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 （ACES） techniques have emerged as a key direction to enhance editing efficiency and reduce human errors. However， the existing research suffers from issues such as scattered findings， and lack of systematic integration and a unified framework. Therefore， a systematic review of ACES techniques was conducted， reviewing relevant research findings published from 2004 to 2025 comprehensively. Firstly， the publication trends in this field were sorted out， and the technical evolution of suggestion models along with the development of related auxiliary technologies were summed up from three dimensions： traditional intelligent methods， deep learning models， and large language models. Based on the above， suggestion tasks were classified into four different types： context information-based， task descriptions and instructions-based， historical edits-based， and input-output examples-based suggestion tasks， and the technical approaches and research findings of each task type were elaborated in detail. Secondly， through analysis of the ACES evaluation systems， the programming languages， editing granularity， scale distribution of the existing evaluation datasets， as well as evaluation metrics such as text similarity and functional correctness of the code were introduced systematically. Finally， an in-depth analysis of the current research status was conducted， a series of prominent challenges in the research were identified， and potential future opportunities were prospected， providing theoretical references and directional guidance for the further development of this field.