Determining the capabilities of generative artificial intelligence tools to increase the efficiency of refactoring process

The object of research is a source code refactoring facilitated and proctored by generative artificial intelligence tools. The paper is aimed at assessing their impact on refactoring quality while determining their practical applicability for improving software maintainability and efficiency. The problem addressed in this research is the limitations of traditional rule-based refactoring tools, which require predefined rules and are often language-specific. Generative AI, with its advanced pattern recognition and adaptive learning capabilities, offers an alternative approach. However, its effectiveness in handling various refactoring tasks and its reliability remain undisclosed. The research involved multiple experiments, where four AI tools – ChatGPT, Copilot, Gemini, and Claude – were tested on various refactoring tasks, including code smell detection, efficiency improvements, decoupling, and large-scale refactoring. The results showed that Claude achieved the highest success rate (78.8%), followed by ChatGPT (76.6%), Copilot (72.8%), and Gemini (61.8%). While all tools demonstrated at least a basic understanding of refactoring principles, their effectiveness varied significantly depending on the complexity of the task. These results can be attributed to differences in model training, specialization, and underlying architectures. Models optimized for programming tasks performed better in structured code analysis, whereas more general-purpose models lacked depth in specific programming-related tasks. The practical implications of this research highlight that while Generative AI tools can significantly aid in refactoring, human oversight remains essential. AI-assisted refactoring can enhance developer productivity, streamline software maintenance, and reduce technical debt, making it a valuable addition to modern software development workflows.