# Agentic Refactoring: An Empirical Study of AI Coding Agents arXiv: https://arxiv.org/abs/2511.04824 ยท HTML: https://mlsanity.com/paper/2511.04824/ ## Key facts - Cited by 18 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 13 of its 62 references that are in the map. - First posted on arXiv 6 Nov 2025. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 13 of 13) - [IntelliCode Compose: Code Generation Using Transformer](https://mlsanity.com/paper/2005.08025/) (2020) - [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022) - [Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants](https://mlsanity.com/paper/2208.09727/) (2022) - [Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?](https://mlsanity.com/paper/2204.04741/) (2022) - [On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](https://mlsanity.com/paper/2509.14745/) (2025) - [Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward](https://mlsanity.com/paper/2406.07765/) (2024) - [Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI](https://mlsanity.com/paper/2505.19443/) (2025) - [Agentic Software Engineering: Foundational Pillars and a Research Roadmap](https://mlsanity.com/paper/2509.06216/) (2025) - [Refactoring Programs Using Large Language Models with Few-Shot Examples](https://mlsanity.com/paper/2311.11690/) (2023) - [Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study](https://mlsanity.com/paper/2402.16480/) (2024) - [How to Refactor this Code? An Exploratory Study on Developer-ChatGPT Refactoring Conversations](https://mlsanity.com/paper/2402.06013/) (2024) - [Why We Refactor? Confessions of GitHub Contributors](https://mlsanity.com/paper/1607.02459/) (2016) - [Leveraging LLMs, IDEs, and Semantic Embeddings for Automated Move Method Refactoring](https://mlsanity.com/paper/2503.20934/) (2025) ## Built on by (most cited first, 18 of 18) - [Agent READMEs: An Empirical Study of Context Files for Agentic Coding](https://mlsanity.com/paper/2511.12884/) (2025) - [The Story is Not the Science: Execution-Grounded Evaluation of Mechanistic Interpretability Research](https://mlsanity.com/paper/2602.18458/) (2026) - [Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents](https://mlsanity.com/paper/2606.05391/) (2026) - [SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring](https://mlsanity.com/paper/2608.09802/) (2026) - [Why Agentic-PRs Get Rejected: A Comparative Study of Coding Agents](https://mlsanity.com/paper/2602.04226/) (2026) - [Coding with "Enemy": Can Human Developers Detect AI Agent Sabotage?](https://mlsanity.com/paper/2606.05647/) (2026) - [Do AI Coding Agents Log Like Humans? An Empirical Study](https://mlsanity.com/paper/2604.09409/) (2026) - [The Devil Is in the Interface: Evaluating How Tool Architecture Shapes Coding Agent Behavior](https://mlsanity.com/paper/2608.11386/) (2026) - ["Refactoring Runaway": Understanding and Mitigating Tangled Refactorings in Coding Agents for Issue Resolution](https://mlsanity.com/paper/2605.22526/) (2026) - [Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Study](https://mlsanity.com/paper/2606.12231/) (2026) - [ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation](https://mlsanity.com/paper/2601.04327/) (2026) - [Do These Violent Delights Have Violent Ends? Measuring the Post-Merge Fate of Agentic Code](https://mlsanity.com/paper/2607.09902/) (2026) - [How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests](https://mlsanity.com/paper/2607.21832/) (2026) - [From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical Documentation](https://mlsanity.com/paper/2608.20195/) (2026) - [REFINE: A Multi-Agent LLM Approach for Evidence-Guided Code Refactoring](https://mlsanity.com/paper/2608.23611/) (2026) - [On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplaces](https://mlsanity.com/paper/2608.28497/) (2026) - [Super Library Agent: Joint Generation and Maintenance of Multiple Applications Beyond the Single Codebase](https://mlsanity.com/paper/2608.29310/) (2026) - [Runnable Commit Untangling for Coding Agents](https://mlsanity.com/paper/2610.11593/) (2026) ## Abstract (arXiv) 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).