RefactorAssist: Agentic Refinement for Reliable Code Refactoring

Investigating the Refactoring Capabilities of Small Open-Weight Language Models

Code Refinement with Repository Context: How Far are We?

Code refinement, a process of revising flawed code changes based on the code review comments, is a fundamental practice in software development for ensuring software quality and maintainability. Recent approaches using large language models (LLMs) show promise in automating this process, yet they typically rely only on review comments and a single code snippet, neglecting broader repository context. When a code refinement task requires invoking reviewer-unspecified identifiers (e.g., variables, functions, or classes) that are defined elsewhere in the repository, the LLM must accurately predict the exact identifier signature and usage, otherwise it could easily result in fatal errors. Prior work demonstrates that 90.59% of such knowledge-conflicting hallucinations cause all test cases to fail. Notably, over 60% of real-world code refinement scenarios in most popular Github repositories depend on repository-level knowledge, yet none of the existing code refinement works leverage this information. Moreover, current datasets do not distinguish between context-dependent and context-independent instances, contain many low-quality instances, and rely on lexical metrics (e.g., BLEU) that poorly correlate with human judgment. These limitations hinder reliable evaluation of code refinement tools in real-world, context-dependent scenarios. To address this gap, we introduce ReCoRe-Bench, a high-quality benchmark of 1,000 code refinement instances from 328 Python, Java, and JavaScript repositories that focused on one of the most challenging code refinement scenarios that strictly requires repository-level knowledge reasoning. To ensure repository-level context is required and to achieve strong review-revision alignment, we applied a strict identifier-based heuristic filtering pipeline and manually filtered low quality instances containing vague comments or unrelated review comments. To better evaluate whether an LLM correctly retrieves and resolves identifiers defined in the repository, instead of inferring them from limited local context, we propose a new evaluation metric, ReDIs Match. Additionally, to address the weaknesses of current LLM-powered code refinement methods identified by ReCoRe-Bench, we propose a straightforward method, RepoRefiner, which retrieves repository-level context by collecting the full file content, extracting definitions of its identifiers, and summarizing these contexts to support code refinement. The experiment results show that the RepoRefiner outperforms current code refinement baselines, and the repository-level context is essential for the reliable code refinement. Our findings underscore both the promise and current limitations of LLMs in repository-level code refinement scenarios, highlighting the need for more effective static analysis techniques and prompt engineering for broader repository-level context exploration.

Formalizing and Automating Fine-Grained Move Refactorings Across Methods

Developers use automated Move refactorings to improve the modular structure of source code and the assignment of responsibilities. Class- and method-level Move refactorings are automated in modern IDEs, but statement- and expression-level moves that adjust method boundaries remain largely unautomated. We formalize five variants of Move Statement refactoring as preconditions and steps grounded in four basic conditions covering data reachability, execution count, side effects, and syntactic constraints required for compilation, of which all but the side-effect condition are checked statically. Combined with existing techniques, this also yields finer-grained moves of expressions and partial expressions. We further refine the formalization iteratively against a real project, deriving twenty additional preconditions and steps that handle Java syntactic diversity in practice. We evaluate applicability and compilability on ten projects, and behavior preservation in a case study on one of them: Move Statement refactorings yield compilable code in 93.3-97.0% of applicable cases, and the case study shows that the observed behavioral changes stem from side-effect reordering left to developer judgment, not from defects in the statically checked conditions.

A Preliminary Study on How AI Coding Agents Describe Refactoring Activities

Refactoring is essential for maintaining and evolving software systems, yet we still have limited insight into how automated code-generation agents describe these changes in pull requests. In this study, we analyze refactoring-related pull requests produced by five AI coding agents, with a particular focus on how their intentions are communicated through pull request descriptions. To better understand recurring transformation patterns, we extract refactoring descriptors terms that capture different refactoring activities and use them to identify similarities across agents. We then classify the pull requests into three main categories: internal quality attributes, external quality attributes, and code smells. Our dataset includes 2,288 unique AIgenerated refactoring pull requests from OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code. The results reveal several limitations in how AI systems perform and document refactoring, highlighting the need for clearer and more structured communication in collaborative software development.

A Preliminary Study on How AI Coding Agents Describe Refactoring Activities

Refactoring is essential for maintaining and evolving software systems, yet we still have limited insight into how automated code-generation agents describe these changes in pull requests. In this study, we analyze refactoring-related pull requests produced by five AI coding agents, with a particular focus on how their intentions are communicated through pull request descriptions. To better understand recurring transformation patterns, we extract refactoring descriptors terms that capture different refactoring activities and use them to identify similarities across agents. We then classify the pull requests into three main categories: internal quality attributes, external quality attributes, and code smells. Our dataset includes 2,288 unique AIgenerated refactoring pull requests from OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code. The results reveal several limitations in how AI systems perform and document refactoring, highlighting the need for clearer and more structured communication in collaborative software development.

REFINE: A Multi-Agent LLM Approach for Evidence-Guided Code Refactoring

Large Language Models (LLMs) offer new opportunities for automated code refactoring. However, generated changes must reduce targeted quality problems without introducing new issues or altering behaviour-relevant code structures. We introduce REFINE (Refactoring with Evidence-aware Flow for Integrated ageNtic Execution), a tool-agnostic, evidence-aware multi-agent approach for generating Java file-level refactoring candidates. REFINE combines static-analysis-guided smell identification, smell-informed planning, LLM-based transformation, automated re-analysis, preservation checks, and structured reporting. We evaluate REFINE on 450 Java files from 15 open-source systems, producing 1,350 model-pass outputs using OpenAI GPT-5.5, Google Gemini 3.1 Pro Preview, and Anthropic Claude Opus 4.8. REFINE reduces detected code smells by 68.26%, 72.79%, and 68.49% across the three configurations, respectively, with the strongest reductions observed for major smells. A matched 150-file direct-prompt baseline shows that REFINE achieves a higher median code-smell reduction with smaller edits and fewer public-method removals. However, broader quality improvements are inconsistent, and preservation checks reveal residual risks, including assert/fail-call changes and public-method removal. Therefore, REFINE outputs should be treated as refactoring candidates requiring compilation, testing, dependency analysis, and human review before adoption in repository- or system-level settings.