Agentic Refactoring: An Empirical Study of AI Coding Agents

• URL: http://arxiv.org/abs/2511.04824v1
• Date: Thu, 06 Nov 2025 21:24:38 GMT
• Title: Agentic Refactoring: An Empirical Study of AI Coding Agents
• Authors: Kosei Horikawa, Hao Li, Yutaro Kashiwa, Bram Adams, Hajimu Iida, Ahmed E. Hassan,
• Abstract summary: Agentic coding tools, such as OpenAI Codex, Claude Code, and Cursor, are transforming the software engineering landscape.<n>These AI-powered systems function as autonomous teammates capable of planning and executing complex development tasks.<n>There is a critical lack of empirical understanding regarding how agentic is utilized in practice, how it compares to human-driven, and what impact it has on code quality.
• Score: 9.698067623031909
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Agentic coding tools, such as OpenAI Codex, Claude Code, and Cursor, are transforming the software engineering landscape. These AI-powered systems function as autonomous teammates capable of planning and executing complex development tasks. Agents have become active participants in refactoring, a cornerstone of sustainable software development aimed at improving internal code quality without altering observable behavior. Despite their increasing adoption, there is a critical lack of empirical understanding regarding how agentic refactoring is utilized in practice, how it compares to human-driven refactoring, and what impact it has on code quality. To address this empirical gap, we present a large-scale study of AI agent-generated refactorings in real-world open-source Java projects, analyzing 15,451 refactoring instances across 12,256 pull requests and 14,988 commits derived from the AIDev dataset. Our empirical analysis shows that refactoring is a common and intentional activity in this development paradigm, with agents explicitly targeting refactoring in 26.1% of commits. Analysis of refactoring types reveals that agentic efforts are dominated by low-level, consistency-oriented edits, such as Change Variable Type (11.8%), Rename Parameter (10.4%), and Rename Variable (8.5%), reflecting a preference for localized improvements over the high-level design changes common in human refactoring. Additionally, the motivations behind agentic refactoring focus overwhelmingly on internal quality concerns, with maintainability (52.5%) and readability (28.1%). Furthermore, quantitative evaluation of code quality metrics shows that agentic refactoring yields small but statistically significant improvements in structural metrics, particularly for medium-level changes, reducing class size and complexity (e.g., Class LOC median $\Delta$ = -15.25).

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