxiv.org/abs/2406.06156)]

- "ULog: Unsupervised Log Parsing with Large Language Models through Log Contrastive Units" [2024-06] [[paper](https://arxiv.org/abs/2406.07174)]

- "Anomaly Detection on Unstable Logs with GPT Models" [2024-06] [[paper](https://arxiv.org/abs/2406.07467)]

- "LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models" [2024-08] [KDD 2024] [[paper](https://arxiv.org/abs/2408.13727)]

- "LUK: Empowering Log Understanding with Expert Knowledge from Large Language Models" [2024-09] [[paper](https://arxiv.org/abs/2409.01909)]

- "A Comparative Study on Large Language Models for Log Parsing" [2024-09] [[paper](https://arxiv.org/abs/2409.02474)]

- "What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach" [2024-10] [[paper](https://arxiv.org/abs/2409.20503)]

- "LogLM: From Task-based to Instruction-based Automated Log Analysis" [2024-10] [[paper](https://arxiv.org/abs/2410.09352)]

### Software Configuration

- "Configuration Validation with Large Language Models" [2023-10] [[paper](https://arxiv.org/abs/2310.09690)]

- "CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation" [2023-11] [[paper](https://arxiv.org/abs/2401.06786)]

- "Can LLMs Configure Software Tools" [2023-12] [[paper](https://arxiv.org/abs/2312.06121)]

- "LuaTaint: A Static Analysis System for Web Configuration Interface Vulnerability of Internet of Things Devices" [2024-02] [IOT] [[paper](https://arxiv.org/abs/2402.16043)]

- "LLM-Based Misconfiguration Detection for AWS Serverless Computing" [2024-11] [[paper](https://arxiv.org/abs/2411.00642)]

- "LogLLM: Log-based Anomaly Detection Using Large Language Models" [2024-11] [[paper](https://arxiv.org/abs/2411.08561)]

### Software Modeling

- "Towards using Few-Shot Prompt Learning for Automating Model Completion" [2022-12] [[paper](https://arxiv.org/abs/2212.03404)]

- "Model Generation from Requirements with LLMs: an Exploratory Study" [2024-04] [[paper](https://arxiv.org/abs/2404.06371)]

- "How LLMs Aid in UML Modeling: An Exploratory Study with Novice Analysts" [2024-04] [[paper](https://arxiv.org/abs/2404.17739)]

- "Leveraging Large Language Models for Software Model Completion: Results from Industrial and Public Datasets" [2024-06] [[paper](https://arxiv.org/abs/2406.17651)]

- "Studying and Benchmarking Large Language Models For Log Level Suggestion" [2024-10] [[paper](https://arxiv.org/abs/2410.08499)]

- "A Model Is Not Built By A Single Prompt: LLM-Based Domain Modeling With Question Decomposition" [2024-10] [[paper](https://arxiv.org/abs/2410.09854)]

- "On the Utility of Domain Modeling Assistance with Large Language Models" [2024-10] [[paper](https://arxiv.org/abs/2410.12577)]

- "On the use of Large Language Models in Model-Driven Engineering" [2024-10] [[paper](https://arxiv.org/abs/2410.17370)]

- "LLM as a code generator in Agile Model Driven Development" [2024-10] [[paper](https://arxiv.org/abs/2410.18489)]

### Requirement Engineering

- "A Transformer-based Approach for Abstractive Summarization of Requirements from Obligations in Software Engineering Contracts" [2023-09] [RE 2023] [[paper](https://ieeexplore.ieee.org/document/10260954)]

- "Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs" [2023-10] [[paper](https://arxiv.org/abs/2310.13976)]

- "Requirements Engineering using Generative AI: Prompts and Prompting Patterns" [2023-11] [[paper](https://arxiv.org/abs/2311.03832)]

- "Prioritizing Software Requirements Using Large Language Models" [2024-04] [[paper](https://arxiv.org/abs/2405.01564)]

- "Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks" [2024-04] [[paper](https://arxiv.org/abs/2405.05135)]

- "Enhancing Legal Compliance and Regulation Analysis with Large Language Models" [2024-04] [[paper](https://arxiv.org/abs/2404.17522)]

- "MARE: Multi-Agents Collaboration Framework for Requirements Engineering" [2024-05] [[paper](https://arxiv.org/abs/2405.03256)]

- "Natural Language Processing for Requirements Traceability" [2024-05] [[paper](https://arxiv.org/abs/2405.10845)]

- "Multilingual Crowd-Based Requirements Engineering Using Large Language Models" [2024-08] [[paper](https://arxiv.org/abs/2408.06505)]

- "From Specifications to Prompts: On the Future of Generative LLMs in Requirements Engineering" [2024-08] [[paper](https://arxiv.org/abs/2408.09127)]

- "Leveraging LLMs for the Quality Assurance of Software Requirements" [2024-08] [[paper](https://arxiv.org/abs/2408.10886)]

- "Generative AI for Requirements Engineering: A Systematic Literature Review" [2024-09] [[paper](https://arxiv.org/abs/2409.06741)]

- "A Fine-grained Sentiment Analysis of App Reviews using Large Language Models: An Evaluation Study" [2024-09] [[paper](https://arxiv.org/abs/2409.07162)]

- "Leveraging Large Language Models for Predicting Cost and Duration in Software Engineering Projects" [2024-09] [[paper](https://arxiv.org/abs/2409.09617)]

- "Privacy Policy Analysis through Prompt Engineering for LLMs" [2024-09] [[paper](https://arxiv.org/abs/2409.14879)]

- "Exploring Requirements Elicitation from App Store User Reviews Using Large Language Models" [2024-09] [[paper](https://arxiv.org/abs/2409.15473)]

- "LLM-Cure: LLM-based Competitor User Review Analysis for Feature Enhancement" [2024-09] [[paper](https://arxiv.org/abs/2409.15724)]

- "Automatic Instantiation of Assurance Cases from Patterns Using Large Language Models" [2024-10] [[paper](https://arxiv.org/abs/2410.05488)]

- "Whose fault is it anyway? SILC: Safe Integration of LLM-Generated Code" [2024-10] [[paper](https://arxiv.org/abs/2410.18703)]

- "Assured Automatic Programming via Large Language Models" [2024-10] [[paper](https://arxiv.org/abs/2410.18494)]

- "Does GenAI Make Usability Testing Obsolete?" [2024-11] [[paper](https://arxiv.org/abs/2411.00634)]

## Analysis of AI-Generated Code

### Security and Vulnerabilities

- "You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion" [2021-08] [USENIX Security Symposium 2021] [[paper](https://www.usenix.org/conference/usenixsecurity21/presentation/schuster)]

- "Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?" [2022-04] [Empir. Softw. Eng.] [[paper](https://arxiv.org/abs/2204.04741)]

- "Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants" [2022-08] [USENIX Security Symposium 2023] [[paper](https://arxiv.org/abs/2208.09727)]

- "Do Users Write More Insecure Code with AI Assistants?" [2022-1] [CCS 2023] [[paper](https://arxiv.org/abs/2211.03622)]

- "Large Language Models for Code: Security Hardening and Adversarial Testing" [2023-02] [CCS 2023] [[paper](https://arxiv.org/abs/2302.05319)]

- "Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models" [2023-12] [[paper](https://arxiv.org/abs/2312.04724)]

- "CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion" [2024-03] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2403.07865)]

- "Just another copy and paste? Comparing the security vulnerabilities of ChatGPT generated code and StackOverflow answers" [2024-03] [[paper](https://arxiv.org/abs/2403.15600)]

- "DeVAIC: A Tool for Security Assessment of AI-generated Code" [2024-04] [[paper](https://arxiv.org/abs/2404.07548)]

- "CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models" [2024-04] [[paper](https://arxiv.org/abs/2404.13161)]

- "LLMs in Web-Development: Evaluating LLM-Generated PHP code unveiling vulnerabilities and limitations" [2024-04] [[paper](https://arxiv.org/abs/2404.14459)]

- "Do Neutral Prompts Produce Insecure Code? FormAI-v2 Dataset: Labelling Vulnerabilities in Code Generated by Large Language Models" [2024-04] [[paper](https://arxiv.org/abs/2404.18353)]

- "Codexity: Secure AI-assisted Code Generation" [2024-05] [[paper](https://arxiv.org/abs/2405.03927)]

- "Measuring Impacts of Poisoning on Model Parameters and Embeddings for Large Language Models of Code" [2024-05] [[paper](https://arxiv.org/abs/2405.11466)]

- "An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection" [2024-06] [[paper](https://arxiv.org/abs/2406.06822)]

- "Is Your AI-Generated Code Really Secure? Evaluating Large Language Models on Secure Code Generation with CodeSecEval" [2024-07] [[paper](https://arxiv.org/abs/2407.02395)]

- "Prompting Techniques for Secure Code Generation: A Systematic Investigation" [2024-07] [[paper](https://arxiv.org/abs/2407.07064)]

- "TAPI: Towards Target-Specific and Adversarial Prompt Injection against Code LLMs" [2024-07] [[paper](https://arxiv.org/abs/2407.09164)]

- "MaPPing Your Model: Assessing the Impact of Adversarial Attacks on LLM-based Programming Assistants" [2024-07] [[paper](https://arxiv.org/abs/2407.11072)]

- "Eliminating Backdoors in Neural Code Models via Trigger Inversion" [2024-08] [[paper](https://arxiv.org/abs/2408.04683)]

- ""You still have to study" -- On the Security of LLM generated code" [2024-08] [[paper](https://arxiv.org/abs/2408.07106)]

- "How Well Do Large Language Models Serve as End-to-End Secure Code Producers?" [2024-08] [[paper](https://arxiv.org/abs/2408.10495)]

- "While GitHub Copilot Excels at Coding, Does It Ensure Responsible Output?" [2024-08] [[paper](https://arxiv.org/abs/2408.11006)]

- "PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)" [2024-09] [[paper](https://arxiv.org/abs/2409.12699)]

- "RMCBench: Benchmarking Large Language Models' Resistance to Malicious Code" [2024-09] [[paper](https://arxiv.org/abs/2409.15154)]

- "Artificial-Intelligence Generated Code Considered Harmful: A Road Map for Secure and High-Quality Code Generation" [2024-09] [[paper](https://arxiv.org/abs/2409.19182)]

- "Demonstration Attack against In-Context Learning for Code Intelligence" [2024-10] [[paper](https://arxiv.org/abs/2410.02841)]

- "Hallucinating AI Hijacking Attack: Large Language Models and Malicious Code Recommenders" [2024-10] [[paper](https://arxiv.org/abs/2410.06462)]

- "SecCodePLT: A Unified Platform for Evaluating the Security of Code GenAI" [2024-10] [[paper](https://arxiv.org/abs/2410.11096)]

- "Security of Language Models for Code: A Systematic Literature Review" [2024-10] [[paper](https://arxiv.org/abs/2410.15631)]

- "RedCode: Risky Code Execution and Generation Benchmark for Code Agents" [2024-11] [[paper](https://arxiv.org/abs/2411.07781)]

### Correctness

- "An Empirical Evaluation of GitHub Copilot's Code Suggestions" [2022-05] [MSR 2022] [[paper](https://ieeexplore.ieee.org/document/9796235)]

- "Large Language Models and Simple, Stupid Bugs" [2023-03] [MSR 2023] [[paper](https://arxiv.org/abs/2303.11455)]

- "Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT" [2023-04] [[paper](https://arxiv.org/abs/2304.10778)]

- "No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT" [2023-08] [[paper](https://arxiv.org/abs/2308.04838)]

- "The Counterfeit Conundrum: Can Code Language Models Grasp the Nuances of Their Incorrect Generations?" [2024-02] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2402.19475)]

- "Bugs in Large Language Models Generated Code: An Empirical Study" [2024-03] [[paper](https://arxiv.org/abs/2403.08937)]

- "ChatGPT Incorrectness Detection in Software Reviews" [2024-03] [[paper](https://arxiv.org/abs/2403.16347)]

- "Validating LLM-Generated Programs with Metamorphic Prompt Testing" [2024-06] [[paper](https://arxiv.org/abs/2406.06864)]

- "Where Do Large Language Models Fail When Generating Code?" [2024-06] [[paper](https://arxiv.org/abs/2406.08731)]

- "GitHub Copilot: the perfect Code compLeeter?" [2024-06] [[paper](https://arxiv.org/abs/2406.11326)]

- "What's Wrong with Your Code Generated by Large Language Models? An Extensive Study" [2024-07] [[paper](https://arxiv.org/abs/2407.06153)]

- "Uncovering Weaknesses in Neural Code Generation" [2024-07] [[paper](https://arxiv.org/abs/2407.09793)]

- "Understanding Defects in Generated Codes by Language Models" [2024-08] [[paper](https://arxiv.org/abs/2408.13372)]

- "CodeSift: An LLM-Based Reference-Less Framework for Automatic Code Validation" [2024-08] [[paper](https://arxiv.org/abs/2408.15630)]

- "Examination of Code generated by Large Language Models" [2024-08] [[paper](https://arxiv.org/abs/2408.16601)]

- "Fixing Code Generation Errors for Large Language Models" [2024-09] [[paper](https://arxiv.org/abs/2409.00676)]

- "Can OpenSource beat ChatGPT? -- A Comparative Study of Large Language Models for Text-to-Code Generation" [2024-09] [[paper](https://arxiv.org/abs/2409.04164)]

- "Insights from Benchmarking Frontier Language Models on Web App Code Generation" [2024-09] [[paper](https://arxiv.org/abs/2409.05177)]

- "Evaluating the Performance of Large Language Models in Competitive Programming: A Multi-Year, Multi-Grade Analysis" [2024-09] [[paper](https://arxiv.org/abs/2409.09054)]

- "A Case Study of Web App Coding with OpenAI Reasoning Models" [2024-09] [[paper](https://arxiv.org/abs/2409.13773)]

- "CodeJudge: Evaluating Code Generation with Large Language Models" [2024-10] [[paper](https://arxiv.org/abs/2410.02184)]

- "An evaluation of LLM code generation capabilities through graded exercises" [2024-10] [[paper](https://arxiv.org/abs/2410.16292)]

- "A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?" [2024-11] [[paper](https://arxiv.org/abs/2411.01414)]

- "Evaluating ChatGPT-3.5 Efficiency in Solving Coding Problems of Different Complexity Levels: An Empirical Analysis" [2024-11] [[paper](https://arxiv.org/abs/2411.07529)]

### Hallucination

- "Exploring and Evaluating Hallucinations in LLM-Powered Code Generation" [2024-04] [[paper](https://arxiv.org/abs/2404.00971)]

- "CodeHalu: Code Hallucinations in LLMs Driven by Execution-based Verification" [2024-04] [[paper](https://arxiv.org/abs/2405.00253)]

- "We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs" [2024-06] [[paper](https://arxiv.org/abs/2406.10279)]

- "Code Hallucination" [2024-07] [[paper](https://arxiv.org/abs/2407.04831)]

- "On Mitigating Code LLM Hallucinations with API Documentation" [2024-07] [[paper](https://arxiv.org/abs/2407.09726)]

- "CodeMirage: Hallucinations in Code Generated by Large Language Models" [2024-08] [[paper](https://arxiv.org/abs/2408.08333)]

- "LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation" [2024-09] [[paper](https://arxiv.org/abs/2409.20550)]

- "Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code" [2024-10] [[paper](https://arxiv.org/abs/2410.09997)]

- "ETF: An Entity Tracing Framework for Hallucination Detection in Code Summaries" [2024-10] [[paper](https://arxiv.org/abs/2410.14748)]

### Efficiency

- "On Evaluating the Efficiency of Source Code Generated by LLMs" [2024-04] [[paper](https://arxiv.org/abs/2404.06041)]

- "A Controlled Experiment on the Energy Efficiency of the Source Code Generated by Code Llama" [2024-05] [[paper](https://arxiv.org/abs/2405.03616)]

- "From Effectiveness to Efficiency: Comparative Evaluation of Code Generated by LCGMs for Bilingual Programming Questions" [2024-06] [[paper](https://arxiv.org/abs/2406.00602)]

- "How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark" [2024-06] [[paper](https://arxiv.org/abs/2406.06647)]

- "ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?" [2024-07] [[paper](https://arxiv.org/abs/2407.14044)]

- "A Performance Study of LLM-Generated Code on Leetcode" [2024-07] [[paper](https://arxiv.org/abs/2407.21579)]

- "Evaluating Language Models for Efficient Code Generation" [2024-08] [[paper](https://arxiv.org/abs/2408.06450)]

- "Effi-Code: Unleashing Code Efficiency in Language Models" [2024-10] [[paper](https://arxiv.org/abs/2410.10209)]

- "Rethinking Code Refinement: Learning to Judge Code Efficiency" [2024-10] [[paper](https://arxiv.org/abs/2410.22375)]

### Robustness

- "Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain" [2023-10] [[paper](https://arxiv.org/abs/2310.14053)]

- "Do Large Code Models Understand Programming Concepts? A Black-box Approach" [2024-02] [ICML 2024] [[paper](https://arxiv.org/abs/2402.05980)]

- "Syntactic Robustness for LLM-based Code Generation" [2024-04] [[paper](https://arxiv.org/abs/2404.01535)]

- "NLPerturbator: Studying the Robustness of Code LLMs to Natural Language Variations" [2024-06] [[paper](https://arxiv.org/abs/2406.19783)]

- "An Empirical Study on Capability of Large Language Models in Understanding Code Semantics" [2024-07] [[paper](https://arxiv.org/abs/2407.03611)]

### Interpretability

- "A Critical Study of What Code-LLMs (Do Not) Learn" [2024-06] [ACL 2024 Findings] [[paper](https://arxiv.org/abs/2406.11930)]

- "Looking into Black Box Code Language Models" [2024-07] [[paper](https://arxiv.org/abs/2407.04868)]

- "DeepCodeProbe: Towards Understanding What Models Trained on Code Learn" [2024-07] [[paper](https://arxiv.org/abs/2407.08890)]

- "Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations" [2024-07] [[paper](https://arxiv.org/abs/2407.08983)]

### API Usage

- "How and Why LLMs Use Deprecated APIs in Code Completion? An Empirical Study" [2024-06] [[paper](https://arxiv.org/abs/2406.09834)]

- "Is ChatGPT a Good Software Librarian? An Exploratory Study on the Use of ChatGPT for Software Library Recommendations" [2024-08] [[paper](https://arxiv.org/abs/2408.05128)]

- "A Systematic Evaluation of Large Code Models in API Suggestion: When, Which, and How" [2024-09] [[paper](https://arxiv.org/abs/2409.13178)]

- "AutoAPIEval: A Framework for Automated Evaluation of LLMs in API-Oriented Code Generation" [2024-09] [[paper](https://arxiv.org/abs/2409.15228)]

### Privacy

- "Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach" [2024-04] [[paper](https://arxiv.org/abs/2404.14296)]

- "CodeCipher: Learning to Obfuscate Source Code Against LLMs" [2024-10] [[paper](https://arxiv.org/abs/2410.05797)]

- "Decoding Secret Memorization in Code LLMs Through Token-Level Characterization" [2024-10] [[paper](https://arxiv.org/abs/2410.08858)]

### AI-Generated Code Detection

- "Zero-Shot Detection of Machine-Generated Codes" [2023-10] [[paper](https://arxiv.org/abs/2310.05103)]

- "CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code" [2024-04] [[paper](https://arxiv.org/abs/2404.15639)]

- "ChatGPT Code Detection: Techniques for Uncovering the Source of Code" [2024-05] [[paper](https://arxiv.org/abs/2405.15512)]

- "Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting" [2024-05] [[paper](https://arxiv.org/abs/2405.16133)]

- "Automatic Detection of LLM-generated Code: A Case Study of Claude 3 Haiku" [2024-09] [[paper](https://arxiv.org/abs/2409.01382)]

- "An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We?" [2024-11] [[paper](https://arxiv.org/abs/2411.04299)]

- "Distinguishing LLM-generated from Human-written Code by Contrastive Learning" [2024-11] [[paper](https://arxiv.org/abs/2411.04704)]

### Others

- "Who Wrote this Code? Watermarking for Code Generation" [2023-05] [ACL 2024] [[paper](https://arxiv.org/abs/2305.15060)]

- "Testing the Effect of Code Documentation on Large Language Model Code Understanding" [2024-04] [[paper](https://arxiv.org/abs/2404.03114)]

- "Automated Creation of Source Code Variants of a Cryptographic Hash Function Implementation Using Generative Pre-Trained Transformer Models" [2024-04] [[paper](https://arxiv.org/abs/2404.15681)]

- "Exploring Multi-Lingual Bias of Large Code Models in Code Generation" [2024-04] [[paper](https://arxiv.org/abs/2404.19368)]

- "Evaluation of the Programming Skills of Large Language Models" [2024-05] [[paper](https://arxiv.org/abs/2405.14388)]

- "Where Are Large Language Models for Code Generation on GitHub?" [2024-06] [[paper](https://arxiv.org/abs/2406.19544)]

- "Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Models" [2024-06] [[paper](https://arxiv.org/abs/2407.00456)]

- "Benchmarking Language Model Creativity: A Case Study on Code Generation" [2024-07] [[paper](https://arxiv.org/abs/2407.09007)]

- "Beyond Correctness: Benchmarking Multi-dimensional Code Generation for Large Language Models" [2024-07] [[paper](https://arxiv.org/abs/2407.11470)]

- "Is Functional Correctness Enough to Evaluate Code Language Models? Exploring Diversity of Generated Codes" [2024-08] [[paper](https://arxiv.org/abs/2408.14504)]

- "Strategic Optimization and Challenges of Large Language Models in Object-Oriented Programming" [2024-08] [[paper](https://arxiv.org/abs/2408.14834)]

- "A Survey on Evaluating Large Language Models in Code Generation Tasks" [2024-08] [[paper](https://arxiv.org/abs/2408.16498)]

- "An exploratory analysis of Community-based Question-Answering Platforms and GPT-3-driven Generative AI: Is it the end of online community-based learning?" [2024-09] [[paper](https://arxiv.org/abs/2409.17473)]

- "Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B" [2024-09] [[paper](https://arxiv.org/abs/2409.19027)]

- "Benchmarking ChatGPT, Codeium, and GitHub Copilot: A Comparative Study of AI-Driven Programming and Debugging Assistants" [2024-09] [[paper](https://arxiv.org/abs/2409.19922)]

- "Mitigating Gender Bias in Code Large Language Models via Model Editing" [2024-10] [[paper](https://arxiv.org/abs/2410.07820)]

- "Model Editing for LLMs4Code: How Far are We?" [2024-11] [[paper](https://arxiv.org/abs/2411.06638)]

## Human-LLM Interaction

- "Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models" [2022-04] [CHI EA 2022] [[paper](https://dl.acm.org/doi/abs/10.1145/3491101.3519665)]

- "Grounded Copilot: How Programmers Interact with Code-Generating Models" [2022-06] [OOPSLA 2023] [[paper](https://arxiv.org/abs/2206.15000)]

- "Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming" [2022-10] [[paper](https://arxiv.org/abs/2210.14306)]

- "The Impact of AI on Developer Productivity: Evidence from GitHub Copilot" [2023-02] [[paper](https://arxiv.org/abs/2302.06590)]

- "The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development" [2023-02] [IUI 2023] [[paper](https://arxiv.org/abs/2302.07080)]

- ""It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers" [2023-04] [ACM TCHI] [[paper](https://arxiv.org/abs/2304.02491)]

- "DevGPT: Studying Developer-ChatGPT Conversations" [2023-08] [[paper](https://arxiv.org/abs/2309.03914)]

- "How Do Analysts Understand and Verify AI-Assisted Data Analyses?" [2023-09] [[paper](https://arxiv.org/abs/2309.10947)]

- "How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment" [2023-09] [Koli Calling 2023] [[paper](https://arxiv.org/abs/2309.14049)]

- "Conversational Challenges in AI-Powered Data Science: Obstacles, Needs, and Design Opportunities" [2023-10] [[paper](https://arxiv.org/abs/2310.16164)]

- "The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers" [2024-04] [[paper](https://arxiv.org/abs/2404.02806)]

- "Unlocking Adaptive User Experience with Generative AI" [2024-04] [[paper](https://arxiv.org/abs/2404.05442)]

- "BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks" [2024-04] [[paper](https://arxiv.org/abs/2404.07387)]

- "How far are AI-powered programming assistants from meeting developers' needs?" [2024-04] [[paper](https://arxiv.org/abs/2404.12000)]

- "Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice" [2024-04] [[paper](https://arxiv.org/abs/2404.14901)]

- "The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances" [2024-04] [[paper](https://arxiv.org/abs/2407.09975)]

- "amplified.dev: a living document that begins to sketch a vision for a future where developers are amplified, not automated" [2024-05] [[paper](https://amplified.dev)]

- "Sketch Then Generate: Providing Incremental User Feedback and Guiding LLM Code Generation through Language-Oriented Code Sketches" [2024-05] [[paper](https://arxiv.org/abs/2405.03998)]

- "Using AI Assistants in Software Development: A Qualitative Study on Security Practices and Concerns" [2024-05] [[paper](https://arxiv.org/abs/2405.06371)]

- "Full Line Code Completion: Bringing AI to Desktop" [2024-05] [[paper](https://arxiv.org/abs/2405.08704)]

- "Developers' Perceptions on the Impact of ChatGPT in Software Development: A Survey" [2024-05] [[paper](https://arxiv.org/abs/2405.12195)]

- "A Transformer-Based Approach for Smart Invocation of Automatic Code Completion" [2024-05] [[paper](https://arxiv.org/abs/2405.14753)]

- "A Study on Developer Behaviors for Validating and Repairing LLM-Generated Code Using Eye Tracking and IDE Actions" [2024-05] [[paper](https://arxiv.org/abs/2405.16081)]

- "Analyzing Chat Protocols of Novice Programmers Solving Introductory Programming Tasks with ChatGPT" [2024-05] [[paper](https://arxiv.org/abs/2405.19132)]

- "Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agent" [2024-05] [[paper](https://arxiv.org/abs/2406.00215)]

- "Learning Task Decomposition to Assist Humans in Competitive Programming" [2024-06] [ACL 2024] [[paper](https://arxiv.org/abs/2406.04604)]

- "Impact of AI-tooling on the Engineering Workspace" [2024-06] [[paper](https://arxiv.org/abs/2406.07683)]

- "Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward" [2024-06] [[paper](https://arxiv.org/abs/2406.07765)]

- "Instruct, Not Assist: LLM-based Multi-Turn Planning and Hierarchical Questioning for Socratic Code Debugging" [2024-06] [[paper](https://arxiv.org/abs/2406.11709)]

- "Transforming Software Development: Evaluating the Efficiency and Challenges of GitHub Copilot in Real-World Projects" [2024-06] [[paper](https://arxiv.org/abs/2406.17910)]

- "Let the Code LLM Edit Itself When You Edit the Code" [2024-07] [[paper](https://arxiv.org/abs/2407.03157)]

- "Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation" [2024-07] [[paper](https://arxiv.org/abs/2407.05437)]

- "How Novice Programmers Use and Experience ChatGPT when Solving Programming Exercises in an Introductory Course" [2024-07] [[paper](https://arxiv.org/abs/2407.20792)]

- "Can Developers Prompt? A Controlled Experiment for Code Documentation Generation" [2024-08] [[paper](https://arxiv.org/abs/2408.00686)]

- "The Impact of Generative AI-Powered Code Generation Tools on Software Engineer Hiring: Recruiters' Experiences, Perceptions, and Strategies" [2024-09] [[paper](https://arxiv.org/abs/2409.00875)]

- "Investigating the Role of Cultural Values in Adopting Large Language Models for Software Engineering" [2024-09] [[paper](https://arxiv.org/abs/2409.05055)]

- "The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot" [2024-09] [[paper](https://arxiv.org/abs/2409.08379)]

- ""I Don't Use AI for Everything": Exploring Utility, Attitude, and Responsibility of AI-empowered Tools in Software Development" [2024-09] [[paper](https://arxiv.org/abs/2409.13343)]

- "Harnessing the Potential of Gen-AI Coding Assistants in Public Sector Software Development" [2024-09] [[paper](https://arxiv.org/abs/2409.17434)]

- "Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks" [2024-10] [[paper](https://arxiv.org/abs/2410.01026)]

- "Code-Survey: An LLM-Driven Methodology for Analyzing Large-Scale Codebases" [2024-10] [[paper](https://arxiv.org/abs/2410.01837)]

- "The potential of LLM-generated reports in DevSecOps" [2024-10] [[paper](https://arxiv.org/abs/2410.01899)]

- "The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot" [2024-10] [[paper](https://arxiv.org/abs/2410.02091)]

- "Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning" [2024-10] [[paper](https://arxiv.org/abs/2410.08922)]

- "One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming Tasks" [2024-10] [[paper](https://arxiv.org/abs/2410.09268)]

- "UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models" [2024-10] [[paper](https://arxiv.org/abs/2410.12841)]

- "How much does AI impact development speed? An enterprise-based randomized controlled trial" [2024-10] [[paper](https://arxiv.org/abs/2410.12944)]

- "Understanding the Effect of Algorithm Transparency of Model Explanations in Text-to-SQL Semantic Parsing" [2024-10] [[paper](https://arxiv.org/abs/2410.16283)]

- "Dear Diary: A randomized controlled trial of Generative AI coding tools in the workplace" [2024-10] [[paper](https://arxiv.org/abs/2410.18334)]

## Datasets

### Pretraining

- **CodeSearchNet**: "CodeSearchNet Challenge: Evaluating the State of Semantic Code Search" [2019-09] [[paper](https://arxiv.org/abs/1909.09436)] [[repo](https://github.com/github/CodeSearchNet)] [[data](https://huggingface.co/datasets/code_search_net)]

- **The Pile**: "The Pile: An 800GB Dataset of Diverse Text for Language Modeling" [2020-12], [[paper](https://arxiv.org/abs/2101.00027)] [[data](https://pile.eleuther.ai/)]

- **CodeParrot**, 2022-02, [[data](https://huggingface.co/datasets/codeparrot/github-code)]

- **The Stack**: "The Stack: 3 TB of permissively licensed source code" [2022-11] [[paper](https://arxiv.org/abs/2211.15533)] [[data](https://huggingface.co/datasets/bigcode/the-stack)]

- **ROOTS**: "The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset" [2023-03] [NeurIPS 2022 Datasets and Benchmarks Track] [[paper](https://arxiv.org/abs/2303.03915)] [[data](https://huggingface.co/datasets?search=bigscience-data/roots)]

- **The Stack v2**: "StarCoder 2 and The Stack v2: The Next Generation" [2024-02] [[paper](https://arxiv.org/abs/2402.19173)] [[data](https://huggingface.co/datasets/bigcode/the-stack-v2-dedup)]

### Benchmarks

#### Integrated Benchmarks

- **CodeXGLUE**: "CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" [2021-02] [NeurIPS Datasets and Benchmarks 2021] [[paper](https://arxiv.org/abs/2102.04664)] [[repo](https://github.com/microsoft/CodeXGLUE)] [[data](https://huggingface.co/datasets?search=code_x_glue)]

- **CodefuseEval**: "CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model" [2023-10] [[paper](https://arxiv.org/abs/2310.06266)] [[repo](https://github.com/codefuse-ai/codefuse-evaluation)]

- **CodeScope**: "CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation" [2023-11] [ACL 2024] [[paper](https://arxiv.org/abs/2311.08588)] [[repo](https://github.com/WeixiangYAN/CodeScope)]

- **CodeEditorBench**: "CodeEditorBench: Evaluating Code Editing Capability of Large Language Models" [2024-04] [[paper](https://arxiv.org/abs/2404.03543)] [[repo](https://github.com/CodeEditorBench/CodeEditorBench)]

- **Long Code Arena**: "Long Code Arena: a Set of Benchmarks for Long-Context Code Models" [2024-06] [[paper](https://arxiv.org/abs/2406.11612)] [[repo](https://github.com/JetBrains-Research/lca-baselines)]

- **CodeRAG-Bench**: "CodeRAG-Bench: Can Retrieval Augment Code Generation?" [2024-06] [[paper](https://arxiv.org/abs/2406.14497)] [[repo](https://github.com/code-rag-bench/code-rag-bench)]

- **LiveBench**: "LiveBench: A Challenging, Contamination-Free LLM Benchmark" [2024-06] [[paper](https://arxiv.org/abs/2406.19314)] [[repo](https://github.com/livebench/livebench)]

- **DebugEval**: "Enhancing the Code Debugging Ability of LLMs via Communicative Agent Based Data Refinement" [2024-08] [[paper](https://arxiv.org/abs/2408.05006)] [[repo](https://github.com/NEUIR/DebugEval)]

#### Program Synthesis

| Date    | Venue                            | Benchmark              | Size                 | Language                                                                         | Source                                                                                                                                                                                                                                                                                       |
| ------- | -------------------------------- | ---------------------- | -------------------- | -------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 2018-02 | LREC 2018                        | NL2Bash                | 9305                 | Bash                                                                             | "NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System" [[paper](https://arxiv.org/abs/1802.08979)] [[data](https://github.com/TellinaTool/nl2bash)]                                                                                            |
| 2018-08 | EMNLP 2018                       | CONCODE                | 104K                 | Java                                                                             | "Mapping Language to Code in Programmatic Context" [[paper](https://arxiv.org/abs/1808.09588)] [[data](https://github.com/sriniiyer/concode)]                                                                                                                                                |
| 2019-10 | EMNLP-IJCNLP 2019                | JuICe                  | 1.5M/3725 \*         | Python                                                                           | "JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation" [[paper](https://arxiv.org/abs/1910.02216)] [[data](https://github.com/rajasagashe/juice)]                                                                                                 |
| 2021-05 | NeurIPS 2021                     | APPS                   | 10000                | Python                                                                           | "Measuring Coding Challenge Competence With APPS" [[paper](https://arxiv.org/abs/2105.09938)] [[data](https://github.com/hendrycks/apps)]                                                                                                                                                    |
| 2021-07 | arXiv                            | HumanEval              | 164                  | Python                                                                           | "Evaluating Large Language Models Trained on Code" [[paper](https://arxiv.org/abs/2107.03374)] [[data](https://github.com/openai/human-eval)]                                                                                                                                                |
| 2021-08 | arXiv                            | MBPP/MathQA-Python     | 974/23914            | Python                                                                           | "Program Synthesis with Large Language Models" [[paper](https://arxiv.org/abs/2108.07732)] [[MBPP](https://github.com/google-research/google-research/tree/master/mbpp)] [[MathQA-Python](https://github.com/google/trax/blob/master/trax/examples/MathQA_Python_generation_notebook.ipynb)] |
| 2021-08 | ACL/IJCNLP 2021                  | PlotCoder              | 40797                | Python                                                                           | "PlotCoder: Hierarchical Decoding for Synthesizing Visualization Code in Programmatic Context" [[paper](https://aclanthology.org/2021.acl-long.169/)] [[data](https://github.com/jungyhuk/plotcoder)]                                                                                        |
| 2022-01 | arXiv                            | DSP                    | 1119                 | Python                                                                           | "Training and Evaluating a Jupyter Notebook Data Science Assistant" [[paper](https://arxiv.org/abs/2201.12901)] [[data](https://github.com/microsoft/DataScienceProblems)]                                                                                                                   |
| 2022-02 | Science                          | CodeContests           | 13610                | C++, Python, Java                                                                | "Competition-Level Code Generation with AlphaCode" [[paper](https://arxiv.org/abs/2203.07814)] [[data](https://github.com/google-deepmind/code_contests)]                                                                                                                                    |
| 2022-03 | EACL 2023 Findings               | MCoNaLa                | 896                  | Python                                                                           | "MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages" [[paper](https://arxiv.org/abs/2203.08388)] [[data](https://github.com/zorazrw/multilingual-conala)]                                                                                                              |
| 2022-06 | arXiv                            | AixBench               | 336                  | Java                                                                             | "AixBench: A Code Generation Benchmark Dataset" [[paper](https://arxiv.org/abs/2206.13179)] [[data](https://github.com/aixcoder-plugin/nl2code-dataset)]                                                                                                                                     |
| 2022-08 | IEEE Trans. Software Engineering | MultiPL-E              |                      |                                                                                  | "MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation", [[paper](https://arxiv.org/abs/2208.08227)] [[data](https://github.com/nuprl/MultiPL-E)]                                                                                                             |
| 2022-10 | ICLR 2023                        | MBXP                   | 12.4K                | Python, Java, JS, TypeScript, Go, C#, PHP, Ruby, Kotlin, C++, Perl, Scala, Swift | "Multi-lingual Evaluation of Code Generation Models" [[paper](https://arxiv.org/abs/2210.14868)] [[data](https://github.com/amazon-science/mxeval)]                                                                                                                                          |
| 2022-10 | ICLR 2023                        | Multilingual HumanEval | 1.9K                 | Python, Java, JS, TypeScript, Go, C#, PHP, Ruby, Kotlin, Perl, Scala, Swift      | "Multi-lingual Evaluation of Code Generation Models" [[paper](https://arxiv.org/abs/2210.14868)] [[data](https://github.com/amazon-science/mxeval)]                                                                                                                                          |
| 2022-10 | ICLR 2023                        | MathQA-X               | 5.6K                 | Python, Java, JS                                                                 | "Multi-lingual Evaluation of Code Generation Models" [[paper](https://arxiv.org/abs/2210.14868)] [[data](https://github.com/amazon-science/mxeval)]                                                                                                                                          |
| 2022-11 | arXiv                            | ExeDS                  | 534                  | Python                                                                           | "Execution-based Evaluation for Data Science Code Generation Models" [[paper](https://arxiv.org/abs/2211.09374)] [[data](https://github.com/Jun-jie-Huang/ExeDS)]                                                                                                                            |
| 2022-11 | arXiv                            | DS-1000                | 1000                 | Python                                                                           | "DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation" [[paper](https://arxiv.org/abs/2211.11501)] [[data](https://github.com/xlang-ai/DS-1000)]                                                                                                                       |
| 2022-12 | arXiv                            | ODEX                   | 945                  | Python                                                                           | "Execution-Based Evaluation for Open-Domain Code Generation" [[paper](https://arxiv.org/abs/2212.10481)] [[data](https://github.com/zorazrw/odex)]                                                                                                                                           |
| 2023-02 | arXiv                            | CoderEval              | 460                  | Python, Java                                                                     | "CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models" [[paper](https://arxiv.org/abs/2302.00288)] [[data](https://github.com/CoderEval/CoderEval)]                                                                                                        |
| 2023-03 | ACL 2024                         | xCodeEval              | 5.5M                 | C, C#, C++, Go, Java, JS, Kotlin, PHP, Python, Ruby, Rust                        | "XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval" [[paper](https://arxiv.org/abs/2303.03004)] [[data](https://github.com/ntunlp/xCodeEval)]                                                         |
| 2023-03 | arXiv                            | HumanEval-X            | 820                  | Python, C++, Java, JS, Go                                                        | "CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X" [[paper](https://arxiv.org/abs/2303.17568)] [[data](https://hub.docker.com/r/codegeex/codegeex)]                                                                                            |
| 2023-05 | arXiv                            | HumanEval+             | 164                  | Python                                                                           | "Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation" [[paper](https://arxiv.org/abs/2305.01210)] [[data](https://github.com/evalplus/evalplus)]                                                                              |
| 2023-06 | ACL 2024 Findings                | StudentEval            | 1749 $^\dagger$      | Python                                                                           | "StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code" [[paper](https://arxiv.org/abs/2306.04556)] [[data](https://huggingface.co/datasets/wellesley-easel/StudentEval)]                                                                                    |
| 2023-08 | ICLR 2024 Spotlight              | HumanEvalPack          | 984                  | Python, JS, Go, Java, C++, Rust                                                  | "OctoPack: Instruction Tuning Code Large Language Models" [[paper](https://arxiv.org/abs/2308.07124)] [[data](https://huggingface.co/datasets/bigcode/humanevalpack)]                                                                                                                        |
| 2023-06 | NeurIPS 2023                     | DotPrompts             | 10538 $^\ddagger$    | Java                                                                             | "Guiding Language Models of Code with Global Context using Monitors" [[paper](https://arxiv.org/abs/2306.10763)] [[data](https://github.com/microsoft/monitors4codegen)]                                                                                                                     |
| 2023-09 | arXiv                            | CodeApex               | 476                  | C++                                                                              | "CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models" [[paper](https://arxiv.org/abs/2309.01940)] [[data](https://github.com/APEXLAB/CodeApex)]                                                                                                                 |
| 2023-09 | arXiv                            | VerilogEval            | 8645/156 $^\diamond$ | Verilog                                                                          | "VerilogEval: Evaluating Large Language Models for Verilog Code Generation" [[paper](https://arxiv.org/abs/2309.07544)] [[data](https://github.com/NVlabs/verilog-eval)]                                                                                                                     |
| 2023-11 | arXiv                            | ML-Bench               | 10040                | Bash                                                                             | "ML-Bench: Large Language Models Leverage Open-source Libraries for Machine Learning Tasks" [[paper](https://arxiv.org/abs/2311.09835)] [[data](https://ml-bench.github.io/)]                                                                                                                |
| 2023-12 | arXiv                            | TACO                   | 26,433               | Python                                                                           | "TACO: Topics in Algorithmic COde generation dataset" [[paper](https://arxiv.org/abs/2312.14852)] [[data](https://github.com/FlagOpen/TACO)]                                                                                                                                                 |
| 2024-01 | HPDC                             | ParEval                | 420                  | C++, CUDA, HIP                                                                   | "Can Large Language Models Write Parallel Code?" [[paper](https://arxiv.org/abs/2401.12554)] [[data](https://github.com/parallelcodefoundry/ParEval)]                                                                                                                                        |
| 2024-02 | ACL 2024 Findings                | OOP                    | 431                  | Python                                                                           | "OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models" [[paper](https://arxiv.org/abs/2401.06628)] [[data](https://github.com/alphadl/OOP-eval)]                                                                                                                  |
| 2024-04 | arXiv                            | USACO                  | 307                  | Python                                                                           | "Can Language Models Solve Olympiad Programming?" [[paper](https://arxiv.org/abs/2404.10952)] [[data](https://github.com/princeton-nlp/USACO)]                                                                                                                                               |
| 2024-04 | LREC-COLING 2024                 | PECC                   | 2396                 | Python                                                                           | "PECC: Problem Extraction and Coding Challenges" [[paper](https://arxiv.org/abs/2404.18766)] [[data](https://github.com/HallerPatrick/pecc)]                                                                                                                                                 |
| 2024-04 | arXiv                            | CodeGuard+             | 23                   | Python, C                                                                        | "Constrained Decoding for Secure Code Generation" [[paper](https://arxiv.org/abs/2405.00218)] [[data](https://github.com/Dynamite321/CodeGuardPlus)]                                                                                                                                         |
| 2024-05 | ACL 2024 Findings                | NaturalCodeBench       | 402                  | Python, Java                                                                     | "NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Prompts" [[paper](https://arxiv.org/abs/2405.04520)] [[data](https://github.com/THUDM/NaturalCodeBench)]                                                                                              |
| 2024-05 | arXiv                            | MHPP                   | 140                  | Python                                                                           | "MHPP: Exploring the Capabilities and Limitations of Language Models Beyond Basic Code Generation" [[paper](https://arxiv.org/abs/2405.11430)] [[repo](https://github.com/SparksofAGI/MHPP)]                                                                                                 |
| 2024-06 | arXiv                            | VHDL-Eval              | 202                  | VHDL                                                                             | "VHDL-Eval: A Framework for Evaluating Large Language Models in VHDL Code Generation" [[paper](https://arxiv.org/abs/2406.04379)]                                                                                                                                                            |
| 2024-06 | arXiv                            | AICoderEval            | 492                  | Python                                                                           | "AICoderEval: Improving AI Domain Code Generation of Large Language Models" [[paper](https://arxiv.org/abs/2406.04712)] [[data](https://huggingface.co/datasets/vixuowis/AICoderEval)]                                                                                                       |
| 2024-06 | arXiv                            | VersiCode              | 98,692               | Python                                                                           | "VersiCode: Towards Version-controllable Code Generation" [[paper](https://arxiv.org/abs/2406.07411)] [[data](https://github.com/wutong8023/VersiCode)]                                                                                                                                      |
| 2024-06 | IEEE AITest 2024                 | ScenEval               | 12,864               | Java                                                                             | "ScenEval: A Benchmark for Scenario-Based Evaluation of Code Generation" [[paper](https://arxiv.org/abs/2406.12635)]                                                                                                                                                                         |
| 2024-06 | arXiv                            | BigCodeBench           | 1,140                | Python                                                                           | "BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions" [[paper](https://arxiv.org/abs/2406.15877)] [[data](https://github.com/bigcode-project/bigcodebench)]                                                                                      |
| 2024-07 | arXiv                            | CodeUpdateArena        | 670                  | Python                                                                           | "CodeUpdateArena: Benchmarking Knowledge Editing on API Updates" [[paper](https://arxiv.org/abs/2407.06249)] [[data](https://github.com/leo-liuzy/CodeUpdateArena)]                                                                                                                          |
| 2024-07 | arXiv                            | LBPP                   | 161                  | Python                                                                           | "On Leakage of Code Generation Evaluation Datasets" [[paper](https://arxiv.org/abs/2407.07565)] [[data](https://huggingface.co/datasets/CohereForAI/lbpp)]                                                                                                                                   |
| 2024-07 | arXiv                            | NoviCode               | 150                  | Python                                                                           | "NoviCode: Generating Programs from Natural Language Utterances by Novices" [[paper](https://arxiv.org/abs/2407.10626)] [[data](https://github.com/BIU-NLP/novicode)]                                                                                                                        |
| 2024-07 | arXiv                            | Case2Code              | 1.3M                 | Python                                                                           | "Case2Code: Learning Inductive Reasoning with Synthetic Data" [[paper](https://arxiv.org/abs/2407.12504)] [[data](https://github.com/choosewhatulike/case2code)]                                                                                                                             |
| 2024-07 | arXiv                            | SciCode                | 338                  | Python                                                                           | "SciCode: A Research Coding Benchmark Curated by Scientists" [[paper](https://arxiv.org/abs/2407.13168)] [[data](https://github.com/scicode-bench/SciCode)]                                                                                                                                  |
| 2024-07 | arXiv                            | auto-regression        | 460                  | Python                                                                           | "Generating Unseen Code Tests In Infinitum" [[paper](https://arxiv.org/abs/2407.19772)]                                                                                                                                                                                                      |
| 2024-07 | arXiv                            | WebApp1K               | 1000                 | JavaScript                                                                       | "WebApp1K: A Practical Code-Generation Benchmark for Web App Development" [[paper](https://arxiv.org/abs/2408.00019)] [[data](https://huggingface.co/datasets/onekq-ai/WebApp1K-React)]                                                                                                      |
| 2024-08 | ACL 2024 Findings                | CodeInsight            | 3409                 | Python                                                                           | "CodeInsight: A Curated Dataset of Practical Coding Solutions from Stack Overflow" [[paper](https://arxiv.org/abs/2409.16819)] [[data](https://github.com/NathanaelBeau/CodeInsight)]                                                                                                        |
| 2024-08 | arXiv                            | DomainEval             | 2454                 | Python                                                                           | "DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation" [[paper](https://arxiv.org/abs/2408.13204)] [[data](https://github.com/domaineval/DomainEval)]                                                                                                                  |
| 2024-09 | arXiv                            | ComplexCodeEval        | 7184/3897            | Python/Java                                                                      | "ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code" [[paper](https://arxiv.org/abs/2409.10280)] [[data](https://github.com/ComplexCodeEval/ComplexCodeEval)]                                                                                                |
| 2024-09 | ASE 2024                         | CoCoNote               | 58221                | Python Notebook                                                                  | "Contextualized Data-Wrangling Code Generation in Computational Notebooks" [[paper](https://arxiv.org/abs/2409.13551)] [[data](https://github.com/Jun-jie-Huang/CoCoNote)]                                                                                                                   |
| 2024-10 | arXiv                            | unnamed                | 77                   | Python                                                                           | "Evaluation of Code LLMs on Geospatial Code Generation" [[paper](https://arxiv.org/abs/2410.04617)] [[data](https://github.com/kraina-ai/geospatial-code-llms-dataset)]                                                                                                                      |
| 2024-10 | arXiv                            | mHumanEval             | 836,400              | 25PL, 204NL                                                                      | "mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code Generation" [[paper](https://arxiv.org/abs/2410.15037)] [[data](https://github.com/mraihan-gmu/mHumanEval)]                                                                                               |
| 2024-10 | arXiv                            | FeatEng                | 103                  | Python                                                                           | "Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists" [[paper](https://arxiv.org/abs/2410.23331)] [[data](https://github.com/FeatEng/FeatEng)]                                                                                                                       |
| 2024-11 | arXiv                            | GitChameleon           | 116                  | Python                                                                           | "GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models" [[paper](https://arxiv.org/abs/2411.05830)] [[data](https://github.com/NizarIslah/GitChameleon)]                                                                                                      |

\* Automatically mined/human-annotated

$^\dagger$ 1749 prompts for 48 problems

$^\ddagger$ 10538 prompts for 1420 problems

$^\diamond$ Machine/human prompts

#### Visually Grounded Program Synthesis

| Date    | Venue | Benchmark   | Size | Language | Source                                                                                                                                                                                                                                      |
| ------- | ----- | ----------- | ---- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 2024-04 | arXiv | MMCode      | 3548 | Python   | "MMCode: Evaluating Multi-Modal Code Large Language Models with Visually Rich Programming Problems" [[paper](https://arxiv.org/abs/2404.09486)] [[data](https://github.com/happylkx/MMCode)]                                                |
| 2024-05 | arXiv | Plot2Code   | 132  | Python   | "Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots" [[paper](https://arxiv.org/abs/2405.07990)] [[data](https://huggingface.co/datasets/TencentARC/Plot2Code)] |
| 2024-06 | arXiv | ChartMimic  | 1000 | Python   | "ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation" [[paper](https://arxiv.org/abs/2406.09961)] [[data](https://github.com/ChartMimic/ChartMimic)]                                                 |
| 2024-10 | arXiv | HumanEval-V | 108  | Python   | "HumanEval-V: Evaluating Visual Understanding and Reasoning Abilities of Large Multimodal Models Through Coding Tasks" [[paper](https://arxiv.org/abs/2410.12381)] [[data](https://github.com/HumanEval-V/HumanEval-V-Benchmark)]           |
| 2024-10 | arXiv | TurtleBench | 260  | Python   | "TurtleBench: A Visual Programming Benchmark in Turtle Geometry" [[paper](https://arxiv.org/abs/2411.00264)] [[data](https://github.com/sinaris76/TurtleBench)]                                                                             |

#### Code Reasoning and QA

| Date    | Venue               | Benchmark  | Size     | Language                            | Source                                                                                                                                                                                                                   |
| ------- | ------------------- | ---------- | -------- | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| 2021-09 | EMNLP 2021 Findings | CodeQA     | 120K/70K | Java/Python                         | "CodeQA: A Question Answering Dataset for Source Code Comprehension" [[paper](https://arxiv.org/abs/2109.08365)] [[data](https://github.com/jadecxliu/CodeQA)]                                                           |
| 2022-10 | NAACL 2022          | CS1QA      | 9237     | Python                              | "CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Course" [[paper](https://arxiv.org/abs/2210.14494)] [[data](https://github.com/cyoon47/CS1QA)]                              |
| 2023-09 | arXiv               | CodeApex   | 250      | C++                                 | "CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models" [[paper](https://arxiv.org/abs/2309.01940)] [[data](https://github.com/APEXLAB/CodeApex)]                                             |
| 2024-01 | ICML 2024           | CRUXEval   | 800      | Python                              | "CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution" [[paper](https://arxiv.org/abs/2401.03065)] [[data](https://github.com/facebookresearch/cruxeval)]                                               |
| 2024-05 | arXiv               | PythonIO   | 2650     | Python                              | "Multiple-Choice Questions are Efficient and Robust LLM Evaluators" [[paper](https://arxiv.org/abs/2405.11966)] [[data](https://github.com/Geralt-Targaryen/MC-Evaluation)]                                              |
| 2024-05 | arXiv               | StaCCQA    | 270K     | Python                              | "Aligning LLMs through Multi-perspective User Preference Ranking-based Feedback for Programming Question Answering" [[paper](https://arxiv.org/abs/2406.00037)] [[data](https://anonymous.4open.science/r/PETCoQA-810A)] |
| 2024-06 | arXiv               | RepoQA     | 500      | Python, C++, Java, Rust, TypeScript | "RepoQA: Evaluating Long Context Code Understanding" [[paper](https://arxiv.org/abs/2406.06025)] [[data](https://github.com/evalplus/repoqa)]                                                                            |
| 2024-08 | arXiv               | CruxEval-X | 12.6K    | 19                                  | "CRUXEval-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution" [[paper](https://arxiv.org/abs/2408.13001)] [[data](https://github.com/CRUXEVAL-X/cruxeval-x)]                                    |
| 2024-09 | arXiv               | SpecEval   | 204      | Java                                | "SpecEval: Evaluating Code Comprehension in Large Language Models via Program Specifications" [[paper](https://arxiv.org/abs/2409.12866)] [[data](https://sites.google.com/view/speceval/)]                              |
| 2024-10 | arXiv               | CodeMMLU   | 19912    | 13                                  | "CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities of CodeLLMs" [[paper](https://arxiv.org/abs/2410.01999)] [[data](https://github.com/FSoft-AI4Code/CodeMMLU)]                             |
| 2024-11 | arXiv               | unnamed    | 80232    | Python                              | "Leveraging Large Language Models in Code Question Answering: Baselines and Issues" [[paper](https://arxiv.org/abs/2411.03012)] [[data](https://github.com/IU-AES-AI4Code/CodeQuestionAnswering)]                        |

#### Text-to-SQL

- "Deep learning driven natural languages text to SQL query conversion: A survey", 2022-08, arXiv, [[paper](https://arxiv.org/abs/2208.04415)]
- "Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect", 2022-08, COLING 2022, [[paper](https://arxiv.org/abs/2208.10099)]
- "A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions", 2022-08, arXiv, [[paper](https://arxiv.org/abs/2208.13629)]
- "A survey on deep learning approaches for text-to-SQL", 2023-01, VLDB J., [[paper](https://link.springer.com/article/10.1007/s00778-022-00776-8)]

| Date    | Venue               | Benchmark        | Size       | Language | Source                                                                                                                                                                                                       |
| ------- | ------------------- | ---------------- | ---------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| 2017-08 | arXiv               | WikiSQL          | 80654      |          | "Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning" [[paper](https://arxiv.org/abs/1709.00103)] [[data](https://github.com/salesforce/WikiSQL)]                      |
| 2018-06 | CL 2018             | Advising         | 4570       |          | "Improving Text-to-SQL Evaluation Methodology" [[paper](https://arxiv.org/abs/1806.09029)] [[data](https://github.com/jkkummerfeld/text2sql-data/)]                                                          |
| 2018-09 | EMNLP 2018          | Spider           | 10181      |          | "Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task" [[paper](https://arxiv.org/abs/1809.08887)] [[data](https://yale-lily.github.io/spider)]    |
| 2019-06 | ACL 2019            | SParC            | 12726      |          | "SParC: Cross-Domain Semantic Parsing in Context" [[paper](https://arxiv.org/abs/1906.02285)] [[data](https://yale-lily.github.io/sparc)]                                                                    |
| 2019-07 | WWW 2020            | MIMICSQL         | 10000      |          | "Text-to-SQL Generation for Question Answering on Electronic Medical Records" [[paper](https://arxiv.org/abs/1908.01839)] [[data](https://github.com/wangpinggl/TREQS)]                                      |
| 2019-09 | EMNLP-IJCNLP 2019   | CoSQL            | 15598      |          | "CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases" [[paper](https://arxiv.org/abs/1909.05378)] [[data](https://yale-lily.github.io/cosql)]        |
| 2020-05 | LREC 2020           | Criteria-to-SQL  | 2003       |          | "Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing" [[paper](https://aclanthology.org/2020.lrec-1.714/)] [[data](https://github.com/xiaojingyu92/Criteria2SQL)]                    |
| 2020-10 | EMNLP 2020 Findings | Squall           | 11276      |          | "On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries" [[paper](https://arxiv.org/abs/2010.11246)] [[data](https://github.com/tzshi/squall)]                                    |
| 2020-10 | NAACL-HLT 2021      | Spider-Realistic | 508        |          | "Structure-Grounded Pretraining for Text-to-SQL" [[paper](https://arxiv.org/abs/2010.12773)] [[data](https://zenodo.org/records/5205322)]                                                                    |
| 2021-06 | ACL/IJCNLP 2021     | Spider-Syn       | 8034       |          | "Towards Robustness of Text-to-SQL Models against Synonym Substitution" [[paper](https://arxiv.org/abs/2106.01065)] [[data](https://arxiv.org/abs/2106.01065)]                                               |
| 2021-06 | NLP4Prog 2021       | SEDE             | 12023      |          | "Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data" [[paper](https://arxiv.org/abs/2106.05006)] [[data](https://github.com/hirupert/sede)]                                 |
| 2021-06 | ACL/IJCNLP 2021     | KaggleDBQA       | 400        |          | "KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers" [[paper](https://arxiv.org/abs/2106.11455)] [[data](https://github.com/chiahsuan156/KaggleDBQA)]                                                   |
| 2021-09 | EMNLP               | Spider-DK        | 535        |          | "Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization" [[paper](https://arxiv.org/abs/2109.05157)] [[data](https://github.com/ygan/Spider-DK)]                                     |
| 2022-05 | NAACL 2022 Findings | Spider-SS/CG     | 8034/45599 |          | "Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment" [[paper](https://arxiv.org/abs/2205.02054)] [[data](https://github.com/ygan/SpiderSS-SpiderCG)]                |
| 2023-05 | arXiv               | BIRD             | 12751      |          | "Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs" [[paper](https://arxiv.org/abs/2305.03111)] [[data](https://bird-bench.github.io/)]              |
| 2023-06 | ACL 2023            | XSemPLR          | 24.4K      |          | "XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations" [[paper](https://arxiv.org/abs/2306.04085)] [[data](https://github.com/psunlpgroup/XSemPLR)]             |
| 2024-05 | ACL 2024 Findings   | EHR-SeqSQL       | 31669      |          | "EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records" [[paper](https://arxiv.org/abs/2406.00019)]                                                            |
| 2024-06 | NAACL 2024          | BookSQL          | 100K       |          | "BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain" [[paper](https://arxiv.org/abs/2406.07860)] [[data](https://github.com/Exploration-Lab/BookSQL)]                                          |
| 2024-08 | ACL 2024 Findings   | MultiSQL         | 9257       |          | "MultiSQL: A Schema-Integrated Context-Dependent Text2SQL Dataset with Diverse SQL Operations" [[paper](https://aclanthology.org/2024.findings-acl.823/)] [[data](https://github.com/grandchicken/MultiSQL)] |
| 2024-09 | arXiv               | BEAVER           | 93         |          | "BEAVER: An Enterprise Benchmark for Text-to-SQL" [[paper](https://arxiv.org/abs/2409.02038)]                                                                                                                |
| 2024-10 | arXiv               | PRACTIQ          | 2812       |          | "PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries" [[paper](https://arxiv.org/abs/2410.11076)]                                                                |
| 2024-10 | arXiv               | BIS              | 239        |          | "BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarios" [[paper](https://arxiv.org/abs/2410.22925)] [[data](https://github.com/boracaglayan/bis-nl2sql)]                              |
| 2024-11 | arXiv               | Spider 2.0       | 632        |          | "Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows" [[paper](https://arxiv.org/abs/2411.07763)] [[data](https://github.com/xlang-ai/Spider2)]                            |

#### Code Translation

| Date    | Venue                                | Benchmark                | Size   | Language                                                  | Source                                                                                                                                                                                                              |
| ------- | ------------------------------------ | ------------------------ | ------ | --------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 2020-06 | NeurIPS 2020                         | Transcoder GeeksforGeeks | 1.4K   | C++, Java, Python                                         | "Unsupervised Translation of Programming Languages" [[paper](https://arxiv.org/abs/2006.03511)] [[data](https://github.com/facebookresearch/TransCoder)]                                                            |
| 2021-02 | NeurIPS Datasets and Benchmarks 2021 | CodeTrans                | 11.8K  | Java, C#                                                  | "CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" [[paper](https://arxiv.org/abs/2102.04664)] [[data](https://huggingface.co/datasets/code_x_glue_cc_code_to_code_trans)]     |
| 2021-08 | ACL 2023 Findings                    | Avatar                   | 9515   | Java, Python                                              | "AVATAR: A Parallel Corpus for Java-Python Program Translation" [[paper](https://arxiv.org/abs/2108.11590)] [[data](https://github.com/wasiahmad/AVATAR)]                                                           |
| 2022-06 | AAAI 2022                            | CoST                     | 132K   | C++, Java, Python, C#, JS, PHP, C                         | "Multilingual Code Snippets Training for Program Translation" [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/21434)] [[data](https://github.com/reddy-lab-code-research/MuST-CoST)]                      |
| 2022-06 | arXiv                                | XLCoST                   | 567K   | C++, Java, Python, C#, JS, PHP, C                         | "XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence" [[paper](https://arxiv.org/abs/2206.08474)] [[data](https://github.com/reddy-lab-code-research/XLCoST)]                                           |
| 2023-03 | arXiv                                | xCodeEval                | 5.6M   | C, C#, C++, Go, Java, JS, Kotlin, PHP, Python, Ruby, Rust | "xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval" [[paper](https://arxiv.org/abs/2303.03004)] [[data](https://github.com/ntunlp/xCodeEval)] |
| 2023-03 | arXiv                                | HumanEval-X              | 1640   | Python, C++, Java, JS, Go                                 | "CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X" [[paper](https://arxiv.org/abs/2303.17568)] [[data](https://github.com/THUDM/CodeGeeX)]                            |
| 2023-08 | arXiv                                | G-TransEval              | 4000   | C++, Java, C#, JS, Python                                 | "On the Evaluation of Neural Code Translation: Taxonomy and Benchmark" [[paper](https://arxiv.org/abs/2308.08961)] [[data](https://github.com/PolyEval/G-TransEval)]                                                |
| 2023-10 | arXiv                                | CodeTransOcean           | 270.5K | 45                                                        | "CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation" [[paper](https://arxiv.org/abs/2310.04951)] [[data](https://github.com/WeixiangYAN/CodeTransOcean)]                                   |
| 2024-11 | arXiv                                | Classeval-T              | 94     | Python, Java, C++                                         | "Escalating LLM-based Code Translation Benchmarking into the Class-level Era" [[paper](https://arxiv.org/abs/2411.06145)]                                                                                           |

#### Program Repair

- "Neural Program Repair: Systems, Challenges and Solutions", 2022-02, Internetware 2022, [[paper](https://arxiv.org/abs/2202.10868)]
- "A Survey of Learning-based Automated Program Repair", 2023-01, arXiv, [[paper](https://arxiv.org/abs/2301.03270)]
- "A Survey on Automated Program Repair Techniques", 2023-03, arXiv, [[paper](https://arxiv.org/abs/2303.18184)]

| Date    | Venue                            | Benchmark           | Size      | Language                                                  | Source                                                                                                                                                                                                                    |
| ------- | -------------------------------- | ------------------- | --------- | --------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 2014-07 | ISSTA 2014                       | Defects4J           | 357       | Java                                                      | "Defects4J: A Database of Existing Faults to Enable Controlled Testing Studies for Java Programs" [[paper](https://dl.acm.org/doi/10.1145/2610384.2628055)] [[data](https://github.com/rjust/defects4j)]                  |
| 2015-12 | IEEE Trans. Software Engineering | ManyBugs/IntroClass | 185/998   | C                                                         | "The ManyBugs and IntroClass Benchmarks for Automated Repair of C Programs" [[paper](https://ieeexplore.ieee.org/document/7153570)] [[data](https://repairbenchmarks.cs.umass.edu/)]                                      |
| 2016-11 | FSE 2016                         | BugAID              | 105K      | JS                                                        | "Discovering Bug Patterns in JavaScript" [[paper](https://dl.acm.org/doi/10.1145/2950290.2950308)] [[data](https://salt.ece.ubc.ca/software/bugaid/)]                                                                     |
| 2017-02 | AAAI 2017                        | DeepFix             | 6971      | C                                                         | "DeepFix: Fixing Common C Language Errors by Deep Learning" [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/10742)] [[data](https://bitbucket.org/iiscseal/deepfix/src/master/)]                                |
| 2017-05 | ICSE-C 2017                      | Codeflaws           | 3902      | C                                                         | "DeepFix: Fixing Common C Language Errors by Deep Learning" [[paper](https://dl.acm.org/doi/10.1109/ICSE-C.2017.76)] [[data](https://codeflaws.github.io/)]                                                               |
| 2017-10 | SPLASH 2017                      | QuixBugs            | 80        | Java, Python                                              | "QuixBugs: a multi-lingual program repair benchmark set based on the quixey challenge" [[paper](https://dl.acm.org/doi/10.1145/3135932.3135941)] [[data](https://github.com/jkoppel/QuixBugs)]                            |
| 2018-05 | MSR 2018                         | Bugs.jar            | 1158      | Java                                                      | "Bugs.jar: a large-scale, diverse dataset of real-world Java bugs" [[paper](https://dl.acm.org/doi/10.1145/3196398.3196473)] [[data](https://github.com/bugs-dot-jar/bugs-dot-jar)]                                       |
| 2018-12 | ACM Trans. Softw. Eng. Methodol. | BFP                 | 124K      | Java                                                      | "An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation" [[paper](https://arxiv.org/abs/1812.08693)] [[data](https://sites.google.com/view/learning-fixes)]                         |
| 2019-01 | SANER 2019                       | Bears               | 251       | Java                                                      | "Bears: An Extensible Java Bug Benchmark for Automatic Program Repair Studies" [[paper](https://arxiv.org/abs/1901.06024)] [[data](https://github.com/bears-bugs/bears-benchmark)]                                        |
| 2019-01 | ICSE 2019                        | unnamed             | 21.8K \*  | Java                                                      | "On Learning Meaningful Code Changes via Neural Machine Translation" [[paper](https://arxiv.org/abs/1901.09102)] [[data](https://sites.google.com/view/learning-codechanges)]                                             |
| 2019-04 | ICST 2019                        | BugsJS              | 453       | JS                                                        | "BugsJS: a Benchmark of JavaScript Bugs" [[paper](https://ieeexplore.ieee.org/document/8730197)] [[data](https://bugsjs.github.io/)]                                                                                      |
| 2019-05 | ICSE 2019                        | BugSwarm            | 1827/1264 | Java/Python                                               | "BugSwarm: mining and continuously growing a dataset of reproducible failures and fixes" [[paper](https://dl.acm.org/doi/10.1109/ICSE.2019.00048)] [[data](https://www.bugswarm.org/)]                                    |
| 2019-05 | ICSE 2019                        | CPatMiner           | 17K \*    | Java                                                      | "Graph-based mining of in-the-wild, fine-grained, semantic code change patterns" [[paper](https://dl.acm.org/doi/10.1109/ICSE.2019.00089)] [[data](https://nguyenhoan.github.io/CPatMiner/)]                              |
| 2019-05 | MSR 2020                         | ManySStuBs4J        | 154K      | Java                                                      | "How Often Do Single-Statement Bugs Occur? The ManySStuBs4J Dataset" [[paper](https://arxiv.org/abs/1905.13334)] [[data](https://github.com/mast-group/mineSStuBs)]                                                       |
| 2019-11 | ASE 2019                         | Refactory           | 1783      | Python                                                    | "Re-factoring based program repair applied to programming assignments" [[paper](https://dl.acm.org/doi/10.1109/ASE.2019.00044)] [[data](https://github.com/githubhuyang/refactory)]                                       |
| 2020-07 | ISSTA 2020                       | CoCoNut             | 24M       | Java, Python, C, JS                                       | "CoCoNuT: combining context-aware neural translation models using ensemble for program repair" [[paper](https://dl.acm.org/doi/10.1145/3395363.3397369)] [[data](https://github.com/lin-tan/CoCoNut-Artifact)]            |
| 2020-10 | Inf. Softw. Technol.             | Review4Repair       | 58021     | Java                                                      | "Review4Repair: Code Review Aided Automatic Program Repairing" [[paper](https://arxiv.org/abs/2010.01544)] [[data](https://github.com/Review4Repair/Review4Repair)]                                                       |
| 2020-11 | ESEC/FSE 2020                    | BugsInPy            | 493       | Python                                                    | "BugsInPy: A Database of Existing Bugs in Python Programs to Enable Controlled Testing and Debugging Studies" [[paper](https://dl.acm.org/doi/abs/10.1145/3368089.3417943)] [[data](https://github.com/soarsmu/BugsInPy)] |
| 2021-07 | ICML 2021                        | TFix                | 105K      | JS                                                        | "TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer" [[paper](https://proceedings.mlr.press/v139/berabi21a.html)] [[data](https://github.com/eth-sri/TFix)]                                              |
| 2021-08 | arXiv                            | Megadiff            | 663K \*   | Java                                                      | "Megadiff: A Dataset of 600k Java Source Code Changes Categorized by Diff Size" [[paper](https://arxiv.org/abs/2108.04631)] [[data](https://github.com/ASSERT-KTH/megadiff)]                                              |
| 2022-01 | SSB/TSSB                         | MSR 2022            | 9M/3M     | Python                                                    | "TSSB-3M: Mining single statement bugs at massive scale" [[paper](https://arxiv.org/abs/2201.12046)] [[data](https://cedricrupb.github.io/TSSB3M/)]                                                                       |
| 2022-10 | MSR 2022                         | FixJS               | 324K      | JS                                                        | "FixJS: a dataset of bug-fixing JavaScript commits" [[paper](https://dl.acm.org/doi/abs/10.1145/3524842.3528480)] [[data](https://github.com/AAI-USZ/FixJS)]                                                              |
| 2022-11 | ESEC/FSE 2022                    | TypeBugs            | 93        | Python                                                    | "PyTER: Effective Program Repair for Python Type Errors" [[paper](https://dl.acm.org/doi/abs/10.1145/3540250.3549130)] [[data](https://github.com/kupl/PyTER)]                                                            |
| 2023-03 | arXiv                            | xCodeEval           | 4.7M      | C, C#, C++, Go, Java, JS, Kotlin, PHP, Python, Ruby, Rust | "xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval" [[paper](https://arxiv.org/abs/2303.03004)] [[data](https://github.com/ntunlp/xCodeEval)]       |
| 2023-04 | arXiv                            | RunBugRun           | 450K      | C, C++, Java, Python, JS, Ruby, Go, PHP                   | "RunBugRun -- An Executable Dataset for Automated Program Repair" [[paper](https://arxiv.org/abs/2304.01102)] [[data](https://github.com/giganticode/run_bug_run)]                                                        |
| 2023-08 | arXiv                            | HumanEvalPack       | 984       | Python, JS, Go, Java, C++, Rust                           | "OctoPack: Instruction Tuning Code Large Language Models" [[paper](https://arxiv.org/abs/2308.07124)] [[data](https://huggingface.co/datasets/bigcode/humanevalpack)]                                                     |
| 2024-01 | arXiv                            | DebugBench          | 4253      | C++, Java, Python                                         | "DebugBench: Evaluating Debugging Capability of Large Language Models" [[paper](https://arxiv.org/abs/2401.04621)] [[data](https://github.com/thunlp/DebugBench)]                                                         |
| 2024-11 | arXiv                            | MdEval              | 3513      | 18                                                        | "MdEval: Massively Multilingual Code Debugging" [[paper](https://arxiv.org/abs/2411.02310)]                                                                                                                               |

\* These are code-change datasest, and only a subset therein concerns bug fixing.

#### Code Summarization

- "A Survey of Automatic Source Code Summarization", 2022-02, Symmetry, [[paper](https://www.mdpi.com/2073-8994/14/3/471)]

| Date    | Venue       | Benchmark     | Size    | Language                        | Source                                                                                                                                                                                                                             |
| ------- | ----------- | ------------- | ------- | ------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 2016-08 | ACL 2016    | CODE-NN       | 66K/32K | C#/SQL                          | "Summarizing Source Code using a Neural Attention Model" [[paper](https://aclanthology.org/P16-1195/)] [[data](https://github.com/sriniiyer/codenn)]                                                                               |
| 2017-07 | IJCNLP 2017 | unnamed       | 150K    | Python                          | "A parallel corpus of Python functions and documentation strings for automated code documentation and code generation" [[paper](https://arxiv.org/abs/1707.02275)] [[data](https://github.com/EdinburghNLP/code-docstring-corpus)] |
| 2018-05 | ICPC 2018   | DeepCom       | 588K    | Java                            | "Deep code comment generation" [[paper](https://dl.acm.org/doi/10.1145/3196321.3196334)] [[data](https://github.com/xing-hu/DeepCom)]                                                                                              |
| 2018-07 | IJCAI 2018  | TL-CodeSum    | 411K    | Java                            | "Summarizing Source Code with Transferred API Knowledge" [[paper](https://www.ijcai.org/proceedings/2018/314)] [[data](https://github.com/xing-hu/TL-CodeSum)]                                                                     |
| 2018-11 | ASE 2018    | unnamed       | 109K    | Python                          | "Improving Automatic Source Code Summarization via Deep Reinforcement Learning" [[paper](https://arxiv.org/abs/1811.07234)] [[data](https://github.com/wanyao1992/code_summarization_public)]                                      |
| 2019-09 | arxiv       | CodeSearchNet | 2.3M    | Go, JS, Python, PHP, Java, Ruby | "CodeSearchNet Challenge: Evaluating the State of Semantic Code Search" [[paper](https://arxiv.org/abs/1909.09436)] [[data](https://github.com/github/CodeSearchNet)]                                                              |
| 2023-08 | arXiv       | HumanEvalPack | 984     | Python, JS, Go, Java, C++, Rust | "OctoPack: Instruction Tuning Code Large Language Models" [[paper](https://arxiv.org/abs/2308.07124)] [[data](https://huggingface.co/datasets/bigcode/humanevalpack)]                                                              |

#### Defect/Vulnerability Detection

- "Benchmarking Software Vulnerability Detection Techniques: A Survey", 2023-03, arXiv, [[paper](https://arxiv.org/abs/2303.16362)]

| Date    | Venue                        | Benchmark      | Size  | Language | Source                                                                                                                                                                                                                       |
| ------- | ---------------------------- | -------------- | ----- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 2018-01 | NDSS 2018                    | CGD            | 62K   | C, C++   | "VulDeePecker: A Deep Learning-Based System for Vulnerability Detection" [[paper](https://arxiv.org/abs/1801.01681)] [[data](https://github.com/CGCL-codes/VulDeePecker)]                                                    |
| 2018-04 | IEEE Trans. Ind. Informatics | unnamed        | 32988 | C, C++   | "Cross-Project Transfer Representation Learning for Vulnerable Function Discovery" [[paper](https://ieeexplore.ieee.org/document/8329207)] [[data](https://github.com/DanielLin1986/TransferRepresentationLearning)]         |
| 2018-07 | ICMLA 2018                   | Draper VDISC   | 12.8M | C, C++   | "Automated Vulnerability Detection in Source Code Using Deep Representation Learning" [[paper](https://arxiv.org/abs/1807.04320)] [[data](https://osf.io/d45bw/)]                                                            |
| 2018-07 | IEEE TDSC                    | SySeVR         | 15591 | C, C++   | "SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities" [[paper](https://arxiv.org/abs/1807.06756)] [[data](https://github.com/SySeVR/SySeVR)]                                                      |
| 2019-02 | MSR 2019                     | unnamed        | 624   | Java     | "A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software" [[paper](https://arxiv.org/abs/1902.02595)] [[data](https://github.com/SAP/project-kb/tree/main/MSR2019)]                                   |
| 2019-09 | NeurIPS 2019                 | Devign         | 49K   | C        | "Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks" [[paper](https://arxiv.org/abs/1909.03496)] [[data](https://sites.google.com/view/devign)]            |
| 2019-11 | IEEE TDSC                    | unnamed        | 170K  | C, C++   | "Software Vulnerability Discovery via Learning Multi-Domain Knowledge Bases" [[paper](https://ieeexplore.ieee.org/document/8906156)] [[data](https://github.com/DanielLin1986/RepresentationsLearningFromMulti_domain)]      |
| 2019-12 | ICLR 2020                    | GREAT          | 2.8M  | Python   | "Global Relational Models of Source Code" [[paper](https://openreview.net/forum?id=B1lnbRNtwr)] [[data](https://zenodo.org/records/3954944)]                                                                                 |
| 2020-01 | IEEE TDSC                    | MVD            | 182K  | C, C++   | "μVulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection" [[paper](https://arxiv.org/abs/2001.02334)] [[data](https://github.com/muVulDeePecker/muVulDeePecker)]                                  |
| 2020-02 | ICICS 2019                   | unnamed        | 1471  | C        | "Deep Learning-Based Vulnerable Function Detection: A Benchmark" [[paper](https://link.springer.com/chapter/10.1007/978-3-030-41579-2_13)] [[data](https://github.com/DanielLin1986/Function-level-Vulnerability-Detection)] |
| 2020-09 | IEEE Trans. Software Eng.    | ReVeal         | 18K   | C        | "Deep Learning based Vulnerability Detection: Are We There Yet?" [[paper](https://arxiv.org/abs/2009.07235)] [[data](https://bit.ly/3bX30ai)]                                                                                |
| 2020-09 | MSR 2020                     | Big-Vul        | 265K  | C, C++   | "A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries" [[paper](https://dl.acm.org/doi/10.1145/3379597.3387501)] [[data](https://github.com/ZeoVan/MSR_20_Code_Vulnerability_CSV_Dataset)]                 |
| 2021-02 | ICSE (SEIP) 2021             | D2A            | 1.3M  | C, C++   | "D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis" [[paper](https://arxiv.org/abs/2102.07995)] [[data](https://github.com/ibm/D2A)]                                             |
| 2021-05 | NeurIPS 2021                 | PyPIBugs       | 2374  | Python   | "Self-Supervised Bug Detection and Repair" [[paper](https://arxiv.org/abs/2105.12787)] [[data](https://www.microsoft.com/en-us/download/103554)]                                                                             |
| 2021-07 | In PROMISE 2021              | CVEfixes       | 5495  | 27       | "CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software" [[paper](https://arxiv.org/abs/2107.08760)] [[data](https://zenodo.org/records/7029359)]                                       |
| 2021-08 | ESEC/FSE 2021                | CrossVul       | 27476 | 40+      | "CrossVul: a cross-language vulnerability dataset with commit data" [[paper](https://dl.acm.org/doi/10.1145/3468264.3473122)] [[data](https://zenodo.org/records/4734050)]                                                   |
| 2023-04 | RAID 2023                    | DiverseVul     | 349K  | C, C++   | "DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection" [[paper](https://arxiv.org/abs/2304.00409)] [[data](https://github.com/wagner-group/diversevul)]                          |
| 2023-06 | arXiv                        | VulnPatchPairs | 26K   | C        | "Limits of Machine Learning for Automatic Vulnerability Detection" [[paper](https://arxiv.org/abs/2306.17193)] [[data](https://github.com/niklasrisse/LimitsOfML4Vuln)]                                                      |
| 2023-11 | arXiv                        | VulBench       | 455   | C        | "How Far Have We Gone in Vulnerability Detection Using Large Language Models" [[paper](https://arxiv.org/abs/2311.12420)] [[data](https://anonymous.4open.science/r/VulBench-EA6F/)]                                         |
| 2024-03 | arXiv                        | PrimeVul       | 236K  | C/C++    | "Vulnerability Detection with Code Language Models: How Far Are We?" [[paper](https://arxiv.org/abs/2403.18624)]                                                                                                             |
| 2024-06 | arXiv                        | VulDetectBench | 1000  | C/C++    | "VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models" [[paper](https://arxiv.org/abs/2406.07595)] [[data](https://github.com/Sweetaroo/VulDetectBench)]                     |
| 2024-08 | arXiv                        | CodeJudge-Eval | 1860  | Python   | "CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?" [[paper](https://arxiv.org/abs/2408.10718)] [[data](https://github.com/CodeLLM-Research/CodeJudge-Eval)]                                   |

#### Code Retrieval

- "Code Search: A Survey of Techniques for Finding Code", 2022-04, ICSME 2021, [[paper](ACM Comput. Surv)]
- "A Survey of Deep Code Search", 2023-05, arXiv, [[paper](https://arxiv.org/abs/2305.05959)]

| Date    | Venue                                | Benchmark            | Size      | Language                                                | Source                                                                                                                                                                                                                                              |
| ------- | ------------------------------------ | -------------------- | --------- | ------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 2018-03 | WWW 2018                             | StaQC                | 148K/120K | Python/SQL                                              | "StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow" [[paper](https://arxiv.org/abs/1803.09371)] [[data](https://github.com/LittleYUYU/StackOverflow-Question-Code-Dataset)]                                                   |
| 2018-05 | ICSE 2018                            | DeepCS               | 16.2M     | Java                                                    | "Deep Code Search" [[paper](https://dl.acm.org/doi/10.1145/3180155.3180167)] [[data](https://github.com/guxd/deep-code-search)]                                                                                                                     |
| 2018-05 | MSR 2018                             | CoNaLa               | 600K/2.9K | Python                                                  | "Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow" [[paper](https://arxiv.org/abs/1805.08949)] [[data](https://conala-corpus.github.io/)]                                                                               |
| 2019-08 | arXiv                                | unnamed              | 287       | Java                                                    | "Neural Code Search Evaluation Dataset" [[paper](https://arxiv.org/abs/1908.09804)] [[data](https://github.com/facebookresearch/Neural-Code-Search-Evaluation-Dataset)]                                                                             |
| 2019-09 | arXiv                                | CodeSearchNet        | 2.3M/99   | Go, PHP, JS, Python, Java, Ruby                         | "CodeSearchNet Challenge: Evaluating the State of Semantic Code Search" [[paper](https://arxiv.org/abs/1909.09436)] [[data](https://github.com/github/CodeSearchNet)]                                                                               |
| 2020-02 | SANER 2020                           | CosBench             | 52        | Java                                                    | "Are the Code Snippets What We Are Searching for? A Benchmark and an Empirical Study on Code Search with Natural-Language Queries" [[paper](https://ieeexplore.ieee.org/document/9054840)] [[data](https://github.com/BASE-LAB-SJTU/CosBench/wiki)] |
| 2020-08 | arXiv                                | SO-DS                | 2.2K      | Python                                                  | "Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent" [[paper](https://arxiv.org/abs/2008.12193)] [[data](https://github.com/nokia/codesearch)]                                                          |
| 2020-10 | ACM Trans. Knowl. Discov. Data       | FB-Java              | 249K      | Java                                                    | "Deep Graph Matching and Searching for Semantic Code Retrieval" [[paper](https://arxiv.org/abs/2010.12908)] [[data](https://github.com/ryderling/DGMS)]                                                                                             |
| 2021-02 | NeurIPS Datasets and Benchmarks 2021 | AdvTest/WebQueryTest | 280K/1K   | Python                                                  | "CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation" [[paper](https://arxiv.org/abs/2102.04664)] [[data]]                                                                                                        |
| 2021-05 | ACL/IJCNLP 2021                      | CoSQA                | 21K       | Python                                                  | "CoSQA: 20,000+ Web Queries for Code Search and Question Answering" [[paper](https://arxiv.org/abs/2105.13239)] [[data](https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-WebQuery)]                                        |
| 2024-03 | arXiv                                | ProCQA               | 5.2M      | C, C++, Java, Python, Ruby, Lisp, JS, C#, Go, Rust, PHP | "ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search" [[paper](https://arxiv.org/abs/2403.16702)] [[data](https://github.com/jordane95/procqa)]                                                            |
| 2024-06 | arXiv                                | CoSQA+               | 109K      | Python                                                  | "CoSQA+: Enhancing Code Search Dataset with Matching Code" [[paper](https://arxiv.org/abs/2406.11589)] [[data](https://github.com/DeepSoftwareAnalytics/CoSQA_Plus)]                                                                                |
| 2024-07 | arXiv                                | CoIR                 | ~2M       | 14                                                      | "CoIR: A Comprehensive Benchmark for Code Information Retrieval Models" [[paper](https://arxiv.org/abs/2407.02883)] [[data](https://github.com/CoIR-team/coir)]                                                                                     |
| 2024-08 | arXiv                                | SeqCoBench           | 14.5K     | Python                                                  | "What can Large Language Models Capture about Code Functional Equivalence?" [[paper](https://arxiv.org/abs/2408.11081)]                                                                                                                             |

#### Type Inference

| Date    | Venue         | Benchmark                    | Size      | Language   | Source                                                                                                                                                                                                                           |
| ------- | ------------- | ---------------------------- | --------- | ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 2019-12 | ESEC/FSE 2020 | TypeWriter OSS               | 208K      | Python     | "TypeWriter: Neural Type Prediction with Search-based Validation" [[paper](https://arxiv.org/abs/1912.03768)] [[data](http://software-lab.org/projects/TypeWriter/data.tar.gz)]                                                  |
| 2020-04 | PLDI 2020     | Typilus                      | 252K      | Python     | "Typilus: Neural Type Hints" [[paper](https://arxiv.org/abs/2004.10657)] [[data](https://github.com/typilus/typilus)]                                                                                                            |
| 2020-04 | ICLR 2020     | LambdaNet                    | 300 \*    | TypeScript | "LambdaNet: Probabilistic Type Inference using Graph Neural Networks" [[paper](https://arxiv.org/abs/2005.02161)] [[data](https://github.com/MrVPlusOne/LambdaNet)]                                                              |
| 2021-04 | MSR 2021      | ManyTypes4Py                 | 869K      | Python     | "ManyTypes4Py: A Benchmark Python Dataset for Machine Learning-based Type Inference" [[paper](https://arxiv.org/abs/2104.04706)] [[data](https://github.com/saltudelft/many-types-4-py-dataset)]                                 |
| 2022-10 | MSR 2022      | ManyTypes4TypeScript         | 9.1M      | TypeScript | "ManyTypes4TypeScript: a comprehensive TypeScript dataset for sequence-based type inference" [[paper](https://dl.acm.org/doi/10.1145/3524842.3528507)] [[data](https://huggingface.co/datasets/kevinjesse/ManyTypes4TypeScript)] |
| 2023-02 | ECOOP 2023    | TypeWeaver                   | 513 \*    | TypeScript | "Do Machine Learning Models Produce TypeScript Types That Type Check?" [[paper](https://arxiv.org/abs/2302.12163)] [[data](https://zenodo.org/records/7662708)]                                                                  |
| 2023-03 | ICLR 2023     | BetterTypes4Py/InferTypes4Py | 608K/4.6K | Python     | "TypeT5: Seq2seq Type Inference using Static Analysis" [[paper](https://arxiv.org/abs/2303.09564)] [[data](https://github.com/utopia-group/TypeT5)]                                                                              |
| 2023-05 | arXiv         | OpenTau                      | 744 \*    | TypeScript | "Type Prediction With Program Decomposition and Fill-in-the-Type Training" [[paper](https://arxiv.org/abs/2305.17145)] [[data](https://github.com/GammaTauAI/opentau)]                                                           |

\* These are project counts.

#### Commit Message Generation

- "On the Evaluation of Commit Message Generation Models: An Experimental Study", 2021-07, ICSME 2021, [[paper](https://arxiv.org/abs/2107.05373)]

| Date    | Venue                            | Benchmark       | Size       | Language                        | Source                                                                                                                                                                                                        |
| ------- | -------------------------------- | --------------- | ---------- | ------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 2017-03 | ICPC 2017                        | unnamed         | 509K       | Java                            | "Towards Automatic Generation of Short Summaries of Commits" [[paper](https://arxiv.org/abs/1703.09603)] [[data](https://notredame.app.box.com/s/wghwpw46x41nu6iulm6qi8j42finuxni)]                           |
| 2017-04 | ACL 2017                         | CommitGen       | 153K       | Python, JS, C++, Java           | "A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes" [[paper](https://arxiv.org/abs/1704.04856)] [[data](https://github.com/epochx/commitgen)]                       |
| 2017-08 | ASE 2017                         | CommitGen       | 32K/75K \* | Java                            | "Automatically Generating Commit Messages from Diffs using Neural Machine Translation" [[paper](https://arxiv.org/abs/1708.09492)] [[data](https://notredame.app.box.com/s/wghwpw46x41nu6iulm6qi8j42finuxni)] |
| 2018-09 | ASE 2018                         | NNGen           | 27K        | Java                            | "Neural-machine-translation-based commit message generation: how far are we?" [[paper](https://dl.acm.org/doi/10.1145/3238147.3238190)] [[data](https://github.com/Tbabm/nngen)]                              |
| 2019-05 | MSR 2019                         | PtrGNCMsg       | 64.9K      | Java                            | "Generating commit messages from diffs using pointer-generator network" [[paper](https://dl.acm.org/doi/10.1109/MSR.2019.00056)] [[data(https://zenodo.org/records/2593787)]]                                 |
| 2019-08 | IJCAI 2019                       | CoDiSum         | 90.7K      | Java                            | "Commit message generation for source code changes" [[paper](https://www.ijcai.org/proceedings/2019/552)] [[data](https://github.com/SoftWiser-group/CoDiSum)]                                                |
| 2019-12 | IEEE Trans. Software Eng.        | ATOM            | 160K       | Java                            | "ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Ranking" [[paper](https://arxiv.org/abs/1912.02972)] [[data](https://zenodo.org/records/4077754#.X4K2b5MzZTY)]                      |
| 2021-05 | arXiv                            | CommitBERT      | 346K       | Python, PHP, Go, Java, JS, Ruby | "CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model" [[paper](https://arxiv.org/abs/2105.14242)] [[data](https://github.com/graykode/commit-autosuggestions)]                 |
| 2021-07 | ICSME 2021                       | MCMD            | 2.25M      | Java, C#, C++, Python, JS       | "On the Evaluation of Commit Message Generation Models: An Experimental Study" [[paper](https://arxiv.org/abs/2107.05373)] [[data](https://github.com/DeepSoftwareAnalytics/CommitMsgEmpirical)]              |
| 2021-07 | ACM Trans. Softw. Eng. Methodol. | CoRec           | 107K       | Java                            | "Context-aware Retrieval-based Deep Commit Message Generation" [[paper](https://dl.acm.org/doi/10.1145/3464689)] [[data](https://zenodo.org/records/3828107)]                                                 |
| 2023-07 | ASE 2023                         | ExGroFi         | 19263      | Java                            | "Delving into Commit-Issue Correlation to Enhance Commit Message Generation Models" [[paper](https://arxiv.org/abs/2308.00147)] [[data](https://zenodo.org/records/7885748#.ZFDT5IJBxD8)]                     |
| 2023-08 | ASE 2023                         | CommitChronicle | 10.7M      | 20                              | "From Commit Message Generation to History-Aware Commit Message Completion" [[paper](https://arxiv.org/abs/2308.07655)] [[data](https://github.com/JetBrains-Research/commit_message_generation)]             |

\* with/without verb-direct object filter

#### Repo-Level Coding

| Date    | Venue             | Benchmark      | Size                   | Language                     | Source                                                                                                                                                                                                      |
| ------- | ----------------- | -------------- | ---------------------- | ---------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 2023-03 | arXiv             | RepoEval       | 1600/1600/373 \*       | Python                       | "RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation" [[paper](https://arxiv.org/abs/2303.12570)] [[data](https://github.com/microsoft/CodeT/tree/main/RepoCoder)]       |
| 2023-06 | ICLR 2024         | RepoBench      | 890K/9M/43K $^\dagger$ | Python, Java                 | "RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems" [[paper](https://arxiv.org/abs/2306.03091)] [[data](https://github.com/Leolty/repobench)]                                           |
| 2023-06 | NeurIPS 2023      | PragmaticCode  | 880 \*\*               | Java                         | "Guiding Language Models of Code with Global Context using Monitors" [[paper](https://arxiv.org/abs/2306.10763)] [[data](https://github.com/microsoft/monitors4codegen)]                                    |
| 2023-06 | arXiv             | Stack-Repo     | 816K                   | Java                         | "RepoFusion: Training Code Models to Understand Your Repository" [[paper](https://arxiv.org/abs/2306.10998)] [[data](https://huggingface.co/RepoFusion)]                                                    |
| 2023-09 | ISMB 2024         | BioCoder       | 2269/460/460           | Python, Java                 | "BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models" [[paper](https://arxiv.org/abs/2308.16458)] [[data](https://huggingface.co/datasets/lilbillbiscuit/biocoder_public)]  |
| 2023-09 | arXiv             | CodePlan       | 645/21 $^\ddagger$     | C#/Python $^\ddagger$        | "CodePlan: Repository-level Coding using LLMs and Planning" [[paper](https://arxiv.org/abs/2309.12499)] [[data](https://aka.ms/CodePlan)]                                                                   |
| 2023-10 | arXiv             | SWE-Bench      | 2294                   | Python                       | "SWE-bench: Can Language Models Resolve Real-World GitHub Issues?" [[paper](https://arxiv.org/abs/2310.06770)] [[data](https://www.swebench.com/)]                                                          |
| 2023-10 | arXiv             | CrossCodeEval  | 9928                   | Python, Java, TypeScript, C# | "CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion" [[paper](https://arxiv.org/abs/2310.11248)] [[data](https://crosscodeeval.github.io/)]                                 |
| 2024-03 | arXiv             | EvoCodeBench   | 275                    | Python                       | "EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories" [[paper](https://arxiv.org/abs/2404.00599)] [[data](https://github.com/seketeam/EvoCodeBench)]              |
| 2024-05 | ACL 2024 Findings | DevEval        | 1874                   | Python                       | "DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories" [[paper](https://arxiv.org/abs/2405.19856)] [[data](https://github.com/seketeam/DevEval)]               |
| 2024-06 | arXiv             | JavaBench      | 389                    | Java                         | "Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench" [[paper](https://arxiv.org/abs/2406.12902)] [[data](https://github.com/java-bench/JavaBench)] |
| 2024-06 | arXiv             | HumanEvo       | 200/200                | Python/Java                  | "Towards more realistic evaluation of LLM-based code generation: an experimental study and beyond" [[paper](https://arxiv.org/abs/2406.06918)] [[data](https://github.com/DeepSoftwareAnalytics/EvoEval)]   |
| 2024-06 | arXiv             | RepoExec       | 355                    | Python                       | "REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark" [[paper](https://arxiv.org/abs/2406.11927)]                                                                               |
| 2024-06 | arXiv             | RES-Q          | 100                    | Python, JavaScript           | "RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository Scale" [[paper](https://arxiv.org/abs/2406.16801)] [[data](https://github.com/Qurrent-AI/RES-Q)]                             |
| 2024-08 | arXiv             | SWE-bench-java | 91                     | Java                         | "SWE-bench-java: A GitHub Issue Resolving Benchmark for Java" [[paper](https://arxiv.org/abs/2408.14354)] [[data](https://github.com/multi-swe-bench/multi-swe-bench.github.io)]                            |
| 2024-10 | arXiv             | Codev-Bench    | 296                    | Python                       | "Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?" [[paper](https://arxiv.org/abs/2410.01353)] [[data](https://github.com/LingmaTongyi/Codev-Bench)]                                  |
| 2024-10 | arXiv             | SWE-bench M    | 617                    | JavaScript                   | "SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?" [[paper](https://arxiv.org/abs/2410.03859)] [[data](https://www.swebench.com/multimodal)]                                      |
| 2024-10 | arXiv             | SWE-Bench+     | 548                    | Python                       | "SWE-Bench+: Enhanced Coding Benchmark for LLMs" [[paper](https://arxiv.org/abs/2410.06992)] [[data](https://zenodo.org/records/13879453)]                                                                  |
| 2024-10 | arXiv             | DA-Code        | 500                    | Python, Bash, SQL            | "DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models" [[paper](https://arxiv.org/abs/2410.07331)] [[data](https://github.com/yiyihum/da-code)]                                  |
| 2024-10 | arXiv             | RepoCod        | 980                    | Python                       | "Can Language Models Replace Programmers? REPOCOD Says 'Not Yet'" [[paper](https://arxiv.org/abs/2410.21647)]                                                                                               |
| 2024-10 | arXiv             | M2rc-Eval      | 5993 repos             | 18                           | "M2rc-Eval: Massively Multilingual Repository-level Code Completion Evaluation" [[paper](https://arxiv.org/abs/2410.21157)] [[data](https://github.com/M2RC-Eval-Team/M2RC-Eval)]

\*Line Completion/API Invocation Completion/Function Completion

$^\dagger$ Retrieval/Completion/Pipeline

\*\* File count

$^\ddagger$ Migration/Temporal Edit

# Security LLMs
Comming soon.

# Log LLMs
## Datasets
- [CUHK] [Loghub](https://github.com/logpai/loghub) - A collection of log data from various systems and applications provided by the Chinese University of Hong Kong.
- [CUHK] [LogPub](https://github.com/logpai/LogPub) - A library released by the Chinese University of Hong Kong containing publicly available log data from multiple sources.
- [CUHK] [bughub](https://github.com/logpai/bughub) - A collection of vulnerability reports from various software projects provided by the Chinese University of Hong Kong.
- [NAB] [The Numenta Anomaly Benchmark(NAB)](https://github.com/numenta/NAB) - An anomaly detection benchmark dataset created by Numenta.
- [Microsoft Azure] [Azure Public Dataset](https://github.com/Azure/AzurePublicDataset) - Public datasets offered by Microsoft Azure, a cloud services platform.
- [Google] [Cluster Traces](https://github.com/google/cluster-data) - Cluster trace data published by Google reflecting workload situations in their production environment.
- [Backblaze] [Hard Drive Dataset](https://www.backblaze.com/b2/hard-drive-test-data.html) - A hard drive test dataset provided by Backblaze.
- [Baidu] [SMART Dataset of PAKDD CUP 2020](https://pan.baidu.com/share/link?shareid=189977&uk=4278294944#list/path=%2FS.M.A.R.T.dataset) - SMART attribute dataset for hard drive monitoring provided by Baidu for the PAKDD CUP 2020 competition.
- [Alibaba] [SSD SMART logs and failure data](https://github.com/alibaba-edu/dcbrain/tree/master/ssd_open_data) - SSD SMART logs and failure data offered by Alibaba.
- [Alibaba] [Alibaba Cluster Trace Program](https://github.com/alibaba/clusterdata) - Alibaba's cluster trace program recording the running status of clusters.
- [Tsinghua] [AIOps Challenge Dataset](https://competition.aiops-challenge.com/home/competition/) - An AI operations challenge dataset provided by Tsinghua University.
- [Yahoo] [A Labeled Anomaly Detection Dataset](https://webscope.sandbox.yahoo.com/catalog.php?datatype=s&did=70) - A labeled anomaly detection dataset provided by Yahoo.
- [Mike Sconzo] [Security Repo](https://www.secrepo.com/) - A repository of security-related data created by Mike Sconzo.
- [AIT] [AIT](https://zenodo.org/records/5789064#.YkFnZWJBxhE) -- [AIT aecid](https://github.com/ait-aecid/kyoushi-environment) - A dataset for information technology security training and simulation environments provided by Austria's Institute of Technology.

## Benchmarks
- [arXiv '22] [Constructing Large-Scale Real-World Benchmark Datasets for AIOps](https://arxiv.org/abs/2208.03938) - Discusses the methods and challenges of constructing large-scale real-world AIOps benchmark datasets.
- [ASPLOS '19] [An Open-Source Benchmark Suite for Microservices and Their Hardware-Software Implications for Cloud and Edge Systems](https://dl.acm.org/doi/10.1145/3297858.3304013) - An open-source benchmark suite for microservices presented at the 2019 ASPLOS conference, exploring implications for cloud and edge systems.
- [Google Cloud] [Online Boutique (A microservices demo application)](https://github.com/GoogleCloudPlatform/microservices-demo) - A microservices demo application named "Online Boutique" provided by Google Cloud Platform, showcasing best practices in cloud-native application development and management.
- [Fudan] [Train Ticket (A benchmark microservice system)](https://github.com/FudanSELab/train-ticket) - A benchmark microservice system named "Train Ticket" developed by Fudan University's research team for studying issues in microservice architecture.
- [Weaveworks] [Sock Shop (A microservices demo application)](https://microservices-demo.github.io/) - A microservices demo application named "Sock Shop" provided by Weaveworks, designed to demonstrate how to build and deploy microservices architecture applications.

## Root Cause Analysis
- [**ASE 2024**] [The Potential of One-Shot Failure Root Cause Analysis: Collaboration of the Large Language Model and Small Classifier](https://dl.acm.org/doi/10.1145/3691620.3695475). Explores one-shot failure RCA with collaborative techniques between LLMs and classifiers.
- [**FSE Industry 2024**] [LM-PACE: Confidence Estimation by Large Language Models for Effective Root Causing of Cloud Incidents](https://arxiv.org/pdf/2309.05833.pdf). LM-PACE focuses on the role of confidence estimation in root cause analysis by employing large language models.
- [**FSE Industry 2024**] [Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4](https://arxiv.org/pdf/2401.13810v1.pdf). Discusses automated RCA for cloud incidents utilizing GPL4 with in-context learning techniques.
- [**FSE Industry 2024**] [X-lifecycle Learning for Cloud Incident Management using LLMs](https://arxiv.org/pdf/2404.03662). Examines x-lifecycle learning as a method for cloud incident management through LLMs.
- [**FSE Industry 2024**] [Exploring LLM-based Agents for Root Cause Analysis](https://arxiv.org/pdf/2403.04123). Investigates the potential for LLM-based agents in performing RCA.
- [**ICSE 2024**] [Xpert: Empowering Incident Management with Query Recommendations via Large Language Models](https://arxiv.org/pdf/2312.11988). Xpert leverages the capabilities of large language models to enhance incident management through recommended diagnostic queries.
- [**ICSE 2023**] [Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models](https://arxiv.org/pdf/2301.03797.pdf). A study on LLM usage in recommending RCA and mitigation steps for cloud incidents.
- [**Preprint 2023**] [RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models](https://arxiv.org/pdf/2310.16340.pdf). RCAgent investigates autonomous RCAs augmented by LLM tools.
- [**EMNLP 2024(Findings)**] [mABC: Multi-Agent Blockchain-inspired Collaboration for Root Cause Analysis in Micro-Services Architecture](https://arxiv.org/pdf/2404.12135). mABC employs multi-agent blockchain collaborations for effective RCA in microservices.
- [Automatic Root Cause Analysis via Large Language Models for Cloud Incidents](https://arxiv.org/abs/2305.15778) - Introduces the use of large language models for automatically analyzing root causes of cloud incidents.
- [LLM As DBA](https://arxiv.org/abs/2308.05481) -- [project](https://github.com/TsinghuaDatabaseGroup/DB-GPT) - Explores using large language models as database administrators (DBAs) to assist with database-related tasks and improve efficiency and performance.
- [A Holistic View of AI-driven Network Incident Management](https://www.microsoft.com/en-us/research/uploads/prod/2023/09/LLM4IcMs___HotNets__23-6.pdf) - A comprehensive perspective presented by Microsoft Research on AI-driven network incident management, describing how AI can improve incident management processes.
- [Assess and Summarize: Improve Outage Understanding with Large Language Models](https://arxiv.org/abs/2305.18084) - Uses large language models to assess and summarize information, improving understanding of outage (interruption) events.
- [PACE-LM: Prompting and Augmentation for Calibrated Confidence Estimation with GPT-4 in Cloud Incident Root Cause Analysis](https://arxiv.org/abs/2309.05833) - Proposes prompting and augmentation techniques for confidence calibration of GPT-4 in cloud incident root cause analysis.

## Log/AIOps LLMs
- [**EMNLP Industry 2023**] [Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering](https://aclanthology.org/2023.emnlp-industry.29.pdf) [[project](https://github.com/ModelInteraction/MSQA)]. MSQA aims to enhance LLM capabilities for domain-specific question-answering in industrial environments.
- [**ICLR 2024**] [OWL: A Large Language Model for IT Operations](https://openreview.net/pdf?id=SZOQ9RKYJu) [[project](https://github.com/HC-Guo/Owl)]. OWL is designed specifically for IT operations, incorporating instruction tuning for improved performance.
- [**SANER 2024**] [Gloss: Guiding Large Language Models to Answer Questions from System Logs](https://ieeexplore.ieee.org/abstract/document/10589781). Gloss explores strategies for guiding LLMs to provide answers to queries derived from system logs.
- [**Preprint 2023**] [An Empirical Study of NetOps Capability of Pre-Trained Large Language Models](https://arxiv.org/pdf/2309.05557.pdf) [[project](https://huggingface.co/datasets/NASP/neteval-exam)]. NetEval empirically assesses NetOps capabilities of pre-trained LLMs.
- [**Preprint 2023**] [OpsEval: A Comprehensive Task-Oriented AIOps Benchmark for Large Language Models](https://arxiv.org/pdf/2310.07637.pdf) [[project](https://opseval.cstcloud.cn/content/home#home)]. OpsEval offers a task-oriented AIOps benchmark evaluation for large language models.
- [codefuse-ai/codefuse-devops-eval](https://github.com/codefuse-ai/codefuse-devops-eval) -- paper coming soon? - An evaluation tool for development operations released by CodeFuse AI, with related paper coming soon.

## Log Parsing

- [**ISSTA 2024**] [A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We?](https://arxiv.org/pdf/2308.10828). Conducts a comprehensive evaluation of current log parsing techniques, analyzing their performance.
- [**ASE-NIER 2023**] [Log Parsing: How Far Can ChatGPT Go?](https://arxiv.org/pdf/2306.01590.pdf) [[project](https://github.com/LogIntelligence/log-analytics-chatgpt)]. Explores the capabilities and limitations of ChatGPT in the context of log parsing.
- [**ICSE 2024**] [DivLog: Log Parsing with Prompt Enhanced In-Context Learning](https://arxiv.org/pdf/2307.09950.pdf). DivLog investigates log parsing strategies employing in-context learning improved through specific prompt designs.
- [**ICSE 2024**] [LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing](https://arxiv.org/pdf/2404.18001). LLMParser explores log parsing using LLMs with approaches such as prompting and finetuning.
- [**FSE 2024**] [LILAC: Log Parsing using LLMs with Adaptive Parsing Cache](https://arxiv.org/pdf/2310.01796.pdf). LILAC uses adaptive parsing caches in LLMs for enhanced log parsing efficiency.
- [**ICPC 2024**] [Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies](https://arxiv.org/pdf/2308.07610.pdf). LogPrompt emphasizes interpretable log analysis leveraging strategic prompt frameworks with LLMs.
- [**Preprint 2023**] [LEMUR : Log Parsing with Entropy Sampling and Chain-of-Thought Merging](https://arxiv.org/abs/2402.18205). LEMUR introduces entropy sampling and chain-of-thought techniques for improved log parsing accuracy.
- [**Preprint 2024**] [Stronger, Cheaper and Demonstration-Free Log Parsing with LLMs](https://arxiv.org/pdf/2406.06156). LogBatcher proposes cost-effective and highly efficient log parsing methods without extensive demonstrations.
- [LLMParser: A LLM-based Log Parsing Framework](https://arxiv.org/abs/2310.01796) - Proposes a log parsing framework based on large language models.
- [**Preprint 2024**] [LUNAR: Unsupervised LLM-based Log Parsing](https://arxiv.org/pdf/2406.07174). LUNAR presents unsupervised techniques in LLM-based log parsing applications.
- [**Preprint 2024**] [Log Parsing with Self-Generated In-Context Learning and Self-Correction](https://arxiv.org/pdf/2406.03376). AdaParser elaborates on self-generated learning strategies and self-correction mechanisms in log parsing.
- [**Preprint 2024**] [OpenLogParser: Unsupervised Parsing with Open-Source Large Language Models](https://www.arxiv.org/pdf/2408.01585). OpenLogParser offers an unsupervised approach leveraging open-source LLMs for parsing logs.

## Log Anomaly Detection

- [**ICPC 2024**] [Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies](https://arxiv.org/pdf/2308.07610.pdf). LogPrompt contributes to interpretable online log analysis frameworks, focusing on anomaly detection strategies.
- [**Preprint 2023**] [Log-based Anomaly Detection based on EVT Theory with feedback](https://arxiv.org/pdf/2306.05032.pdf). ScaleAD integrates EVT theory with feedback mechanisms for log anomaly detection enhancements.
- [**Preprint 2023**] [LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection](https://arxiv.org/pdf/2309.01189.pdf). Investigates the potential of using ChatGPT in the detection of anomalies within log data, integrating prompting strategies.
- [**Preprint 2024**] [RAGLog: Log Anomaly Detection using Retrieval Augmented Generation](https://arxiv.org/pdf/2311.05261.pdf). RAGLog develops retrieval-augmented generation strategies for log anomaly detection.
- [**Preprint 2024**] [Anomaly Detection on Unstable Logs with GPT Models](https://arxiv.org/pdf/2406.07467). Discusses anomaly detection on unstable log environments utilising the GPT model and finetuning enhancements.

## Logging Statement Generation 
- [**ICSE 2024**] [UniLog: Automatic Logging via LLM and In-Context Learning](https://www.computer.org/csdl/proceedings-article/icse/2024/021700a129/1RLIWpCelqg). UniLog focuses on automatic logging statement generation using LLMs and in-context learning methods.
- [**FSE 2024**] [Go Static: Contextualized Logging Statement Generation](https://arxiv.org/pdf/2402.12958.pdf). SCLogger applies contextual strategies for generating logging statements, utilizing LLM capabilities.
- [**TSE 2024**] [Exploring the Effectiveness of LLMs in Automated Logging Generation: An Empirical Study](https://arxiv.org/pdf/2307.05950.pdf) [[project](https://github.com/LogStudySE/LogStudy)]. An empirical study assessing how large language models can automate and improve logging statement generation processes.

## Survey
- [**Preprint 2024**] [A Survey of AIOps for Failure Management in the Era of Large Language Models](https://arxiv.org/pdf/2406.11213). A comprehensive survey exploring the application of AIOps in failure management enhanced by large language models.
- [**Preprint 2024**] [AI Assistants for Incident Lifecycle in a Microservice Environment: A Systematic Literature Review](https://arxiv.org/pdf/2410.04334). A systematic literature review on AI assistants within microservice environments, focusing on the incident lifecycle.
- [arXiv '23] [AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges](https://arxiv.org/abs/2304.04661) - A review of AI for IT operations (AIOps) on cloud platforms, exploring current research opportunities and challenges.
- [CSUR '22] [Anomaly Detection and Failure Root Cause Analysis in (Micro) Service-Based Cloud Applications: A Survey](https://dl.acm.org/doi/full/10.1145/3501297) - A survey of anomaly detection and failure root cause analysis techniques in service-based cloud applications.
- [ASE '22] [Going through the Life Cycle of Faults in Clouds: Guidelines on Fault Handling](https://github.com/IntelligentDDS/Post-mortems-Analysis) - Provides guidelines on the lifecycle handling of faults in cloud computing environments.
- [arXiv '21] [Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection](https://arxiv.org/abs/2107.05908) - Reports on the practical experience of using deep learning-based system log analysis techniques for anomaly detection.
- [CSUR '21] [A Survey on Automated Log Analysis for Reliability Engineering](https://arxiv.org/abs/2009.07237) - Studies the application of automated log analysis techniques in reliability engineering.
- [ESEC/FSE '20] [Towards intelligent incident management: why we need it and how we make it](https://dl.acm.org/doi/abs/10.1145/3368089.3417055) - Discusses why intelligent incident management is needed and how to achieve it.
- [arXiv '20] [A Systematic Mapping Study in AIOps](https://arxiv.org/abs/2012.09108) - A systematic mapping study of the AIOps field, summarizing research trends and directions.
- [ICSE '19] [AIOps: Real-World Challenges and Research Innovations](https://ieeexplore.ieee.org/document/8802836) - Introduces the real-world challenges and research innovations in AIOps.
- [HotOS '19] [What bugs cause production cloud incidents?](https://dl.acm.org/doi/10.1145/3317550.3321438) - Explores what kinds of defects can cause incidents in cloud production environments.
- [ISSRE '16] [Experience Report: System Log Analysis for Anomaly Detection](https://ieeexplore.ieee.org/document/7774521) - Shares an experience report on using system log analysis for anomaly detection.
- [ASE '13] [Software analytics for incident management of online services: An experience report](https://ieeexplore.ieee.org/document/6693105) - Shares practical experience report on using software analytics for incident management of online services.

## Resources
- [VMware] [Proactive Incident and Problem Management](https://docplayer.net/8854482-Proactive-incident-and-problem-management.html) - A guide provided by VMware on how to proactively manage incidents and problems, aimed at helping enterprises prevent and reduce issues and incidents in IT operations.
- [Awesome Open Source] [Aiops Handbook](https://awesomeopensource.com/project/chenryn/aiops-handbook) - An open-source project compiling AIOps-related resources and tools, serving as a practical handbook for implementing AI in operations.
- [GREATOPS] [《Enterprise AIOps Implementation Recommendation》White Paper](https://pic.huodongjia.com/ganhuodocs/2018-04-16/1523873064.74.pdf) - A white paper released by the GREATOPS community, providing suggestions and solutions for enterprise-level AIOps implementation.
- [rob-med] [awesome-TS-anomaly-detection](https://github.com/rob-med/awesome-TS-anomaly-detection) - A curated list on GitHub gathering various resources, libraries, frameworks, and academic papers in the field of time series anomaly detection.

## WeChat Official Accounts
- Tencent Weaving Cloud (Tencent) - An official WeChat public account operated by Tencent, dedicated to sharing Tencent's technology and experience in cloud computing, data center management, and intelligent operations.
- Intelligent Operations Frontline (Tsinghua Pei Dan Team) - The official WeChat public account of Professor Pei Dan's team at Tsinghua University, publishing the latest research progress on intelligent operations and relevant news.
- AIOps Intelligent Operations (Baidu) - Baidu's official WeChat public account, focusing on the application of artificial intelligence in IT operations and sharing the latest developments and solutions in AIOps.
- Huawei Product Servicability (Huawei) - Huawei's official WeChat public account providing information on Huawei product operations, services, and servicability capabilities.

## Other Influential Repositories
- [https://github.com/logpai](https://github.com/logpai) [active] - Logpai is a GitHub organization dedicated to research on log analysis and providing related resources, including numerous log-related projects and datasets, with active updates.
- [https://github.com/logpai/awesome-log-analysis](https://github.com/logpai/awesome-log-analysis) [active] - Awesome-log-analysis is a list curated by the logpai organization compiling tools, libraries, and resources for log analysis, continuously updated and expanded.
- [https://github.com/chenryn/aiops-handbook](https://github.com/chenryn/aiops-handbook) [active] - Aiops-handbook is an open-source manual aiming to compile learning resources, tools, and practical guides in the AIOps field, with ongoing maintenance and updates.
- [https://github.com/OpsPAI/awesome-AIOps](https://github.com/OpsPAI/awesome-AIOps) [active] - Awesome-AIOps collects excellent resources, tools, papers, and communities related to AIOps, aiming to provide references for AIOps researchers and practitioners.
- [https://github.com/zhuyiche/awesome-anomaly-detection](https://github.com/zhuyiche/awesome-anomaly-detection) [not active] - Awesome-anomaly-detection is a resource list about the field of anomaly detection, but it is no longer actively updated.
- [https://github.com/linjinjin123/awesome-AIOps](https://github.com/linjinjin123/awesome-AIOps) [not active] - Awesome-AIOps is another compilation of resources for AIOps, including related papers, tools, and frameworks, but this repository is not actively maintained.

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