SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub
  Issue Resolution
Large Language Models (LLMs) have demonstrated remarkable proficiency across
a variety of complex tasks. One significant application of LLMs is in tackling
software engineering challenges, particularly in resolving real-world tasks on
GitHub by fixing code based on the issues reported by the users. However, many
current approaches rely on proprietary LLMs, which limits reproducibility,
accessibility, and transparency. The critical components of LLMs for addressing
software engineering issues and how their capabilities can be effectively
enhanced remain unclear. To address these challenges, we introduce SWE-Fixer, a
novel open-source LLM designed to effectively and efficiently resolve GitHub
issues. SWE-Fixer comprises two essential modules: a code file retrieval module
and a code editing module. The retrieval module employs BM25 along with a
lightweight LLM model to achieve coarse-to-fine file retrieval. Subsequently,
the code editing module utilizes the other LLM model to generate patches for
the identified files. Then, to mitigate the lack of publicly available
datasets, we compile an extensive dataset that includes 110K GitHub issues
along with their corresponding patches, and train the two modules of SWE-Fixer
separately. We assess our approach on the SWE-Bench Lite and Verified
benchmarks, achieving state-of-the-art performance among open-source models
with scores of 23.3% and 30.2%, respectively. These outcomes highlight the
efficacy of our approach. We will make our model, dataset, and code publicly
available at https://github.com/InternLM/SWE-Fixer.
6780943c53cd2d16be1e9aae
Chengxing Xie
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2025-01-10T03:32:24.510Z
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chengxing xie
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yitianlian
6780943c53cd2d16be1e9aaf
Bowen Li
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2025-01-10T09:31:08.408Z
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Bowen Li
user
bowenli
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Chang Gao
admin_assigned
2025-01-10T16:10:27.384Z
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Chang Gao
user
changgy
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He Du
6780943c53cd2d16be1e9ab2
Wai Lam
6780943c53cd2d16be1e9ab3
Difan Zou
6780943c53cd2d16be1e9ab4
Kai Chen
SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub
  Issue Resolution
Large Language Models (LLMs) have demonstrated remarkable proficiency across
a variety of complex tasks. One significant application of LLMs is in tackling
software engineering challenges, particularly in resolving real-world tasks on
GitHub by fixing code based on the issues reported by the users. However, many
current approaches rely on proprietary LLMs, which limits reproducibility,
accessibility, and transparency. The critical components of LLMs for addressing
software engineering issues and how their capabilities can be effectively
enhanced remain unclear. To address these challenges, we introduce SWE-Fixer, a
novel open-source LLM designed to effectively and efficiently resolve GitHub
issues. SWE-Fixer comprises two essential modules: a code file retrieval module
and a code editing module. The retrieval module employs BM25 along with a
lightweight LLM model to achieve coarse-to-fine file retrieval. Subsequently,
the code editing module utilizes the other LLM model to generate patches for
the identified files. Then, to mitigate the lack of publicly available
datasets, we compile an extensive dataset that includes 110K GitHub issues
along with their corresponding patches, and train the two modules of SWE-Fixer
separately. We assess our approach on the SWE-Bench Lite and Verified
benchmarks, achieving state-of-the-art performance among open-source models
with scores of 23.3% and 30.2%, respectively. These outcomes highlight the
efficacy of our approach. We will make our model, dataset, and code publicly
available at https://github.com/InternLM/SWE-Fixer.
SWE-Fixer is an open-source Large Language Model designed to resolve GitHub issues using two modules: a code file retrieval module that uses BM25 and a lightweight LLM, and a code editing module that generates patches for identified files, achieving state-of-the-art performance on the SWE-Bench Lite and Verified benchmarks.
Large Language Models LLMs GitHub issues BM25 code file retrieval module code editing module SWE-Bench Lite Verified benchmarks