The DOI matches its Crossref record; 34 of the 35 references it cites were found in a registry or an index, and 1 are not checked yet.
AutoCodeRover: Autonomous Program Improvement
Open accessAbstract
Researchers have made significant progress in automating the software development process in the past decades. Automated techniques for issue summarization, bug reproduction, fault localization, and program repair have been built to ease the workload of developers. Recent progress in Large Language Models (LLMs) has significantly impacted the development process, where developers can use LLM-based programming assistants to achieve automated coding. Nevertheless, software engineering involves the process of program improvement apart from coding, specifically to enable software maintenance (e.g. program repair to fix bugs) and software evolution (e.g. feature additions). In this paper, we propose an automated approach for solving Github issues to autonomously achieve program improvement. In our approach called AutoCodeRover, LLMs are combined with sophisticated code search capabilities, ultimately leading to a program modification or patch. In contrast to recent LLM agent approaches from AI researchers and practitioners, our outlook is more software engineering oriented. We work on a program representation (abstract syntax tree) as opposed to viewing a software project as a mere collection of files. Our code search exploits the program structure in the form of classes/methods to enhance LLM’s understanding of the issue’s root cause, and effectively retrieve a context via iterative search. The use of spectrum-based fault localization using tests, further sharpens the context, as long as a test-suite is available. Experiments on the recently proposed SWE-bench-lite (300 real-life Github issues) show increased efficacy in solving Github issues (19% on SWE-bench-lite), which is higher than the efficacy of the recently reported Swe-agent. Interestingly, our approach resolved 57 GitHub issues in about 4 minutes each (pass@1), whereas developers spent more than 2.68 days on average. In addition, AutoCodeRover achieved this efficacy with significantly lower cost (on average, $0.43 USD), compared to other baselines. We posit that our workflow enables autonomous software engineering, where, in future, auto-generated code from LLMs can be autonomously improved.
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“memory methods that maintain working state, retrieve repository evidence, store reusable experience, and support shared interaction histories [45, 46, 47, 48]”
From Code as Agent Harness (2026) · cites this paper as [46]
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“Mechanisms such as AST-based structured chunking, iterative query rewriting, and retrieval strategies conditioned on current localization clues can substantially improve the utility of retrieved context for downstream generation.”
From Code as Agent Harness (2026) · cites this paper as [46]
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“Representative works such as AutoCodeRover [46] and RepoCoder [47] show that repository-level coding tasks benefit not simply from retrieving more content, but from retrieving evidence aligned with program structure.”
From Code as Agent Harness (2026) · cites this paper as [46]
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Cites
A Survey on Software Fault Localization
Wong, W. E., Gao, R., Li, Y., Abreu, R., & Wotawa, F. (2016) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2521368 · Cited by 1,110
Competition-level code generation with AlphaCode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · Science · doi:10.1126/science.abq1158 · Cited by 1,043
Competition-Level Code Generation with AlphaCode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Lago, A. D., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.07814 · Cited by 1,043
On the Accuracy of Spectrum-based Fault Localization
Abreu, R., Zoeteweij, P., & van Gemund, A. V. (2007) · Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION (TAICPART-MUTATION 2007) · doi:10.1109/taic.part.2007.13 · Cited by 939
Visualization of test information to assist fault localization
Jones, J. A., Harrold, M. J., & Stasko, J. (2002) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/581339.581397 · Cited by 925
Symbolic execution for software testing
Cadar, C., & Sen, K. (2013) · Communications of the ACM · doi:10.1145/2408776.2408795 · Cited by 785
Automatically finding patches using genetic programming
Weimer, W. R., Nguyen, T. H., Goues, C. L., & Forrest, S. (2009) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2009.5070536 · Cited by 768
Böhme, M., Pham, V.-T., Nguyen, M.-D., & Roychoudhury, A. (2017) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3133956.3134020 · Cited by 759
Self-citationMechtaev, S., Yi, J., & Roychoudhury, A. (2016) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/2884781.2884807 · Cited by 515
Self-citationAutomated Program Repair in the Era of Large Pre-trained Language Models
Xia, C. S., Wei, Y., & Zhang, L. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00129 · Cited by 433
Research works printed but not linked: 12
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- Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. 2024. Leaderboard results on SWE-bench. https://www.swebench.com/ Retrieved April 8, 2024 from
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Cited by
Sorted by citations
A Survey on Large Language Models for Code Generation
Jiang, J., Wang, F., Shen, J., Kim, S., & Kim, S. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3747588 · Cited by 251
He, J., Treude, C., & Lo, D. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3712003 · Cited by 246
LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead
He, J., Treude, C., & Lo, D. (2024) · arXiv · doi:10.48550/arxiv.2404.04834 · Cited by 246
Automated Program Repair via Conversation: Fixing 162 out of 337 Bugs for $0.42 Each using ChatGPT
Xia, C. S., & Zhang, L. (2024) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3650212.3680323 · Cited by 468
(2025) · doi:10.1109/icse55347.2025.00157 · Cited by 157
Metadata
- Authors
- Zhang, Y., Ruan, H., Fan, Z., & Roychoudhury, A.
- Affiliations
- National University of Singapore (SG)
- Venue
- Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis
- Publisher
- ACM
- Published
- 2024-09-11
- Pages
- 1592-1604
- OpenAlex
- W4402442868
- Cited by
- 202
- References
- 34
- Citations by year
- 2026: 123 · 2025: 70 · 2024: 9
- FWCI
- 76.83
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Ministry of Education - Singapore (MOE-MOET32021-0001)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Keywords
- program improvement, program repair, large language models, code search, spectrum-based fault localization, abstract syntax tree, software maintenance, software evolution, LLM agents
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