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
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Flagged first, then by citations
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Sorted by citations
Items 201–227 of 227
CoCoMUT: A Tool for Code-Context Mining and Automated Dataset Generation
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Zhang, T., Mao, X., Zhao, Y., Yang, K., Zhang, Z., Lu, Y., Chen, Y., & Yu, Y. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3849809
Wen, X.-C., Chen, B., Lan, H., Yu, H., Di, P., & Gao, C. (2026) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3832783.3834438 · Cited by 3
Who Should Own the Loop? Harness Decomposition for Small-Model Repository Repair
Dente, F., Satriani, D., Santoro, D., Veltri, E., & Papotti, P. (2026) · International Workshop on Agentic AI for Next-Generation Software Development · doi:10.1145/3843282.3844442
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MERA: A Multi-expert LLM Framework for Severity Assessment of User Tickets in Mobile OS Beta Testing
Sun, Y., Zuo, Y., Zhang, Q., Hao, B., Gu, W., Luan, J., Zhou, S., Zhang, S., & Pei, D. (2026) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3832783.3834518
RegoMender: A Multi-agent Framework for Automated Rego Policy Repair
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From Automated Program Repair to Automated Agent Repair: Why Failed Trajectories Matter
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CiL-Agent: Compiler-in-the-Loop LLM Agent for Memory-Safe C Program Repair
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Schema-Conditioned Next-Tool Prediction for Coding Agents under Interface Drift
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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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