Empirical Evaluation of Large Language Models in Automated Program Repair

• URL: http://arxiv.org/abs/2506.13186v1
• Date: Mon, 16 Jun 2025 07:52:15 GMT
• Title: Empirical Evaluation of Large Language Models in Automated Program Repair
• Authors: Jiajun Sun, Fengjie Li, Xinzhu Qi, Hongyu Zhang, Jiajun Jiang,
• Abstract summary: Large language models (LLMs) offer new opportunities for automated program repair (APR)<n>We study four open-source LLMs, CodeLlama, LLaMA, StarCoder, and DeepSeek-Coder, spanning 7B to 33B parameters, diverse architectures, and purposes.<n>We evaluate them across two bug scenarios (enterprise-grades and algorithmic), three languages (Java, C/C++, Python), and four prompting strategies, analyzing over 600K generated patches on six benchmarks.
• Score: 11.840927951970146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing prevalence of software bugs has made automated program repair (APR) a key research focus. Large language models (LLMs) offer new opportunities for APR, but existing studies mostly rely on smaller, earlier-generation models and Java benchmarks. The repair capabilities of modern, large-scale LLMs across diverse languages and scenarios remain underexplored. To address this, we conduct a comprehensive empirical study of four open-source LLMs, CodeLlama, LLaMA, StarCoder, and DeepSeek-Coder, spanning 7B to 33B parameters, diverse architectures, and purposes. We evaluate them across two bug scenarios (enterprise-grades and algorithmic), three languages (Java, C/C++, Python), and four prompting strategies, analyzing over 600K generated patches on six benchmarks. Key findings include: (1) model specialization (e.g., CodeLlama) can outperform larger general-purpose models (e.g., LLaMA); (2) repair performance does not scale linearly with model size; (3) correct patches often appear early in generation; and (4) prompts significantly affect results. These insights offer practical guidance for designing effective and efficient LLM-based APR systems.

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