SWE-Flow: Synthesizing Software Engineering Data in a Test-Driven Manner

TL;DR

SWE-Flow is a framework that automatically synthesizes software engineering data using a test-driven approach, enabling the creation of a large benchmark dataset and improving TDD-based code generation.

Contribution

It introduces SWE-Flow, which infers development steps from unit tests and constructs a dependency graph to generate structured TDD tasks from real-world projects.

Findings

Generated over 18,000 training and test instances from GitHub.

Fine-tuning models on SWE-Flow data enhances TDD-based coding performance.

Released comprehensive datasets and tools for future research.

Abstract

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).