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SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution
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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 framework 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 model to achieve coarse-to-fine file retrieval. Subsequently, the code editing module utilizes the other model to generate patches for the identified files. 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 models of SWE-Fixer separately. We assess our approach on the SWE-Bench Lite and Verified benchmarks, achieving competitive performance among open-source models with scores of 22.0% and 30.2%. Furthermore, SWE-Fixer reaches state-of-the-art performance (24.7% on Lite and 32.8% on Verified) with PASS_TO_PASS (P2P) filtering. Additionally, our approach requires only two model calls per instance, making it significantly more efficient than existing methods. These results highlight the effectiveness of SWE-Fixer in real-world code-fixing scenarios. We will make our model, dataset, and code publicly available at https://github.com/InternLM/SWE-Fixer.
Forward citations
Cited by 15 Pith papers
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IssueExec: A Test-Driven Approach for Localizing Software Engineering Issues
IssueExec localizes issue-relevant code by retrieving related tests and following their execution traces, beating prior localization methods on SWE-bench Lite.
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Retrieval-Oriented Code Representations in Agentic Bug Localization
Role-aware file summaries give the best cost-effectiveness for file-level bug localization, beating file paths by up to 40% Hit@5 at far smaller footprint than raw source.
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Backward reasoning distilled from a repository's already-fixed issues, injected as prompts, lifts LLM repair success on SWE-Bench Lite by up to 10.4 Pass@1 points.
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SWE-Manager: Selecting and Synthesizing Golden Proposals Before Coding
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SWE-Perf: Can Language Models Optimize Code Performance on Real-World Repositories?
A repository-level benchmark shows LLMs and agents currently achieve far smaller, statistically verified speedups than expert human patches on 140 real-world Python performance tasks.
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Skywork-SWE: Unveiling Data Scaling Laws for Software Engineering in LLMs
Training on more validated GitHub issue-fix trajectories steadily raises SWE-bench Verified pass@1 from 6.4% to 38.0%, with 47.0% after test-time scaling, and no plateau yet.
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Agent-RLVR: Training Software Engineering Agents via Guidance and Environment Rewards
A guidance-augmented RLVR framework improves software engineering agent pass@1 on SWE-Bench Verified from 9.4% to 22.4%, with guidance being the critical component.
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Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering
A 32B model trained to self-evolve patch candidates through RL reaches 41.6% on SWE-Bench Verified with 50 samples, comparable to a 70B model using 500 samples.
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MCTS-Refined CoT: High-Quality Fine-Tuning Data for LLM-Based Repository Issue Resolution
MCTS-REFINE uses tree search plus strict ground-truth matching to build chain-of-thought training data that lifts open-source LLM issue-resolution scores on SWE-bench.
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