关键词: 软件工程, 自动代码编辑推荐, 深度学习, 大语言模型, 代码变更

Abstract: Code editing, as a core component of software development, is crucial for the continuous optimization of software systems. With the discovery of regularities in code editing behaviors, automated code edit suggestion techniques have emerged as a key direction to enhance editing efficiency and reduce human errors. However, existing research suffers from issues such as scattered findings, lack of systematic integration, and a unified framework. Therefore, a systematic review of automated code edit suggestion techniques was conducted, comprehensively reviewing relevant studies published between 2004 and 2025. First, the publication trends in this field were summarized, and the technical evolution of suggestion models along with the development of related auxiliary technologies were outlined from three dimensions: traditional intelligent methods, deep learning models, and large language models. Based on this, suggestion tasks were classified into four types according to their objectives: context-based information, task descriptions and instructions, historical edits, and input-output examples. The technical approaches and research outcomes of each task type were elaborated in detail. Second, through the analysis of the evaluation systems for automated code edit suggestion, the programming languages, granularity, scale distribution of existing evaluation datasets, as well as evaluation metrics such as text similarity and functional correctness were systematically introduced. Finally, an in-depth analysis of the current research landscape was conducted, and a series of prominent challenges were identified while potential future opportunities were outlined, providing theoretical references and directional guidance for the further development of this field.

Key words: software engineering, Automated Code Edit Suggestion (ACES), deep learning, large language model, code change