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

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Boyang Yang , Zi-Jian Cai , Brian Le , Lingming Zhang , Tegawendé F. Bissyandé , Haoye Tian ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2506.23749

Large language models (LLMs) are reshaping automated program repair. We present a unified taxonomy that groups 62 recent LLM-based repair systems into four paradigms defined by parameter adaptation and control authority over the repair loop, and overlays two cross-cutting layers for retrieval and analysis augmentation. Prior surveys have either focused on classical software repair techniques, on LLMs in software engineering more broadly, or on subsets of LLM-based software repair, such as fine-tuning strategies or vulnerability repair. We complement these works by treating fine-tuning, prompting, procedural pipelines, and agentic frameworks as first-class paradigms and systematically mapping representative systems to each of these paradigms. We also consolidate evaluation practice on common benchmarks by recording benchmark scope, pass@k, and fault-localization assumptions to support a more meaningful comparison of reported success rates. We clarify trade-offs among paradigms in task alignment, deployment cost, controllability, and ability to repair multi-hunk or cross-file bugs. We discuss challenges in current LLM-based software repair and outline research directions. Our artifacts, including the representation papers and scripted survey pipeline, are publicly available at https://github.com/GLEAM-Lab/ProgramRepair.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2506.23749
PDF: https://arxiv.org/pdf/2506.23749
OA Status: green
OpenAlex ID: https://openalex.org/W4416519743

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OpenAlex ID: https://openalex.org/W4416519743

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DOI: https://doi.org/10.48550/arxiv.2506.23749

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

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-06-30

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Authors: Boyang Yang, Zi-Jian Cai, Brian Le, Lingming Zhang, Tegawendé F. Bissyandé, Haoye Tian

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Landing page: https://arxiv.org/abs/2506.23749

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PDF URL: https://arxiv.org/pdf/2506.23749

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Cited by: 0

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