Automated Program Repair (APR) is currently an essential technique for improving software maintenance, assisting in reducing the burden on developers, and minimizing software defects. Recent advances in large language models (LLMs) have significantly impacted this area by improving bug localization, patch generation, and validation.The current LLM based automated program repair (APR) techniques are systematically reviewed in this paper in order to evaluate their advancements, real world constraints, performance comparisons, and security considerations. The study explores the advantages and disadvantages of open-source alternatives like CodeT5 and UniXcoder as well as proprietary LLMs like GPT4 and Codex, taking into consideration their respective costs, customization, and accuracy. Retrieval augmented generation, finetuning techniques, and hybrid approaches that combine LLM with static analysis tools have all significantly increased the effectiveness of APR. However, problems still persist, such as reliance on AI generated patches, high processing demands, and challenges incorporating these techniques into development processes. Furthermore, ethical issues including biases in created patches and the potential to introduce security defects necessitate further research on validation and security aware patch production. This paper summarizes current research to identify key gaps and proposes future directions to optimize LLM based APR, to assure its reliability and scalability in real world software development.

Automated Program Repair, Large Language Models, Software Maintenance, Bug Fixing, Bug Repair, Code Repair