Exploring the Potential of Conversational Test Suite Based Program Repair on SWE-bench
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Abstract (arXiv)
Automatic program repair at project level may open yet to be seen opportunities in various fields of human activity. Since the SWE-Bench challenge was presented, we have seen numerous of solutions. Patch generation is a part of program repair, and test suite-based conversational patch generation has proven its effectiveness. However, the potential of conversational patch generation has not yet specifically estimated on SWE-Bench. This study reports experimental results aimed at evaluating the individual effectiveness of conversational patch generation on problems from SWE-Bench. The experiments show that a simple conversational pipeline based on LLaMA 3.1 70B can generate valid patches in 47% of cases, which is comparable to the state-of-the-art in program repair on SWE-Bench.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 11 references that are in the map. First posted on arXiv 6 Oct 2024.
Builds on
5 of 11 references.
References · 5
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2022Practical Program Repair in the Era of Large Pre-trained Language Modelscited by 133
- 2023Keep the Conversation Going: Fixing 162 out of 337 bugs for 0.42 each using ChatGPTcited by 125
- 2022SelfAPR: Self-supervised Program Repair with Test Execution Diagnosticscited by 33
- 2023A Critical Review of Large Language Model on Software Engineering: An Example from ChatGPT and Automated Program Repaircited by 29
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Citing papers per year: 2025: 1.
1 paper.