Agentic Refactoring

• Agentic refactoring is a process where AI agents autonomously detect, execute, and verify change operations to improve code maintainability and manage technical debt.
• It uses a formal tuple-based framework (S, A, G, T, R) to systematically plan and perform refactorings, ensuring improvements in metrics like complexity and test pass rates.
• Multi-agent architectures coordinate planner, executor, verifier, and repair roles to achieve safe, scalable, and energy-efficient code transformations across various programming paradigms.

Agentic refactoring refers to the use of autonomous or semi-autonomous AI-powered coding agents to perform, plan, and validate behavior-preserving restructuring operations on software codebases. These agents employ LLMs, formal reasoning, and integrated toolchains to detect refactoring opportunities, propose and execute transformations, and iteratively verify that internal code quality improves without altering observable behavior. Agentic refactoring fundamentally transforms software maintenance, library design, and technical debt management by enabling scalable, closed-loop, and multi-agent orchestration of transformation workflows across diverse programming paradigms, including object-oriented, imperative, and functional languages (Horikawa et al., 6 Nov 2025, Xu et al., 18 Mar 2025, Siddeeq et al., 24 Jun 2025, Siddeeq et al., 11 Feb 2025, Oueslati et al., 5 Nov 2025, Dearing et al., 4 May 2025, Sapkota et al., 26 May 2025, Kovacic et al., 26 May 2025).

1. Formal Definitions and Foundational Principles

Agentic refactoring generalizes the agentic coding paradigm to the problem of automated codebase transformation. The canonical formalization is as an agent-environment interaction process defined by a tuple $(S, A, G, T, R)$, where:

• State space $S$ encodes the abstract syntax tree (AST) of the codebase, associated metrics (e.g., cyclomatic complexity, test coverage), version-control snapshots, and agent-local memory (e.g., planned subtasks, diff logs).
• Action space $A$ comprises atomic or composite refactoring operations (e.g., ExtractMethod, RenameSymbol, RunTests), code analysis probes, and repository actions.
• Goal specification $G$ encodes objectives such as minimizing code complexity, maximizing coverage, or eliminating style violations.
• Transition function $T: S \times A \rightarrow S$ applies an action to update the code state and recompute relevant metrics.
• Reward function $R: S \times A \rightarrow \mathbb{R}$ quantifies the desirability of a transition, capturing improvements and penalizing regressions (e.g., failed tests or style violations).

The agentic refactoring policy $\pi$ maximizes expected cumulative reward over refactoring trajectories: $$\max_\pi \mathbb{E}\Bigl[\sum_{t=0}^T R(s_t,a_t)\Bigr] \quad \text{subject to } s_{t+1} = T(s_t, a_t)$$ This framework underpins agentic systems operating at multiple abstraction layers, including method, class, module, and multi-repository scales. A plausible implication is that agentic refactoring subsumes traditional scripted and search-based refactoring within a more general, self-correcting decision-making loop (Sapkota et al., 26 May 2025).

2. Multi-Agent System Architectures

State-of-the-art agentic refactoring leverages modular, multi-agent system designs to segment responsibilities and scale closed-loop orchestration. Key agent roles observed across implementations include:

• Planner/Context Agent: Parses project structure (via ASTs or static analysis), computes dependency graphs, and generates candidate refactoring plans through scoring functions over complexity, cohesion, and coupling metrics (Oueslati et al., 5 Nov 2025, Siddeeq et al., 24 Jun 2025, Siddeeq et al., 11 Feb 2025).
• Transformer/Executor Agent: Consumes a plan and modifies code, applying transformations either directly on code or over ASTs.
• Verifier/Validator Agent: Compiles the code, runs static analyses (e.g., DesigniteJava, HLint, CheckStyle), and executes test suites to ensure correctness and adherence to intended refactoring types.
• Repair/Debugging Agent: Iteratively patches code in response to compilation, test, or static-analysis failures, employing verbal reinforcement learning or fault-localization prompts to guide minimal corrections (Xu et al., 18 Mar 2025, Oueslati et al., 5 Nov 2025).
• Tool Adapter Agents: Interface with ecosystem tools (e.g., Maven/JUnit, GHC, EvoSuite, RefactoringMiner) to extract contextual data.

Agents communicate via JSON or message-passing protocols, often over distributed middleware (e.g., RabbitMQ). Orchestration can be sequenced (pipeline) or iterative (feedback loops), with checkpoints for human-in-the-loop validation. In distributed scenarios, agent replicas enable parallel processing of codebase shards, enhancing scalability for large repositories (Siddeeq et al., 11 Feb 2025, Siddeeq et al., 24 Jun 2025).

3. Execution Models, Feedback Loops, and Safety Mechanisms

Three algorithmic loop architectures are prevalent:

• Open-loop (Reactive): All transformations are executed in bulk, with minimal error correction.
• Closed-loop (Self-correcting): Each atomic or composite action is immediately followed by test and static analysis; upon failure, the agent debugs and retries before progressing. Empirical results show that closed-loop micro-iterations substantially raise success rates (e.g., a 90%+ unit test pass rate in RefAgent versus ≤ 60% for single-shot LLMs) (Oueslati et al., 5 Nov 2025).
• Hybrid loop (Human+Agent): Closed-loop at the action level, introducing approval checkpoints at macro-milestones.

Safety mechanisms include containerized execution sandboxes, incremental Git commits, plan-level rollbacks, code differencing graphs for explainability, and static-analysis gates (e.g., CodeQL, SonarQube) that preempt merging of regressions. Formal verification—e.g., SMT- or type-based proofs—can be integrated for critical transformations (Sapkota et al., 26 May 2025).

4. Empirical Methods and Benchmarking

Evaluation of agentic refactoring systems is standardized along several axes:

Metrics and Formulas:

• Correctness: Unit test pass rate $\frac{\text{PassedTests}}{\text{TotalTests}}$; minimal test post-refactoring must match or exceed baseline.
• Code quality: Cyclomatic complexity ($CC = E - N + 2P$ for each function), class LOC, WMC, fan-in/fan-out, and code-smell count reductions.
• Compression & Reusability: Code compression rate $$\mathrm{CR}(L,C) = \frac{\sum |s_i|}{|L| + \sum |c_i|}$$, code smell reduction ratio, MDL ratio.
• Opportunity Identification: F1-score comparing agentic, human, and search-based refactoring locations/types.
• Performance & Efficiency (for parallel/scientific codes): Energy usage ($E_\mathrm{net}$), runtime, average power ($P_\mathrm{avg}$), and derived metrics (e.g., energy-delay product), measured empirically on hardware (Dearing et al., 4 May 2025).

Benchmarks:

• Minicode: Agents refactor $n \approx 30$ independent solutions into a library, evaluated by correctness and code compression (Kovacic et al., 26 May 2025).
• Production Codebases: Agentic commits are mined in the wild (e.g., AIDev, 1,613 Java projects), categorized by type/abstraction level using RefactoringMiner (Horikawa et al., 6 Nov 2025).
• HeCBench: Scientific kernels for energy-aware transformations on GPUs (Dearing et al., 4 May 2025).
• Human-Likeness: CodeBLEU and AST-diff scores; perceptual studies rate readability and reusability (Xu et al., 18 Mar 2025).

5. Types, Motivations, and Limits of Agentic Refactoring

Empirical studies report that agentic refactoring, as performed by leading code agents, is intentional and common—38.6% of agentic commits in production Java projects target refactoring, with 19.8% of associated code changes being explicit restructuring operations (Horikawa et al., 6 Nov 2025).

Types:

Agent-produced refactorings are dominated by low-level, consistency-oriented edits (e.g., Change Variable Type 11.8%, Rename Parameter 10.4%), favoring local improvements, whereas human refactorings more frequently entail high-level API and system-wide design changes (54.9% human versus 43.0% agent high-level) (Horikawa et al., 6 Nov 2025).

Motivations:

Agentic refactoring is driven primarily by maintainability (52.5% of cases vs. 11.7% for humans), and readability (28.1% vs. 25.7% for humans). Agents rarely pursue deduplication or broader reuse objectives (Horikawa et al., 6 Nov 2025).

Limitations:

• Statistically significant but modest code metric improvements (e.g., median class LOC $\Delta = -15.25$ lines after agentic refactoring).
• Minimal practical reduction in code design/implementation smells (median $\Delta = 0.00$).
• Agents underperform in system-wide redesigns, architectural improvements, or non-local coupling reductions compared to humans.
• Overapplication of local refactorings may lead to overparameterization or incomplete inlining (Xu et al., 18 Mar 2025, Horikawa et al., 6 Nov 2025).

6. Advanced Applications: Library Design, Energy-Aware, and Functional Codebases

Reusable Library Extraction:

Agentic refactoring extends to automatic synthesis of general libraries from sets of related solutions. The "Librarian" method applies a sample-and-rerank pipeline, using MDL minimization or code compression as an objective, with a two-stage LLM generation + reranking process. On Minicode, it yields 1.6–2$\times$ greater compression gains over SOTA code agents while matching or improving correctness (Kovacic et al., 26 May 2025).

Multi-Agent Functional Refactoring:

Complex multi-agent LLM frameworks (e.g., for Haskell) decompose tasks into role-specific agents for code analysis, smell detection, planning, two-phase code refactoring, testing/validation, and iterative debugging. Quantitative gains include 11.03% (single-agent) to 20% (distributed) reductions in cyclomatic complexity, 13–50% performance efficiency, and up to 41.73% memory allocation savings (Siddeeq et al., 24 Jun 2025, Siddeeq et al., 11 Feb 2025).

Energy-Aware Pipelines:

Agentic refactoring incorporates physical profiling (e.g., GPU energy via NVML) and iterative, closed-loop LLM-guided optimization for energy efficiency. The LASSI-EE system achieves an average energy reduction of 47% across 85% of tested benchmarks, with built-in semantic equivalence checks via LLM-as-Judge agents (Dearing et al., 4 May 2025).

7. Implications for Practice and Future Directions

Agentic refactoring enables safe, automated delegation of routine improvement tasks while reserving high-level system design for human oversight (Horikawa et al., 6 Nov 2025). Best practices are emerging:

• Separate refactoring commits from features/fixes to promote clarity and facilitate regression tracing.
• Integrate static/code smell detectors into the agent’s loop to proactively pursue quality thresholds.
• Use closed-loop micro-iterations to maximize correctness, with human-in-the-loop gates at macro-level milestones (Sapkota et al., 26 May 2025).
• Extend agentic architectures into hybrid (vibe + agentic) models that exploit both human-guided intent and autonomous execution (Sapkota et al., 26 May 2025).
• In library synthesis, reward downstream functional reuse, not only code compression (Kovacic et al., 26 May 2025).
• For functional/energy-aware domains, supply relevant background knowledge and system summarization via self-prompting, dynamically tuning LLM sampling to escape local optima (Dearing et al., 4 May 2025, Siddeeq et al., 24 Jun 2025).

Key open challenges include scaling robustly to very large, multi-module codebases, providing stronger behavioral equivalence guarantees beyond test coverage, automatically handling interprocedural or architectural refactorings, and continuously adapting agentic strategies based on historic feedback and code evolution (Xu et al., 18 Mar 2025, Horikawa et al., 6 Nov 2025, Kovacic et al., 26 May 2025).

System	Pass Rate	Code Smell Reduction	Complexity Δ	Performance Δ
RefAgent	90%	52.5% median	—	—
Librarian	90.7%	—	1.89× CR	—
MANTRA	82.8%	—	—	—
LASSI-EE	—	—	—	47% energy saved
Haskell Multi	—	22.46% (ΔQ)	11.03%–20%	13–50% speedup

Agentic refactoring offers a modular, self-correcting paradigm for codebase evolution, with empirical validation across industrial and open-source repositories, scientific computing, and functional paradigms, but further integration with system-level reasoning and architectural planning remains an open frontier.