SWE-Flow

KO | EN

gitlite — search

#python #hacktoberfest #react #ai #llm #golang #machine-learning #deep-learning #nodejs #windows #awesome #vue #pytorch #ios

(★ 40)

SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner

SWE-Flow Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner

> Official implementation of the ICML 2025 paper: Synthesizing Software Engineering Data in a Test-Driven Manner

## ✨ Overview

**SWE-Flow** is a data-synthesis framework that turns unit tests into fully-verifiable, incremental development tasks.
It constructs a Runtime Dependency Graph (RDG) to trace function interactions and automatically derives a step-by-step development schedule:

- Partial codebase for each step
- Unit tests that express the high-level requirement
- Minimal code patch needed to make the tests pass

With this pipeline we generated 16,061 training and 2,020 test instances from real-world GitHub projects, forming the **SWE-Flow** Dataset. Fine-tuning open models on this dataset yields significant gains on TDD-oriented coding tasks.

## 🔧 Installation

```bash
git clone https://github.com/Hambaobao/SWE-Flow.git
cd SWE-Flow
pip install -e .
```

## 📚 Documentation

- [Dataset Synthesis Pipeline](./docs/pipeline.md)

## 🤝 Contributing

Contributions are welcome! A detailed [CONTRIBUTING.md](./CONTRIBUTING.md) guideline will be added soon. Feel free to open issues or pull requests in the meantime.

## 📄 License

This repository is licensed under the **MIT** License. See [License](./LICENSE) for the full text.

## 📌 Citation

If you use SWE-Flow, please cite:
```bibtex
@misc{zhang2025sweflow,
      title={SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner}, 
      author={Lei Zhang and Jiaxi Yang and Min Yang and Jian Yang and Mouxiang Chen and Jiajun Zhang and Zeyu Cui and Binyuan Hui and Junyang Lin},
      year={2025},
      eprint={2506.09003},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.09003}, 
}
```

## 🙏 Acknowledgments

Work done during an internship at **Alibaba Qwen**.
We thank the Alibaba Qwen Team and the open-source community for the projects that enabled **SWE-Flow**.

SWE-Flow

SWE-Flow Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

SWE-Flow

SWE-Flow Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README