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

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.

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

• Dataset Synthesis Pipeline

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

This repository is licensed under the MIT License. See License for the full text.

If you use SWE-Flow, please cite:

@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}, 
}

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.