A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications

[2506.23749] A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications Skip to main content Search Submit Donate Log in Computer Science > Software Engineering arXiv:2506.23749 (cs) [Submitted on 30 Jun 2025 ( v1 ), last revised 27 Aug 2026 (this version, v3)] Title: A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications Authors: Boyang Yang , Zijian Cai , Fengling Liu , Bach Le , Lingming Zhang , Tegawendé F. Bissyandé , Yang Liu , Haoye Tian View PDF HTML (experimental) Abstract: Large language models (LLMs) are reshaping automated program repair. We present a reproducible hierarchical taxonomy that organizes 66 LLM-based repair systems according to where repair capability and control logic principally reside: task-adapted parameters, prompt and context design, designer-specified workflows, or LLM-directed runtime control. Adaptation, generation pattern, runtime control, and auxiliary evidence are preserved as separate coded dimensions. This representation exposes control distinctions hidden by utilization labels and supports cross-paradigm analysis of system design and evaluation evidence. To the best of our knowledge, it is the first publicly available LLM-based software repair survey to operationalize Fine-Tuning, Prompting, Procedural, and Agentic as one corpus-wide primary classification. Our hierarchy complements prior surveys through one corpus-wide primary decision rule, while the result-level protocol audit bounds which reported results are defensibly comparable. To analyze how repair systems use benchmarks, we record benchmark variants, metrics, base models, and fault-localization assumptions for each system's primary reported result, identifying protocol-aligned comparison windows and benchmark-family fragments. We clarify paradigm trade-offs in task alignment, deployment cost, controllability, and multi-hunk or cross-file repair. We outline open challenges and research directions. Our artifacts and scripted survey pipeline are publicly available at this https URL . Subjects: Software Engineering (cs.SE) Cite as: arXiv:2506.23749 [cs.SE] (or arXiv:2506.23749v3 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2506.23749 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Boyang Yang [ view email ] [v1] Mon, 30 Jun 2025 11:46:01 UTC (490 KB) [v2] Thu, 4 Dec 2025 08:21:22 UTC (957 KB) [v3] Thu, 27 Aug 2026 07:31:27 UTC (1,633 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.SE < prev | next > new | recent | 2025-06 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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