Refactoring improves maintainability without altering externally observable behavior,yet it remains costly and error-prone when applied manually at scale.While large language models(LLMs)can generate plausible refacto...Refactoring improves maintainability without altering externally observable behavior,yet it remains costly and error-prone when applied manually at scale.While large language models(LLMs)can generate plausible refactorings,practical adoption is limited by uncontrolled edit scope,inconsistent outputs under stochastic decoding,and weak traceability of why a change was produced.This paper proposes a smell-targeted,scope-bound refactoring framework for JavaScript that couples deterministic AST-based smell detection with constrained LLM transformation.The key design principle is to bind generation to explicitly detected smell instances,enforce a structured output contract(refactored code plus per-smell rationale),and log full refactoring artifacts for repeatable evaluation.We implement the framework as a microservice-based prototype(detector,prompt construction and routing,orchestrator,analytics,and UI)and evaluate it on LeetCode-style solutions and multiple GitHub repositories.Across the evaluated projects,the approach achieves an average smell reduction of 83.96%and an average maintainability index improvement of+5.366,while maintaining a mean developer acceptance rate of 91.66%.A targeted temperature study identifies an operating point around 0.4 that maximizes acceptance(approximately 95%in controlled trials),balancing determinism with sufficient flexibility for structure-improving edits.These results suggest that explicit scope control and structured traceability are central to making LLM-based refactoring reliable and reviewable,and motivate future integration with automated validation(tests,linting)and repository-conditioned policies.展开更多
摘要Refactoring improves maintainability without altering externally observable behavior,yet it remains costly and error-prone when applied manually at scale.While large language models(LLMs)can generate plausible refactorings,practical adoption is limited by uncontrolled edit scope,inconsistent outputs under stochastic decoding,and weak traceability of why a change was produced.This paper proposes a smell-targeted,scope-bound refactoring framework for JavaScript that couples deterministic AST-based smell detection with constrained LLM transformation.The key design principle is to bind generation to explicitly detected smell instances,enforce a structured output contract(refactored code plus per-smell rationale),and log full refactoring artifacts for repeatable evaluation.We implement the framework as a microservice-based prototype(detector,prompt construction and routing,orchestrator,analytics,and UI)and evaluate it on LeetCode-style solutions and multiple GitHub repositories.Across the evaluated projects,the approach achieves an average smell reduction of 83.96%and an average maintainability index improvement of+5.366,while maintaining a mean developer acceptance rate of 91.66%.A targeted temperature study identifies an operating point around 0.4 that maximizes acceptance(approximately 95%in controlled trials),balancing determinism with sufficient flexibility for structure-improving edits.These results suggest that explicit scope control and structured traceability are central to making LLM-based refactoring reliable and reviewable,and motivate future integration with automated validation(tests,linting)and repository-conditioned policies.