SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub
  Issue Resolution
Authors: name: Chengxing Xie, name: Bowen Li, name: Chang Gao, name: He Du, name: Wai Lam, name: Difan Zou, name: Kai Chen

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.