Bug fixing and code generation have been core research topics in software development for many years. The recent explosive growth in Large Language Models has completely transformed these spaces, putting in reach incredibly powerful tools for both. In this survey, 27 recent papers have been reviewed and split into two groups: one dedicated to Automated Program Repair (APR) and LLM integration and the other to code generation using LLMs. The first group consists of new methods for bug detection and repair, which include locating semantic errors, security vulnerabilities, and runtime failure bugs. The place of LLMs in reducing manual debugging efforts is emphasized in this work by APR toward context-aware fixes, with innovations that boost accuracy and efficiency in automatic debugging. The second group dwells on code generation, providing an overview of both general-purpose LLMs fine-tuned for programming and task-specific models. It also presents methods to improve code generation, such as identifier-aware training, fine-tuning at the instruction level, and incorporating semantic code structures. This survey work contrasts the methodologies in APR and code generation to identify trends such as using LLMs, feedback loops to enable iterative code improvement and open-source models. It also discusses the challenges of achieving functional correctness and security and outlines future directions for research in LLM-based software development.

本研究针对软件开发中的程序修复和代码生成问题，评估了27篇论文，并将其分为自动程序修复与大规模语言模型（LLM）集成以及基于LLM的代码生成两大类。研究提出通过集成LLM提高自动调试的准确性与效率，同时探讨了代码生成中改进方法及其在功能正确性和安全性方面面临的挑战，为LLM驱动的软件开发研究指明了未来方向。

基于人工智能进展和技术的自动程序修复与代码生成综合研究