mlsanityData to 7 Oct 2026

Exploring the Potential of Conversational Test Suite Based Program Repair on SWE-bench

arXiv 2410.04485 ↗First posted 6 Oct 2024cs.SE cs.AI cs.MAcited by 1

Not on the map. Why?

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

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  2. 2022Practical Program Repair in the Era of Large Pre-trained Language Modelscited by 133
  3. 2023Keep the Conversation Going: Fixing 162 out of 337 bugs for 0.42 each using ChatGPTcited by 125
  4. 2022SelfAPR: Self-supervised Program Repair with Test Execution Diagnosticscited by 33
  5. 2023A Critical Review of Large Language Model on Software Engineering: An Example from ChatGPT and Automated Program Repaircited by 29

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.