Empirical Evaluation of Large Language Models in Automated Program Repair

Jiajun Sun; Fengjie Li; Xinzhu Qi; Hongyu Zhang; Jiajun Jiang

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arXiv · 2506.13186

Empirical Evaluation of Large Language Models in Automated Program Repair

Jiajun Sun·Fengjie Li·Xinzhu Qi·Hongyu Zhang·Jiajun Jiang

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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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Jiajun Sun, Fengjie Li, Xinzhu Qi, Hongyu Zhang, Jiajun Jiang. 2025-06-16. Empirical Evaluation of Large Language Models in Automated Program Repair. https://arxiv.org/abs/2506.13186

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