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，现代社会对软件系统的依赖日益加深。然而，软件所暴露的问题也日益突出。软件缺陷已成为困扰开发者的重要因素。在此背景下，自动化程序修复（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。尤其是受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。为帮助研究人员快速了解APR技术的完整发展历程与未来机遇，本文重新梳理了APR技术的演进过程，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技术的发展情况。此外，我们提出了一套统一的评价标准来审视和比较各类APR工具，总结了APR技术的优缺点，并讨论了APR的发展现状。同时，我们介绍了与APR相关的技术领域研究，这些研究也为推动APR发展提供了强大动力。最后，我们分析了当前面临的挑战与未来方向，重点强调了大型语言模型为APR研究带来的关键机遇。