Synthesizing Software Engineering Data in a Test-Driven Manner

We introduce SWE-Flow, a novel data synthesis framework grounded in Test-Driven Development (TDD). Unlike existing software engineering data that rely on human-submitted issues, SWE-Flow automatically infers incremental development steps directly from unit tests, which inherently encapsulate high-level requirements. The core of SWE-Flow is the construction of a Runtime Dependency Graph (RDG), which precisely captures function interactions, enabling the generation of a structured, step-by-step development schedule. At each step, SWE-Flow produces a partial codebase, the corresponding unit tests, and the necessary code modifications, resulting in fully verifiable TDD tasks. With this approach, we generated 16, 061 training instances and 2, 020 test instances from real-world GitHub projects, creating the SWE-Flow-Eval benchmark. Our experiments show that fine-tuning open model on this dataset significantly improves performance in TDD-based coding. To facilitate further research, we release all code, datasets, models, and Docker images at Github.

Authors

• Lei Zhang 0201
• Jiaxi Yang 0004
• Min Yang 0007
• Jian Yang 0003
• Mouxiang Chen
• Jiajun Zhang 0012
• Zeyu Cui
• Binyuan Hui
• Junyang Lin

Keywords

• Large language Model
• Software Engineering
• Test-Driven Development
• Code Agent