Non-Contiguous Code Refactoring: A Hybrid Approach of Static Analysis and Large Language Model

The widespread adoption of large language model (LLM) in software engineering has made automated code refactoring, leveraging their powerful code comprehension and generation capabilities, a crucial direction for enhancing software quality and development efficiency. However, when refactoring non-contiguous code clones which arise from statement interleaving, reordering, and similar transformations, LLM faces core challenges: dispersed semantic context, difficulty in capturing critical dependencies, and susceptibility to “hallucination” errors. To address these challenges, we propose a novel method for non-contiguous code clone refactoring that integrates static analysis with LLM. Our method first efficiently and accurately identifies non-contiguous clones by combining program slicing with an algebraic classifier. Next, a context-aware refactoring opportunity identification algorithm determines the optimal refactoring targets for the LLM. Finally, a chain-of-thought few-shot prompting strategy guides the LLM to generate high-quality “extract method” refactoring suggestions, and a verification mechanism, inspired by metamorphic relations, validates the semantic and structural consistency of the generated results. Experiments on the open-source datasets Google Code Jam and BigCloneBench demonstrate that our proposed refactoring method reduces clone code by 66% to 71% in real-world projects like Junit. Furthermore, our detection method achieves an F1-score 2% to 18% higher than existing mainstream tools. On the Community Corpus-A refactoring opportunity identification benchmark, it reaches an F1-score of 0.415, surpassing the state-of-the-art tool GEMS by 7.5%, enhancing software quality.