With the rapid development and large-scale popularity of program software, modern society increasingly relies on software systems. However, the problems exposed by software have also come to the fore. Software defect has become an important factor troubling developers. In this context, Automated Program Repair (APR) techniques have emerged, aiming to automatically fix software defect problems and reduce manual debugging work. In particular, benefiting from the advances in deep learning, numerous learning-based APR techniques have emerged in recent years, which also bring new opportunities for APR research. To give researchers a quick overview of APR techniques' complete development and future opportunities, we revisit the evolution of APR techniques and discuss in depth the latest advances in APR research. In this paper, the development of APR techniques is introduced in terms of four different patch generation schemes: search-based, constraint-based, template-based, and learning-based. Moreover, we propose a uniform set of criteria to review and compare each APR tool, summarize the advantages and disadvantages of APR techniques, and discuss the current state of APR development. Furthermore, we introduce the research on the related technical areas of APR that have also provided a strong motivation to advance APR development. Finally, we analyze current challenges and future directions, especially highlighting the critical opportunities that large language models bring to APR research.

翻译：随着程序软件的快速发展和大规模普及，现代社会日益依赖软件系统。然而，软件暴露出的问题也日益凸显。软件缺陷已成为困扰开发人员的重要因素。在此背景下，自动程序修复（APR）技术应运而生，旨在自动修复软件缺陷问题，减少人工调试工作。特别是，得益于深度学习的进步，近年来涌现出大量基于学习的APR技术，这为APR研究带来了新的机遇。为了让研究人员快速了解APR技术的完整发展历程和未来机遇，我们重新审视了APR技术的演变过程，并深入探讨了APR研究的最新进展。本文从四种不同的补丁生成方案（基于搜索、基于约束、基于模板和基于学习）出发，介绍了APR技术的发展历程。此外，我们提出了一套统一的评估标准来审视和比较每个APR工具，总结了APR技术的优缺点，并讨论了APR发展的当前状态。进一步地，我们介绍了APR相关技术领域的研究，这些研究也为推动APR发展提供了强大动力。最后，我们分析了当前挑战和未来方向，特别强调了大语言模型为APR研究带来的关键机遇。