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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.”
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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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Chen, F., Yang, G., Lee, C., Jie, Z., & Chen, Y. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3831589
Benchmarking Open-Weight LLMs for Security Backporting
Sghaier, S., Daoud, M. A., Chaieb, M., & Chouchen, M. (2026) · Intelligence in Business and Industry (IBI) · doi:10.1109/ibi68858.2026.11604072
Wang, Y., Zhang, X., Han, J., Zhi, C., & Deng, S. (2026) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1145/3794763.3794815 · Cited by 2
Gong, S., Wang, M., Wang, J., & Cai, Y. (2026) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1145/3794763.3794823
PaperRepro: Automated Computational Reproducibility Assessment for Social Science Papers
Zhang, L., Xia, T., Piao, J., & Cui, L. (2026) · Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 · doi:10.1145/3770855.3818943
Cao, Y., Keung, J., Wang, Y., Sun, Y., & Mao, Z. (2026) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac69091.2026.00062
ReviewTestFuse: Fusing review-guided and direct test generation for deployable regression testing
Zhen, H., & Zhang, Z. (2026) · Automated Software Engineering · doi:10.1007/s10515-026-00669-y
CGARF: a causality-guided framework for reliable automated program repair
Yuan, L., Liu, S., Wang, Y., Yao, Y.-W., Liu, Y., Wang, Y., Wang, T., & Mao, T. (2026) · Empirical Software Engineering · doi:10.1007/s10664-026-10940-1
CI-Bench: A Framework for Evaluating Large Language Model Tools on CI Failures
Nabil, R. L., Zhu, H., & Rubio-González, C. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3774748.3787606
Change And Cover: Last-Mile, Pull Request-Based Regression Test Augmentation
Zhou, Z., Paltenghi, M., Kim, M., & Pradel, M. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3787768
MaCTG: Multi-Agent Collaborative Thought Graph for Automatic Programming
Zhao, Z., Sun, J., Hóu, Z., Wei, Z., Cai, C., Qiao, M., & Dong, J. S. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3773221 · Cited by 9
Synthetic Repo-level Bug Dataset for Training Automated Program Repair Models
Minh, P. V. T., Phan, H. N., Phan, N. H., Le, C. A., Nguyen, T. N., & Bui, N. D. Q. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3764562
Agent-Based Ensemble Reasoning for Repository-Level Issue Resolution
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MioHint: LLM-Assisted Request Mutation for Whitebox REST API Testing
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Generative File Systems: Specification-Driven Synthesis and Evolution via LLMs
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PyHealer: Agentic Self-Repair for Python Program ExecutionPreprint
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Jonnakuti, S. R. (2026) · International Conference on Modern Sustainable Systems (CMSS) · doi:10.1109/cmss69636.2026.11688270
Jonnakuti, S. R. (2026) · International Conference on Modern Sustainable Systems (CMSS) · doi:10.1109/cmss69636.2026.11688408
Messarhi, O., Kerdoudi, M. L., & Tibermacine, O. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.113101
Execution Control Harness for Governed LLM-Based Code Generation
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DepTwin: A Digital Twin-Based Framework to Manage Deployments in Cloud
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Bravo, E. J. G., Chavira, G., Álvarez-Navarro, E., Montoto-González, A., & Juarez, J. L. D. (2026) · Computers · doi:10.3390/computers15100657
Closed-Loop LLM-Driven Self-Healing for Enterprise Data Pipelines
Sivaraman, M. K. (2026) · Technology and Innovation Conference (TIC) · doi:10.1109/tic68483.2026.11703022
Marasinghe, M. M. K. S., Fernando, D. C. Y., Senarathne, G. S. R. U. R., Bambarendage, T. L. P., Kasthurirathna, D., & Jayalath, J. M. T. I. (2026) · International Conference on Computer Technology Applications (ICCTA) · doi:10.1109/iccta70480.2026.11706444
NSync: Automated Cloud Infrastructure-as-Code Reconciliation with AI Agents
Yang, Z., Guan, H., Nicolet, V., Paulsen, B., Dodds, J., Kroening, D., & Chen, A. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3832121
AgentInspect: Diagnosing Behavioral Failures in Artificial Intelligence Agents
Manke, R., Wardat, M., Khomh, F., & Rajan, H. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3832174
SAGE: Signal-Amplified Guided Embeddings for Vulnerability Detection
Shan, Z., Qian, X., Xin, J., Xu, M., Zhang, Y., Yang, Z., Wu, H., & Cheng, X. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3832141 · Cited by 1
Self-citationLLM-Based Repair of Static Nullability Errors
Karimipour, N., Joos, P., Pradel, M., Kellogg, M., & Sridharan, M. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3832114
Automated Program Repair for UI-Centric Android Bugs: How Far Are We?
Mahmud, J., Pandey, S., Silva, N. D., Dipongkor, A. K., Wu, J., Chaparro, O., Fazzini, M., & Moran, K. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3832272
No One-Size-Fits-All: Adaptive Code Editing with Feature-Based Strategy Selection
Wan, J., Liu, Z., Chen, D., Jiang, W., Li, Y., & Ren, X. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3832234
SWE-PDB: Teaching LLMs to Leverage Debugging Tools via Agentic Training
Liu, J., Hu, X., & Xia, X. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3832117
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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