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.

Modern software development often involves writing tests to ensure code works correctly. Inspired by this practice, we created SWE-Flow, a new system that helps machines learn how to build software step by step by following test instructions. Unlike previous methods that depend on manually written issue reports, SWE-Flow learns directly from unit tests, which naturally reflect what the software is supposed to do. At each step, it provides a small piece of code, a test to check it, and suggestions on what needs to be changed—just like how a real developer would work. We used this approach to build a large dataset from real-world projects and showed that it helps AI models do a better job at writing software in a test-driven way. All our tools, data, and code are freely available at https://github.com/Hambaobao/SWE-Flow.