SWE-smith: Scaling Data for Software Engineering Agents
Authors: name: John Yang, name: Kilian Leret, name: Carlos E. Jimenez, name: Alexander Wettig, name: Kabir Khandpur, name: Yanzhe Zhang, name: Binyuan Hui, name: Ofir Press, name: Ludwig Schmidt, name: Diyi Yang

Despite recent progress in Language Models (LMs) for software engineering,
collecting training data remains a significant pain point. Existing datasets
are small, with at most 1,000s of training instances from 11 or fewer GitHub
repositories. The procedures to curate such datasets are often complex,
necessitating hundreds of hours of human labor; companion execution
environments also take up several terabytes of storage, severely limiting their
scalability and usability. To address this pain point, we introduce SWE-smith,
a novel pipeline for generating software engineering training data at scale.
Given any Python codebase, SWE-smith constructs a corresponding execution
environment, then automatically synthesizes 100s to 1,000s of task instances
that break existing test(s) in the codebase. Using SWE-smith, we create a
dataset of 50k instances sourced from 128 GitHub repositories, an order of
magnitude larger than all previous works. We train SWE-agent-LM-32B, achieving
40.2% Pass@1 resolve rate on the SWE-bench Verified benchmark, state of the art
among open source models. We open source SWE-smith (collection procedure, task
instances, trajectories, models) to lower the barrier of entry for research in
LM systems for automated software engineering. All assets available at
https://swesmith.com.