This research introduces MM-assist, an innovative tool that leverages large language models to automatically identify misplaced methods in code and recommend better locations.

• Combines LLMs, IDEs, and semantic embeddings for expert-level code refactoring
• Aligns with how expert developers perform Move Method refactoring
• Provides contextual recommendations based on code understanding
• Outperforms existing automated refactoring tools

This advancement matters for Engineering teams by reducing technical debt, improving code organization, and boosting developer productivity through AI assistance in routine refactoring tasks.

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