SWE-smith: Scaling Data for Software Engineering Agents
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.
681b80bd2552f1531d8be2b7
John Yang
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2025-05-08T08:59:16.662Z
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John
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Kilian Leret
681b80bd2552f1531d8be2b9
Carlos E. Jimenez
681b80bd2552f1531d8be2ba
Alexander Wettig
681b80bd2552f1531d8be2bb
Kabir Khandpur
681b80bd2552f1531d8be2bc
Yanzhe Zhang
claimed_verified
2025-08-21T09:16:47.416Z
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Yanzhe Zhang
user
zyanzhe
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Binyuan Hui
681b80bd2552f1531d8be2be
Ofir Press
681b80bd2552f1531d8be2bf
Ludwig Schmidt
681b80bd2552f1531d8be2c0
Diyi Yang
SWE-smith: Scaling Data for Software Engineering Agents
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.
SWE-smith automates the generation of large-scale software engineering training data and improves the performance of language models on automated software engineering tasks.
Language Models software engineering training data GitHub repositories execution environment task instances SWE-smith SWE-agent-LM-32B SWE-bench Verified benchmark automated software engineering