Excessively long methods, loaded with multiple responsibilities, are challenging to understand, debug, reuse, and maintain. The solution lies in the widely recognized Extract Method refactoring. While the application of this refactoring is supported in modern IDEs, recommending which code fragments to extract has been the topic of many research tools. However, they often struggle to replicate real-world developer practices, resulting in recommendations that do not align with what a human developer would do in real life. To address this issue, we introduce EM-Assist, an IntelliJ IDEA plugin that uses LLMs to generate refactoring suggestions and subsequently validates, enhances, and ranks them. Finally, EM-Assist uses the IntelliJ IDE to apply the user-selected recommendation. In our extensive evaluation of 1,752 real-world refactorings that actually took place in open-source projects, EM-Assist's recall rate was 53.4% among its top-5 recommendations, compared to 39.4% for the previous best-in-class tool that relies solely on static analysis. Moreover, we conducted a usability survey with 18 industrial developers and 94.4% gave a positive rating.

EM-Assist是一款IntelliJ IDEA插件，使用LLMs生成重构建议并进行验证、增强和排序，通过对真实世界开源项目中的1752次重构进行广泛评估，EM-Assist的前五个建议中的召回率为53.4％，相比只依赖静态分析的先进工具的39.4％，用户易用性调查中94.4％的工业开发者给出了积极评价。

EM-Assist:使用LLM进行安全自动化提取方法重构