Agentic Refactoring Pull Requests

• Agentic refactoring PRs are pull requests initiated by autonomous AI agents that perform code restructuring to enhance maintainability while preserving functional behavior.
• These PRs combine low-level annotation edits with occasional structural modifications, yielding measurable improvements in metrics such as cyclomatic complexity and LOC reduction.
• Empirical studies reveal high merge rates and significant human oversight, underscoring the need for reviewer intervention to ensure alignment with project-specific standards.

Agentic refactoring pull requests (PRs) represent an emergent paradigm in software engineering in which autonomous AI coding agents propose and execute code restructuring operations with the explicit intent of improving maintainability, readability, modularity, or related aspects of internal code quality—without altering externally observable behavior. These PRs are typically created by agents such as Claude Code, OpenAI Codex, Copilot, Cursor, and Devin, and have become common in both open-source and enterprise contexts. The agentic refactoring workflow is characterized by automated code analysis, transformation, and submission of PRs, often requiring follow-up human review for deeper architectural alignment or project-specific standards. This article synthesizes contemporary empirical findings concerning agentic refactoring PRs, focusing on their definition, detection, operation types, review patterns, code quality impact, and methodological best practices.

1. Taxonomy and Definition of Agentic Refactoring PRs

Agentic refactoring PRs are precisely defined as pull requests in which the initial code change was performed by an autonomous agent, with the explicit goal (as stated in commit message or PR description) of code restructuring, simplification, redundancy elimination, or related maintainability improvements. These operations do not alter the external API or observable behavior of the system (Watanabe et al., 18 Sep 2025, Horikawa et al., 6 Nov 2025). Representative agent instructions include:

• "Extract helper methods to reduce cyclomatic complexity and improve naming consistency."
• "Cleanup unused variables, remove dead code, and consolidate branching."

Within review comment taxonomies, "refactoring needs" are encoded as suggestions for code simplification, redundancy elimination, or restructuring (e.g., “remove unused variables,” “simplify if-chain,” “extract method to clarify intent”) (Haider et al., 27 Jan 2026). The scope spans both superficial (e.g., renaming, annotation edits) and structural (e.g., method extraction, class reorganization) changes.

2. Quantitative Prevalence and Operational Patterns

Empirical analyses of agentic PRs indicate that refactoring is frequent and intentional. For instance, in a large-scale study of 3,177 agent-authored PRs, 14% of all review comments were devoted to refactoring needs, placing it as the second most common review theme and among the top three PR-level dominant themes (10.4%) (Haider et al., 27 Jan 2026). In the AIDev dataset, agents explicitly target refactoring in 26.1% of commits, with agentic refactoring labeling based on both structural changes and commit message patterns (e.g., “refactor”, “cleanup”, “rename”) (Horikawa et al., 6 Nov 2025). Acceptance rates are high; 83.8% of agent-assisted PRs are merged, and within a representative sample, refactoring PRs merge at a comparable rate to feature or bug-fix PRs (Watanabe et al., 18 Sep 2025).

Modification rates post-submission provide additional insight: 25.9% of merged agentic PRs subsequently receive developer refactoring or revision, with human interventions most commonly comprising additional refactoring, bug fixes, documentation updates, and style adjustments (Watanabe et al., 18 Sep 2025, Cynthia et al., 27 Jan 2026). Core developers are more likely than peripheral developers to conduct deeper refactoring and enforce verification (CI checks) before merge (Cynthia et al., 27 Jan 2026).

3. Agentic Refactoring Types and Distribution

Agentic refactoring operations, as detected by tools such as RefactoringMiner and DesigniteJava, can be grouped into annotation-related and structural refactorings (Ottenhof et al., 28 Jan 2026, Horikawa et al., 6 Nov 2025). In aggregate:

• Agents' refactorings are highly skewed toward annotation edits: Add Method Annotation (22.52%), Add Parameter Annotation (12.82%), and Modify Method Annotation (10.37%) together constitute over 45% of all agent-applied refactorings (Ottenhof et al., 28 Jan 2026).
• Structural refactorings (e.g., Change Attribute Access Modifier, Extract Method, Move Class, Rename Variable) are less frequent, especially for agents like Claude Code.
• Human developers exhibit a much more diverse refactoring profile, with no single type exceeding 6% share.
• Cursor Agent is the exception, performing more structural changes (Extract Method 29%) and fewer annotation edits.

Agents generally perform more refactorings per commit than humans. For example, Claude Code averages 762.7 refactorings per commit (median 475), compared to 15.3 for developers (median 3) (Ottenhof et al., 28 Jan 2026). However, this volume mainly reflects annotation proliferation rather than architectural restructuring.

The abstraction level of agentic refactorings is biased toward low-level (35.8%) and high-level signature-only changes (43.0%), with medium-level (e.g., Move and Inline Method, Change Attribute Type) at 21.2% (Horikawa et al., 6 Nov 2025).

4. Review Dynamics and Human Oversight

Review comments on agentic refactoring PRs predominantly target maintainability, readability, and structural issues. LLM-based annotation pipelines, such as Gemma 3:12B via Ollama, achieve substantial alignment with human reviewers in classifying refactoring needs (Exact Match 78.63%, Macro F1 0.78, Cohen's κ 0.73), at both comment and PR levels (Haider et al., 27 Jan 2026). Reviewers frequently request:

• Elimination of dead code or unused variables
• Simplification of complex branching or nested loops
• Extraction of helper methods
• Structural realignment with project conventions

Despite autonomous agent capabilities, substantial reviewer burden persists. Recommendations for agentic system architects include:

• Implementing automated internal refactoring checks (AST-based analyzers, unused symbol detectors) prior to PR creation.
• Enforcing cyclomatic complexity thresholds and linter/formatter integration at generation time.
• Separating intents (distinct "refactor" vs. "feature" PRs) to minimize tangling and facilitate review.
• Active use of "confidence cards" to document agent rationale for change.

Human revisions to agent-generated refactoring PRs often address error-handling gaps, synchronize documentation, further refactor code structure, and ensure style compliance (Watanabe et al., 18 Sep 2025, Cynthia et al., 27 Jan 2026).

5. Code Quality Impact and Empirical Evaluation

Quantitative analysis using code metric suites (DesigniteJava) demonstrates that agentic refactoring PRs yield statistically significant but modest improvements in internal code metrics, particularly for medium-level refactorings. Findings include (Horikawa et al., 6 Nov 2025, Ottenhof et al., 28 Jan 2026):

• Median class LOC reduction: Δ = –15.25 for medium-level agentic refactorings
• Weighted Methods per Class (WMC) median Δ = –2.07
• Minimal effect on design and implementation smell counts (median Δ = 0.00 across most types; Wilcoxon p < 0.001, Cohen’s d ≈ –0.03)
• Cursor Agent is the sole model associated with a statistically significant increase in code smells post-refactoring (Wilcoxon p = 0.013, Cliff’s δ = 0.51)

This pattern suggests that agentic refactoring predominantly achieves naming consistency, minor code cleanup, and localized complexity reduction; however, deep architectural improvement (e.g., duplication removal, API redesign) remains rarely targeted by agents and is more prevalent in human-driven refactoring.

6. Best Practices, Limitations, and Future Directions

Empirical studies converge on several actionable best practices for agentic refactoring PRs (Watanabe et al., 18 Sep 2025, Haider et al., 27 Jan 2026):

• Restrict agentic refactoring PRs to single, well-defined concerns to avoid "too large" changes—a top cause of rejection.
• Supply style rules and project-specific conventions upfront, embedding guides (e.g., CLAUDE.md) in agent context.
• Encourage agent generation of explanation artifacts ("confidence card") to facilitate reviewer understanding and streamline merge process.
• Automate low-risk chore management (e.g., rebasing, conflict resolution) to keep agent-produced branches merge-ready.
• Favor mixed-authorship revision loops, leveraging agent proficiency for bulk annotation but reserving deeper structural changes for developers.
• Integrate refactoring detectors and code smell analysis in CI pipelines, particularly when employing agents shown to increase smell incidence.

Agent training and workflow design should increasingly emphasize structural (medium/high-level) refactoring patterns, automated separation of intent, and behavior-preserving restructuring for long-term maintainability. Current agentic systems deliver measurable, though limited, code health improvements; advancements require fine-tuning agent behavior and integrating higher-fidelity architectural transformation curricula.

7. Comparative Insights and Implications

Direct comparison of agentic and human refactoring PRs reveals substantial differences in motivation, operation types, and outcomes (Horikawa et al., 6 Nov 2025, Ottenhof et al., 28 Jan 2026):

• Agents focus predominantly on internal quality (52.5% maintainability, 28.1% readability), with a bias toward low-level changes (e.g., renames, annotation edits).
• Humans prioritize design-level improvements (13.7% duplication removal, 12.9% code reuse) and perform more high-level signature modifications.
• Agentic refactoring leads to routine improvement in naming and localized complexity, but rarely achieves broader architectural benefit.
• Human oversight remains vital to calibrate trust, ensure compliance with project standards, and address "draft"-like agent output.

A plausible implication is that agents are well-positioned to automate repetitive clean-up, freeing developers for strategic architectural evolution, but long-term reduction of technical debt and defect risk requires synergistic human–AI workflows and further research in measuring and encouraging substantive design improvements.

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Key References

• "Understanding Dominant Themes in Reviewing Agentic AI-authored Code" (Haider et al., 27 Jan 2026)
• "On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub" (Watanabe et al., 18 Sep 2025)
• "Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agents" (Cynthia et al., 27 Jan 2026)
• "How do Agents Refactor: An Empirical Study" (Ottenhof et al., 28 Jan 2026)
• "Agentic Refactoring: An Empirical Study of AI Coding Agents" (Horikawa et al., 6 Nov 2025)