# Leveraging LLMs, IDEs, and Semantic Embeddings for Automated Move Method Refactoring

arXiv: https://arxiv.org/abs/2503.20934 · HTML: https://mlsanity.com/paper/2503.20934/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 61 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Focused Transformer: Contrastive Training for Context Scaling](https://mlsanity.com/paper/2307.03170/) (2023)
- [Refactoring Programs Using Large Language Models with Few-Shot Examples](https://mlsanity.com/paper/2311.11690/) (2023)
- [Unprecedented Code Change Automation: The Fusion of LLMs and Transformation by Example](https://mlsanity.com/paper/2402.07138/) (2024)
- [How to Refactor this Code? An Exploratory Study on Developer-ChatGPT Refactoring Conversations](https://mlsanity.com/paper/2402.06013/) (2024)
- [Why We Refactor? Confessions of GitHub Contributors](https://mlsanity.com/paper/1607.02459/) (2016)
- [An Empirical Study on the Potential of LLMs in Automated Software Refactoring](https://mlsanity.com/paper/2411.04444/) (2024)
- [MANTRA: Enhancing Automated Method-Level Refactoring with Contextual RAG and Multi-Agent LLM Collaboration](https://mlsanity.com/paper/2503.14340/) (2025)
- [EM-Assist: Safe Automated ExtractMethod Refactoring with LLMs](https://mlsanity.com/paper/2405.20551/) (2024)
- [Recommendation of Move Method Refactoring Using Path-Based Representation of Code](https://mlsanity.com/paper/2002.06392/) (2020)
- [RMove: Recommending Move Method Refactoring Opportunities using Structural and Semantic Representations of Code](https://mlsanity.com/paper/2212.12195/) (2022)
- [Automated Variable Renaming: Are We There Yet?](https://mlsanity.com/paper/2212.05738/) (2022)

## Built on by (most cited first, 1 of 1)

- [Agentic Refactoring: An Empirical Study of AI Coding Agents](https://mlsanity.com/paper/2511.04824/) (2025)

## Abstract (arXiv)

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.