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

• URL: http://arxiv.org/abs/2506.23749v1
• Date: Mon, 30 Jun 2025 11:46:01 GMT
• Title: A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications
• Abstract summary: Large language models (LLMs) are reshaping automated program repair (APR)<n>We categorize the recent 63 LLM-based APR systems published from January 2022 to June 2025 into four paradigms.<n>This taxonomy clarifies key trade-offs: fine-tuning delivers strong task alignment at high training cost; prompting enables rapid deployment but is limited by prompt design and context windows.<n>We outline research directions that combine lightweight human feedback, repository-aware retrieval, code analysis, and cost-aware planning to advance reliable and efficient LLM-based APR.
• Score: 12.409034344742768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are reshaping automated program repair (APR). We categorize the recent 63 LLM-based APR systems published from January 2022 to June 2025 into four paradigms, and show how retrieval- or analysis-augmented contexts strengthen any of them. This taxonomy clarifies key trade-offs: fine-tuning delivers strong task alignment at high training cost; prompting enables rapid deployment but is limited by prompt design and context windows; procedural pipelines offer reproducible control with moderate overhead; agentic frameworks tackle multi-hunk or cross-file bugs at the price of increased latency and complexity. Persistent challenges include verifying semantic correctness beyond test suites, repairing repository-scale defects, and lowering the costs of LLMs. We outline research directions that combine lightweight human feedback, repository-aware retrieval, code analysis, and cost-aware planning to advance reliable and efficient LLM-based APR.

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