Large Language Models Meet Automated Program Repair: Innovations, Challenges and Solutions

As the field of Automated Program Repair (APR) continues to evolve, traditional Neural Program Repair (NPR) methods, while successful in low-resource computing scenarios, still confront numerous challenges, including the demand for extensive training data, the limited generality of specially designed networks, and a lack of robustness. In recent years, Large Language Models (LLMs) have demonstrated remarkable efficacy in downstream code-related tasks, thanks to their potent comprehension and text generation capabilities, gradually emerging as pivotal tools in automated program repair. Compared to NPR techniques, LLM-based APRs exhibit superior repair performance and enhanced generality, leading to their increasing adoption in APR tasks. Currently, the performance of zero-shot LLM-based APRs has surpassed that of NPR. LLM-based APRs have issues, such as excessive fine-tuning costs, data leakage concerns, and a shortage of domain-specific knowledge. This paper aims to review and summarize the latest advancements in LLM-based APRs from the perspectives of innovation, challenges, and solutions, providing researchers with profound insights and future directions.