Leveraging LLMs, IDEs, and Semantic Embeddings for Automated Move Method Refactoring
Abhiram Bellur, Fraol Batole, Mohammed Raihan Ullah, Malinda Dilhara, Yaroslav Zharov, Timofey Bryksin, Kai Ishikawa, Haifeng Chen, Masaharu Morimoto, Shota Motoura, Takeo Hosomi, Tien N. Nguyen, Hridesh Rajan, Nikolaos Tsantalis, Danny Dig
TL;DR
The paper tackles MoveMethod refactoring by deploying MM-assist, an end-to-end system that combines LLM reasoning with IDE static analysis, semantic embeddings, and refactoring-aware retrieval to filter hallucinations and manage global project-wide decisions. It introduces a structured approach to identify misplaced methods, filter candidate targets, and rank suitable destination classes, while ensuring safe execution via the IDE. Extensive evaluations on synthetic and real-world corpora show MM-assist achieves up to ~2.4x higher recall than baselines and delivers refactorings in tens of seconds, with a user study indicating 82.8% positive perceptions. The work demonstrates practical impact for large-scale projects and establishes a replication package to enable broader adoption and extension to other refactoring domains.
Abstract
MOVEMETHOD is a hallmark refactoring. Despite a plethora of research tools that recommend which methods to move and where, these recommendations do not align with how expert developers perform MOVEMETHOD. Given the extensive training of Large Language Models and their reliance upon naturalness of code, they should expertly recommend which methods are misplaced in a given class and which classes are better hosts. Our formative study of 2016 LLM recommendations revealed that LLMs give expert suggestions, yet they are unreliable: up to 80% of the suggestions are hallucinations. We introduce the first LLM fully powered assistant for MOVEMETHOD refactoring that automates its whole end-to-end lifecycle, from recommendation to execution. We designed novel solutions that automatically filter LLM hallucinations using static analysis from IDEs and a novel workflow that requires LLMs to be self-consistent, critique, and rank refactoring suggestions. As MOVEMETHOD refactoring requires global, projectlevel reasoning, we solved the limited context size of LLMs by employing refactoring-aware retrieval augment generation (RAG). Our approach, MM-assist, synergistically combines the strengths of the LLM, IDE, static analysis, and semantic relevance. In our thorough, multi-methodology empirical evaluation, we compare MM-assist with the previous state-of-the-art approaches. MM-assist significantly outperforms them: (i) on a benchmark widely used by other researchers, our Recall@1 and Recall@3 show a 1.7x improvement; (ii) on a corpus of 210 recent refactorings from Open-source software, our Recall rates improve by at least 2.4x. Lastly, we conducted a user study with 30 experienced participants who used MM-assist to refactor their own code for one week. They rated 82.8% of MM-assist recommendations positively. This shows that MM-assist is both effective and useful.