title: SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub
    Issue Resolution
summary: 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.
paper_authors._id: 6780943c53cd2d16be1e9aae
paper_authors.name: Chengxing Xie
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-01-10T03:32:24.510Z
paper_authors._id: 62d00ff8dd7bdfc5e5c553c6
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paper_authors.fullname: chengxing xie
paper_authors.type: user
paper_authors.user: yitianlian
paper_authors._id: 6780943c53cd2d16be1e9aaf
paper_authors.name: Bowen Li
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-01-10T09:31:08.408Z
paper_authors._id: 61cd553d3dd34ba1985e075b
paper_authors.avatarUrl: /avatars/5c39e4ac0892decc3af8e08109469196.svg
paper_authors.fullname: Bowen Li
paper_authors.type: user
paper_authors.user: bowenli
paper_authors._id: 6780943c53cd2d16be1e9ab0
paper_authors.name: Chang Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-10T16:10:27.384Z
paper_authors._id: 65570843c4865c852d541688
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/AEGlyozNi3YGSbdcJLmOL.jpeg
paper_authors.fullname: Chang Gao
paper_authors.type: user
paper_authors.user: changgy
paper_authors._id: 6780943c53cd2d16be1e9ab1
paper_authors.name: He Du
paper_authors._id: 6780943c53cd2d16be1e9ab2
paper_authors.name: Wai Lam
paper_authors._id: 6780943c53cd2d16be1e9ab3
paper_authors.name: Difan Zou
paper_authors._id: 6780943c53cd2d16be1e9ab4
paper_authors.name: Kai Chen
paper_title: SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub
    Issue Resolution
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) SWE-Fixer BM25 LLM model code file retrieval module code editing module patches SWE-Bench Lite Verified benchmarks