← 研踪 PaperTrailarXiv:2511.04824 · v2
Agentic Refactoring: An Empirical Study of AI Coding Agents
首次提交 2025-11-07 05:24
版本更新 2026-10-27 02:28
雷达观测 2026-10-29 00:35
摘要
Agentic coding tools, such as OpenAI Codex, Claude Code, and Cursor, are changing how software is developed. These AI-powered systems function as autonomous teammates capable of planning and executing complex development tasks. Agents have become active participants in refactoring, a key practice for improving internal code quality without altering observable behavior. Despite their increasing adoption, there is little empirical understanding of how agentic refactoring is used in practice, how it compares to human-driven refactoring, and what impact it has on code quality. To address this gap, we present a large-scale study of AI agent-generated refactorings in real-world open-source Java projects, analyzing 51,412 refactoring instances across 12,256 pull requests and 14,998 commits based on the AIDev dataset. We find that refactoring is a common and intentional activity, with agents explicitly targeting refactoring in 26.1% of commits. The subsequent analyses of refactoring types, purposes, and code quality focus on the 3,907 commits (32,611 instances) with explicit refactoring intent. High-level refactorings form the largest category overall; however, agentic refactoring contains a slightly larger share of low-level, consistency-oriented edits than human refactoring (27.6% vs. 24.4%), and its two most frequent operations are Rename Variable (7.6%) and Change Variable Type (6.8%). Additionally, the stated motivations behind agentic refactoring focus overwhelmingly on internal quality concerns (52.5% maintainability and 28.1% readability), although the measured metric improvements from these frequent low-level refactorings remain limited. Finally, code quality metrics show that agentic refactoring yields small but statistically significant improvements in structural metrics, particularly for medium-level changes, reducing class size and complexity.
作者备注：28 pages, 8 Tables, 5 Figures, Accepted to ACM Transactions on Software Engineering and Methodology(TOSEM)
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v2 · 2026-10-27 02:28 · 最新观测版本
Agentic Refactoring: An Empirical Study of AI Coding Agents
Agentic coding tools, such as OpenAI Codex, Claude Code, and Cursor, are changing how software is developed. These AI-powered systems function as autonomous teammates capable of planning and executing complex development tasks. Agents have become active participants in refactoring, a key practice for improving internal code quality without altering observable behavior. Despite their increasing adoption, there is little empirical understanding of how agentic refactoring is used in practice, how it compares to human-driven refactoring, and what impact it has on code quality. To address this gap, we present a large-scale study of AI agent-generated refactorings in real-world open-source Java projects, analyzing 51,412 refactoring instances across 12,256 pull requests and 14,998 commits based on the AIDev dataset. We find that refactoring is a common and intentional activity, with agents explicitly targeting refactoring in 26.1% of commits. The subsequent analyses of refactoring types, purposes, and code quality focus on the 3,907 commits (32,611 instances) with explicit refactoring intent. High-level refactorings form the largest category overall; however, agentic refactoring contains a slightly larger share of low-level, consistency-oriented edits than human refactoring (27.6% vs. 24.4%), and its two most frequent operations are Rename Variable (7.6%) and Change Variable Type (6.8%). Additionally, the stated motivations behind agentic refactoring focus overwhelmingly on internal quality concerns (52.5% maintainability and 28.1% readability), although the measured metric improvements from these frequent low-level refactorings remain limited. Finally, code quality metrics show that agentic refactoring yields small but statistically significant improvements in structural metrics, particularly for medium-level changes, reducing class size and complexity.
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