# EM-Assist: Safe Automated ExtractMethod Refactoring with LLMs

arXiv: https://arxiv.org/abs/2405.20551 · HTML: https://mlsanity.com/paper/2405.20551/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 3 of 3)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Why We Refactor? Confessions of GitHub Contributors](https://mlsanity.com/paper/1607.02459/) (2016)
- [JExtract: An Eclipse Plug-in for Recommending Automated Extract Method Refactorings](https://mlsanity.com/paper/1506.06086/) (2015)

## Built on by (most cited first, 8 of 8)

- [MANTRA: Enhancing Automated Method-Level Refactoring with Contextual RAG and Multi-Agent LLM Collaboration](https://mlsanity.com/paper/2503.14340/) (2025)
- [SkillScope: Toward Fine-Grained Least-Privilege Enforcement for Agent Skills](https://mlsanity.com/paper/2605.05868/) (2026)
- [Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects](https://mlsanity.com/paper/2502.02368/) (2025)
- [What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects](https://mlsanity.com/paper/2509.07763/) (2025)
- [Leveraging LLMs, IDEs, and Semantic Embeddings for Automated Move Method Refactoring](https://mlsanity.com/paper/2503.20934/) (2025)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [RefactorAssist: Agentic Refinement for Reliable Code Refactoring](https://mlsanity.com/paper/2608.00924/) (2026)
- [Multi-Agent Coordinated Rename Refactoring](https://mlsanity.com/paper/2601.00482/) (2026)

## Abstract (arXiv)

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.