ThinkRepair: Self-Directed Automated Program Repair
Open accessAbstract
Though many approaches have been proposed for Automated Program Repair (APR) and indeed achieved remarkable performance, they still have limitations in fixing bugs that require analyzing and reasoning about the logic of the buggy program. Recently, large language models (LLMs) instructed by prompt engineering have attracted much attention for their powerful ability to address many kinds of tasks including bug-fixing. However, the quality of the prompt will highly affect the ability of LLMs and manually constructing high-quality prompts is a costly endeavor. To address this limitation, we propose a self-directed LLM-based automated program repair, ThinkRepair, with two main phases: collection phase and fixing phase. The former phase automatically collects various chains of thoughts that constitute pre-fixed knowledge by instructing LLMs with the Chain-of-Thought (CoT) prompt. The latter phase targets fixing a bug by first selecting examples for few-shot learning and second automatically interacting with LLMs, optionally appending with feedback of testing information. Evaluations on two widely studied datasets (Defects4J and QuixBugs) by comparing ThinkRepair with 12 SOTA APRs indicate the priority of ThinkRepair in fixing bugs. Notably, ThinkRepair fixes 98 bugs and improves baselines by 27%∼344.4% on Defects4J V1.2. On Defects4J V2.0, ThinkRepair fixes 12∼65 more bugs than the SOTA APRs. Additionally, ThinkRepair also makes a considerable improvement on QuixBugs (31 for Java and 21 for Python at most).
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ThinkRepair significantly improves LLMs' bug fixing capability.
“This approach significantly improves LLMs' bug fixing capability.”
From When LLMs meet cybersecurity: a systematic literature review (2025) · cites this paper as Yin et al. 2024
Cites
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Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity
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When LLMs meet cybersecurity: a systematic literature review
Zhang, J., Bu, H., Wen, H., Liu, Y., Fei, H., Xi, R., Li, L., Yang, Y., Zhu, H., & Meng, D. (2025) · Cybersecurity · doi:10.1186/s42400-025-00361-w · Cited by 220 · see citing works
“ThinkRepair (Yin et al. 2024) is an LLM-based autonomous two-stage automatic program repair framework.”
- Authors
- Yin, X., Ni, C., Wang, S., Li, Z., Zeng, L., & Yang, X.
- Affiliations
- Zhejiang University (CN)
- Central University of Finance and Economics (CN)
- Concordia University (CA)
- Venue
- Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis
- Publisher
- ACM
- Published
- 2024-09-11
- Pages
- 1274-1286
- OpenAlex
- W4402442387
- Cited by
- 68
- References
- 12
- Citations by year
- 2026: 34 · 2025: 32 · 2024: 2
- FWCI
- 18.50
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (Grant No.62202419)
- Fundamental Research Funds for the Central Universities (No. 226-2022-00064)
- Zhejiang Provincial Natural Science Foundation of China (No. LY24F020008)
- State Street Zhejiang University Technology Center
- Topics
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Keywords
- automated program repair, large language models, chain-of-thought prompting, prompt engineering, bug fixes, few-shot learning, Defects4J
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