Analysis of Research Status in the Field of Automated Program Repair

Abstract

As software systems keep developing and becoming more widely used, it’s impossible to avoid the program bugs that come with them. To reduce the pressure on developers when modifying programs and make bug fixes more efficient, Automated Program Repair (APR) plays a key role. This paper summarizes the main research progress in the field of APR, analyzes in detail the features, development process, and performance of various methods applied in this field. By comparing the advantages and disadvantages of these methods, it also discusses the challenges faced in the APR field and the possible directions for its future development. After discussion and comparison, it can be found that APR technology has made noticeable progress—especially the methods based on Large Language Models (LLMs) and hybrid agent methods. However, core challenges still exist, such as limitations of datasets and insufficient ability of models to understand code. In the future, it will be necessary to build more representative datasets, improve models’ ability to understand complex code logic, optimize the cooperation mechanism between multiple models, and reduce reliance on specific tools.