LLM-Based Code Refactoring

• LLM-Based Refactoring is the automated, context-aware transformation of source code using neural models guided by prompts, static analysis, and multi-agent orchestration.
• Key advances include agent-based workflows, fine-tuned prompt strategies, and rule-based safety checks that improve maintainability, readability, and overall code integrity.
• Implementations integrate IDE plugins, CI/CD pipelines, and DSL-driven transformations to achieve measurable gains in efficiency and code quality.

LLM-Based Refactoring is the automated, context-aware transformation of source code utilizing pretrained or instruction-tuned neural models, typically transformer architectures, guided by prompts, domain-specific rules, or multi-agent orchestration. This paradigm leverages a combination of synthetic reasoning, explicit transformation logic, and integration with code analysis tools to achieve improvements in maintainability, readability, efficiency, and structural organization, commonly across multiple programming languages and domains. Key advances include agent-based workflows for planning and validation, fine-tuned prompt engineering strategies, and the synthesis of human-like refactoring decisions at scale and high precision (Gao et al., 2024, Oueslati et al., 5 Nov 2025, Cordeiro et al., 2024, Piao et al., 4 Oct 2025, Xu et al., 18 Mar 2025, Suárez et al., 17 Jun 2025, Tapader et al., 26 Nov 2025, Liu et al., 2024, Batole et al., 26 Mar 2025, Pomian et al., 2024, Siddeeq et al., 24 Jun 2025, Siddeeq et al., 11 Feb 2025, Wadhwa et al., 2023, Shirafuji et al., 2023, Khairnar et al., 12 Aug 2025).

1. Architectural Paradigms and System Workflows

LLM-based refactoring systems exhibit a layered architecture comprising input preprocessing, context extraction, agentic collaboration, iterative refinement, and validation. Agent-based frameworks, such as RefAgent, MANTRA, and distributed Haskell systems, divide the end-to-end refactoring lifecycle among specialized agents for planning, code generation, compilation, testing, and self-reflection. These agents communicate via natural-language prompts, structured APIs, and artifact exchange (e.g., JSON, AST graphs):

• Planner/Context Agent: Performs static analysis (e.g., jdeps, AST extraction) and computes software metrics (cyclomatic complexity, cohesion, coupling).
• Refactoring Generator/Developer Agent: Synthesizes code transformations by reasoning over plans and context, often invoking few-shot RAG exemplars.
• Compiler Agent: Enforces syntactic correctness and initiates build/test suites (Maven, Gradle, stack test).
• Tester/Validation Agent: Executes regression/unit tests and provides pass/fail feedback.
• Repair/Self-Reflection Agent: Analyzes and revises failed transformations through verbal reinforcement learning (Reflexion), error-focused prompts, or agent-to-agent collaboration (Oueslati et al., 5 Nov 2025, Xu et al., 18 Mar 2025, Siddeeq et al., 24 Jun 2025, Siddeeq et al., 11 Feb 2025).

This orchestration is frequently implemented with strict iterative budgets (e.g., 20 rounds per class) and integrated with static tools (RefactoringMiner, DesigniteJava, HLint). The effectiveness is driven by the synergy between LLM-driven creativity and rule-based, programmatic safety checks.

2. Prompt Engineering, Instruction Strategies, and Domain-Specific Knowledge

LLM refactoring performance is highly sensitive to prompt structure and the design of transformation logic. Three primary prompt/instruction strategies are discerned:

• Descriptive/Step-by-Step: Natural-language, sequential instructions mirroring human refactoring manuals (e.g., Fowler's catalog), which break complex transformations into atomic steps for clarity and context preservation (Piao et al., 4 Oct 2025).
• Rule-Based/Formal: Predicate-logic or template-driven instructions encoding the precise structural changes (e.g., pattern-matching for method rename) that align with automated tooling standards.
• Goal-Focused/Objectives: High-level refactoring objectives (improve maintainability, readability, efficiency) that allow the model to select the most appropriate transformation(s), often yielding quality gains outside explicit benchmarks.

Prompt enhancement by specifying refactoring types, subcategories, and narrowing search spaces yields dramatically improved identification (from 15.6% to 86.7% recall in opportunity-identification tasks) (Liu et al., 2024). One-shot prompting and few-shot example injection enable stylistic alignment and semantic preservation across languages and refactoring types (Tapader et al., 26 Nov 2025, Cordeiro et al., 2024, Shirafuji et al., 2023).

External knowledge bases, including DSLs for test-smell elimination (Gao et al., 2024) and taxonomies for migration scenarios in quantum and classical domains (Suárez et al., 17 Jun 2025, Suárez et al., 8 Jun 2025), further guide LLM reasoning and improve precision/recall in pattern-driven transformations.

3. Technical Implementation: Algorithms and Application Domains

Implementations draw on a range of refactoring operations:

• Extract Method/Function, Inline Method, Move Method: Automated by chains of static analysis, embedding-based retrieval (e.g., semantic embeddings for "feature envy"), and IDE-integration APIs (Batole et al., 26 Mar 2025, Pomian et al., 2024, Xu et al., 18 Mar 2025).
• Assertion Elimination, Parameterization, Code Smell Reduction: Guided by DSL-based rule sets, chain-of-thought prompting, prioritization (removal, structural optimization, functional optimization), and checkpoint mechanisms (Gao et al., 2024).
• Multi-Language/Quantum-Specific Refactoring: Utilizes domain taxonomies, language-adaptive DSLs, and migration scenario patterning (e.g., Qiskit 0.46+ compatibility) (Suárez et al., 17 Jun 2025, Suárez et al., 8 Jun 2025).
• Energy-Aware Transformations: Employs staged planning, iterative LLM judgment loops, and system profiling for parallel scientific codes, integrating low-level power and performance metrics (Dearing et al., 4 May 2025).
• Distributed Multi-Agent Systems: Facilitates refactoring of Haskell and other functional codes via agent pipelines, message buses, and intermediate verification (Siddeeq et al., 24 Jun 2025, Siddeeq et al., 11 Feb 2025).

A typical LLM-driven workflow employs semantic filtering (e.g., hallucination removal), heat/popularity ranking, and self-consistency via multiple LLM runs (Batole et al., 26 Mar 2025, Pomian et al., 2024).

4. Quantitative Evaluation and Quality Metrics

Empirical studies employ a suite of formal metrics:

• Smell Reduction Rate (SRR, RR):

$RR = \frac{N_{0} - N_f}{N_0} \times 100\%$

With reported 89% reduction in test smells for UTRefactor (Gao et al., 2024); 44.36% SRR for StarCoder2 (vs. 24.27% for developers) (Cordeiro et al., 2024); 52.5% median for RefAgent (Oueslati et al., 5 Nov 2025).

• Functional Correctness:

Compilation pass rate (CPR), execution pass rate (EPR), and unit/regression test pass rates (e.g., 90% for RefAgent, 82.8% for MANTRA) (Oueslati et al., 5 Nov 2025, Xu et al., 18 Mar 2025, Cordeiro et al., 2024, Gao et al., 2024).

• Code Quality Metrics:
  • Cyclomatic complexity reduction (e.g., average ΔCC = –17.35% for Python programs (Shirafuji et al., 2023), up to –47.06% for Haskell (Siddeeq et al., 11 Feb 2025)
  • Improvements in maintainability attributes (reusability, understandability) via QMOOD computations (Oueslati et al., 5 Nov 2025).
• User/Developer Acceptance:

Developer agreement and perceived readability/reusability (e.g., 81.3% for Extract Method (Pomian et al., 2024), human parity for MANTRA (Xu et al., 18 Mar 2025)).

• Time/Efficiency:

Automated approaches typically achieve per-class or per-test transformations in seconds (e.g., 3.8s/test for UTRefactor (Gao et al., 2024)).

5. Robustness, Safety, and Limitations

LLM-based refactoring systems actively address risks of hallucinations, semantic drift, and unsafe transformations. Toolchain integration with static analysis engines (RefactoringMiner, CodeQL, SonarQube), post-processing pipelines (RefactoringMirror), and agentic orchestration (checkpointing, self-reflection, iterative repair) mitigate errors:

• RefactoringMirror achieves 0% unsafe edits after reapplication (Liu et al., 2024).
• Multi-agent feedback increases functional correctness (+64.7 pp against single-agent baselines) (Oueslati et al., 5 Nov 2025).
• Checkpointing and embedding-based filtering reduce hallucinations (e.g., 76.3% hallucination rate to 23.7% valid suggestions for Extract Method (Pomian et al., 2024)).

Limitations persist in cross-module/cross-class refactorings, non-determinism, domain-specific API mismatches, and context window restrictions for large codebases. Scope is often method-level or file-level, with extension to architectural refactorings suggested as future work.

6. Practical Applications and Best Practices

LLM-based refactoring integrates into CI/CD pipelines, IDE plugins, and educational settings:

• IDE-integrated assistants offer refactoring suggestions with automated test validation (Batole et al., 26 Mar 2025, Pomian et al., 2024, Xu et al., 18 Mar 2025).
• Multi-language frameworks support C, C++, C#, Java, Python, and quantum (Qiskit) codebases with adaptive prompt engineering and few-shot learning for transferability and stylistic generalization (Tapader et al., 26 Nov 2025, Suárez et al., 17 Jun 2025, Suárez et al., 8 Jun 2025, Shirafuji et al., 2023).
• Educational deployments scaffold prompt engineering and maintainability understanding (Khairnar et al., 12 Aug 2025).

Key best practices include: explicit specification of refactoring type and scope, injection of high-quality one-shot exemplars, use of multi-candidate generation (pass@5 or greater), pre-commit hooks for automated validation, hybrid workflows for systematic vs. contextual refactorings, and continuous static/testing feedback (Cordeiro et al., 2024, Liu et al., 2024, Piao et al., 4 Oct 2025).

7. Directions for Extension and Research

Future research envisages:

• Expansion to cross-module, architectural, and multi-file refactorings.
• Adaptive DSL rule induction, multi-language DSLs, and automated taxonomy mining for migration frameworks.
• Human-in-the-loop interactive workflows, LLM-based global quality judges, and CI-integrated agentic orchestration.
• Empirical benchmarking on larger, diverse codebases and further refinement of semantic similarity, code metric, and human-likeness scores (Tapader et al., 26 Nov 2025, Liu et al., 2024, Suárez et al., 8 Jun 2025, Suárez et al., 17 Jun 2025, Piao et al., 4 Oct 2025).

LLM-based refactoring approaches demonstrate consistently increasing performance, robustness, and applicability, rapidly approaching parity with human expert interventions for routine, systematic, and increasingly context-sensitive code transformations.