Multi-Agent Coordinated Rename Refactoring
Abhiram Bellur, Mohammed Raihan Ullah, Fraol Batole, Mohit Kansara, Masaharu Morimoto, Kai Ishikawa, Haifeng Chen, Yaroslav Zharov, Timofey Bryksin, Tien N. Nguyen, Hridesh Rajan, Danny Dig

TL;DR
This paper introduces a novel multi-agent framework that automates coordinated renaming in software refactoring, reducing developer effort and errors by intelligently inferring scope, planning, executing, and replicating renames across projects.
Contribution
It presents the first multi-agent system for automated coordinated renaming, integrating scope inference, planning, and execution to improve refactoring accuracy and efficiency.
Findings
The framework significantly reduces manual effort in coordinated renaming.
It achieves higher accuracy than heuristic-based approaches.
Developers find the system helpful and trustworthy.
Abstract
The primary value of AI agents in software development lies in their ability to extend the developer's capacity for reasoning and action, not to supplant human involvement. To showcase how to use agents working in tandem with developers, we designed a novel approach for carrying out coordinated renaming. Coordinated renaming, where a single rename refactoring triggers refactorings in multiple, related identifiers, is a frequent yet challenging task. Developers must manually propagate these rename refactorings across numerous files and contexts, a process that is both tedious and highly error-prone. State-of-the-art heuristic-based approaches produce an overwhelming number of false positives, while vanilla Large Language Models (LLMs) provide incomplete suggestions due to their limited context and inability to interact with refactoring tools. This leaves developers with incomplete…
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Taxonomy
TopicsSoftware Engineering Research · Advanced Software Engineering Methodologies · Software Engineering Techniques and Practices
