SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner
Authors: name: Lei Zhang, name: Jiaxi Yang, name: Min Yang, name: Jian Yang, name: Mouxiang Chen, name: Jiajun Zhang, name: Zeyu Cui, name: Binyuan Hui, name: Junyang Lin

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](https://github.com/Hambaobao/SWE-Flow).