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Models and Tool Kits for Web3 Software Engineering

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# Web3 Software Engineering Toolkit

🤩 We are building a comprehensive toolkit for **Web3 Software Engineering**!

**🚀 Website:** <https://web3se-lab.github.io>

**🤗 Dataset:**: <https://huggingface.co/datasets/web3se/smart-contract-intent-vul-dataset>

**🤗 Models:**:

- [SmartBERT V2](https://huggingface.co/web3se/SmartBERT-v2)
- [SmartIntent V2](https://github.com/web3se-lab/web3-sekit/releases/tag/V2)

## Toolkit

**Tags**: Web3, AI, Software Engineering

- 🕸️ Web3 Data Crawler
- 🤔 Web3 Data Collection, Labelling, and Analysis
- 🚀 Build, Train, and Evaluate AI Models
- 👀 Data Visualization
- 🤝 Blockchain Network Interactions

### Project-1: SmartBERT

![SmartBERT API](https://raw.githubusercontent.com/web3se-lab/web3-sekit/main/imgs/smartbert.png)

**Task:** Learn representations from smart contracts and provide a service for smart contract embedding.

**Link:** <https://github.com/web3se-lab/SmartBERT>

**Technical Points:**

- Built with **BERT-based** pre-training models.
- Encode **smart contract** _function-level_ code into embeddings.
- MLM-trained on **16,000+** (V2) real-world smart contracts.
- Serving APIs (POST):
    - [embed](https://web3.devil.ren/smartbert/embed)
    - [tokenize](https://web3.devil.ren/smartbert/tokenize)
    - [code tree](https://web3.devil.ren/smartbert/tree).

### Project-2: SmartIntent

![SmartIntentNN Example](https://raw.githubusercontent.com/web3se-lab/web3-sekit/main/imgs/example.png)

🤩 **SmartIntentNN** is a deep neural network tool powered by [Tensorflow.js](https://github.com/tensorflow/tfjs).

**Task:** Build a deep learning model to detect developers' unsafe intents in smart contracts.

**Website:** <https://web3se-lab.github.io>

**Technical Guide:**

- 🕵️ Click **"Detect My Smart Contract"** to copy and detect your customized smart contract.
- 🚀 Click **"Predict 🚀"** to detect the malicious intents in smart contracts.
- 🌲 Click **"CCTree 🌲"** to view the smart contract code tree.
- 📱 Pages for testing the model online:
    - [Detect](https://web3se-lab.github.io)
    - [Highlight](https://web3se-lab.github.io/highlight/)
    - [Evaluate](https://web3se-lab.github.io/evaluate/)
- ⚙️ Frontend repository: <https://github.com/web3se-lab/web3se-lab.github.io>

**Technical Points:**

- **SmartIntentNN V1** is trained and evaluated on **Tensorflow.js**.
- **SmartIntentNN V1** employs a **Universal Sentence Encoder** to generate smart contract embeddings.
- **SmartIntentNN V1** integrates an **intent highlight model**, trained using **K-means clustering**.
- **SmartIntent V2** integrates **SmartBERT** to embed smart contracts.

## Dataset

We have compiled a comprehensive dataset of **vulnerabilities** and **intents** in smart contracts!

**💽 Access Our Dataset via this API: <https://web3.devil.ren>**

The URL above offers a `GET/POST JSON` API.

You can query data by modifying the `key` parameter in the URL.

Please refer to the guidelines below:

### Smart Contract Intent

<https://web3.devil.ren/data/intent?key=1>

Please iterate over keys: `1, 2, 3, ...`. If an error occurs, skip that key and continue (key++, continue).

Ground truth label distribution for Intent data:

| Intent Type    | Explanation                                                                                                      | Num   |
| -------------- | ---------------------------------------------------------------------------------------------------------------- | ----- |
| Fee            | Arbitrarily changes transaction fees, transferring them to specified wallet addresses.                           | 33268 |
| DisableTrading | Enables or disables trading actions on a smart contract.                                                         | 6617  |
| Blacklist      | Restricts designated users' activities, potentially infringing on fair trade rights.                             | 4729  |
| Reflection     | Redistributes taxes from transactions to holders based on their holdings, attracting users to buy native tokens. | 46452 |
| MaxTX          | Limits the maximum number or volume of transactions.                                                             | 17043 |
| Mint           | Issues new tokens, either unlimited or controlled.                                                               | 10572 |
| Honeypot       | Traps user-provided funds under the guise of leaking funds.                                                      | 290   |
| Reward         | Rewards users with crypto assets to encourage token use, despite possible lack of value.                         | 4178  |
| Rebase         | Adjusts token supply algorithmically to control price.                                                           | 659   |
| MaxSell        | Limits specified users' selling times or amounts to lock liquidity.                                              | 68    |

### Smart Contract Vulnerability

<https://web3.devil.ren/data/vulnerability?key=1>

Please iterate over keys: `1, 2, 3, ...`. If an error occurs, skip that key and continue (key++, continue).

Ground truth label distribution for Vulnerability data:

| Vulnerability Type           | Num |
| ---------------------------- | --- |
| block number dependency (BN) | 116 |
| timestamp dependency (TP)    | 214 |
| dangerous delegatecall (DE)  | 48  |
| ether frozen (EF)            | 57  |
| ether strict equality (SE)   | 45  |
| integer overflow (OF)        | 23  |
| reentrancy (RE)              | 103 |
| unchecked external call (UC) | 113 |

### Models

🤖 Access **SmartIntent** models: <https://github.com/web3se-lab/web3-sekit/releases>

**SmartIntentNN V1**

- Download `v1.zip`, then unzip and move them to `/tf/models/v1/`.
- For **Universal Sentence Encoder**, download <https://tfhub.dev/google/universal-sentence-encoder/4>, then move it to `/tf/models/` and rename the dir as `universal-sentence-encoder`.
- For **K-means intent highlight model**, download `kmeans-model.json`, then move it to `/tf/models/kmeans-model.json`.

**SmartIntent V2**

- Download `v2.zip`, then unzip and move them to `/tf/models/v2/`.
- **SmartBERT** needs to be deployed separately. Model address: <https://huggingface.co/web3se/SmartBERT-v2>.

The structure of directory should be like the following figure:

How to run V1 models in Tensorflow.js?

- To predict: `node tf/v1/use-high-bilstm-x2.js predict 1`
- To evaluate: `node tf/v1/use-high-bilstm-x2.js evaluate`
- To train: `node tf/v1/use-high-bilstm-x2.js train`
- To summary: `node tf/v1/use-high-bilstm-x2.js summary`

How to run V2 models?

- **Tensorflow.js**: Similar to V1, check scripts in `tf/v2`.
- **Python**: Refer to <https://github.com/web3se-lab/SmartIntent-py>.

### Training & Evaluating Settings

See settings in `/tf/v1/model.js`.

The following is the setup of SmartIntentNN (V1).

**Training**

Scope: 1, 10000
Batch: 200
Batch Size: 50
Epoch: 100

**Evaluating**

Scope: 20000, 10000

## Install

Before using this program, you will need to install **nodejs** and **npm** tools first, then you install dependencies.

Our recommended version is Node.js v20+.

```bash
yarn
# or
npm install
```

## Database

We have released the `sql.gz` on Hugging Face.
You can go to the address <https://huggingface.co/datasets/web3se/smart-contract-intent-vul-dataset> to download the complete dataset and import it into MySQL.

If you would like to set up a localhost database, we prepare a `docker-compose.yml` for you.

To start a MySQL docker service locally, try:

```bash
docker-compose -f ./docker/docker-compose.yml
```

To connect to local mysql database, you can create and modify the `.env` as the following content:

```
# DB
DB_DIALECT=mysql
MYSQL_HOST=localhost
MYSQL_USER=web3
MYSQL_PASS=web3
MYSQL_DB=web3

# SMARTBERT
EMBED_API=

# this service port
WEB_PORT=8081

# for CPU mode
TFJS=@tensorflow/tfjs-node
# for GPU mode
# TFJS=@tensorflow/tfjs-node-gpu
```

Then you can test your database connection and create the initial tables:

```bash

# test connect to databases
node ./db/DB.js test

# init tables, if table is not input, default is all the tables
node ./db/DB.js init [table_name]

# drop tables, be careful!
node ./db/DB.js drop [table_name]

```

We use [sequelize](https://sequelize.org/) to manage a database.

For the details of data structures, please refer to `db/Model.js`

To get the entire database backup, please contact us: **devilyouwei@foxmail.com**

## Web

**Serve Web APIs**

```bash
# development mode
yarn dev
# product mode
yarn start
# stop web service
yarn stop
```

**APIs for dataset**

- [data/get](https://web3.devil.ren/data/get)
- [data/intent](https://web3.devil.ren/data/intent)
- [data/vulnerability](https://web3.devil.ren/data/vulnerability)

**SmartBERT APIs**

- [tokenize](https://web3.devil.ren/smartbert/tokenize)
- [embed](https://web3.devil.ren/smartbert/embed)
- [token/get](https://web3.devil.ren/smartbert/tree)

**POST params**

1. `code` smart contract code content (Solidity)
2. `pool` pooling type: `avg`, `max`, `cls`

EXAMPLE: [embed](https://web3.devil.ren/smartbert/embed) is used to convert smart contract code to embedding vectors:

```json
{
    "code": "input solidity code here",
    "pool": "avg"
}
```

## Resource

### DataSource

1. <https://bscscan.com>
2. <https://etherscan.io>
3. <https://tokensniffer.com>
4. <https://bscheck.eu>
5. <https://scamsniper.net>
6. <https://aphd.github.io/smart-corpus>
7. <https://dashboard.tenderly.co/explorer>
8. <https://tools.staysafu.org>
9. <https://github.com/Messi-Q/Smart-Contract-Dataset>

### Dependency

1. [TensorFlow.js](https://js.tensorflow.org/api/latest/)
2. [TensorFlow Hub](https://tfhub.dev/)
3. [Universal Sentence Encoder V4](https://tfhub.dev/google/universal-sentence-encoder/4)

## Paper

```tex
@article{huang2025smart,
  title={Smart Contract Intent Detection with Pre-trained Programming Language Model},
  author={Huang, Youwei and Li, Jianwen and Fang, Sen and Li, Yao and Yang, Peng and Hu, Bin},
  journal={arXiv preprint arXiv:2508.20086},
  year={2025}
}
```

```tex
@inproceedings{huang2025deep,
  title={Deep smart contract intent detection},
  author={Huang, Youwei and Fang, Sen and Li, Jianwen and Hu, Bin and Tao, Jiachun and Zhang, Tao},
  booktitle={2025 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)},
  pages={124--135},
  year={2025},
  organization={IEEE}
}
```

```tex
@article{huang2022smartintentnn,
  title={Smartintentnn: Towards smart contract intent detection},
  author={Huang, Youwei and Fang, Sen and Li, Jianwen and Hu, Bin and Zhang, Tao},
  journal={arXiv preprint arXiv:2211.13670},
  year={2022}
}
```

## Resource

1. <https://bscscan.com>
2. <https://etherscan.io>
3. <https://tokensniffer.com>
4. <https://bscheck.eu>
5. <https://scamsniper.net>
6. <https://aphd.github.io/smart-corpus>
7. <https://dashboard.tenderly.co/explorer>
8. <https://tools.staysafu.org>
9. <https://github.com/Messi-Q/Smart-Contract-Dataset>

## Contributor

- [Youwei Huang](https://www.devil.ren)
- [Sen Fang](https://tomasandersonfang.github.io)

## Acknowledgment

- [Institute of Intelligent Computing Technology, Suzhou, CAS](http://iict.ac.cn/)
- [Macau University of Science and Technology](http://www.must.edu.mo)

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