arxiv:2506.09003

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

Published on Jun 10

· Submitted by

Lemoncoke on Jun 12

Upvote

Authors:

Lei Zhang ,

Abstract

A novel data synthesis framework, SWE-Flow, uses unit tests to automatically infer development steps and generate a structured schedule for Test-Driven Development (TDD), significantly improving the performance of open models fine-tuned on real-world projects.

AI-generated summary

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).

View arXiv page View PDF GitHub repository Add to collection

Community

Lemoncoke

Paper author Paper submitter 4 days ago

•

edited 4 days ago

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-Bench 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.