Automated Software Refactoring Utilizing Machine Learning for Extensive Systems

Software refactoring is fundamental for maintaining both code quality and performance, particularly in extensive systems subject to changing demands. Conventional refactoring techniques are frequently manual and labor-intensive, constraining their scalability. This study investigates artificial intelligence (AI) methodologies for automating the software refactoring process, employing machine learning (ML) strategies to identify inefficient coding patterns and propose optimal restructuring methodologies. We introduce an intelligent refactoring instrument that incorporates deep learning models to evaluate software complexity and improve maintainability. The effectiveness of AI-driven refactoring is assessed based on reductions in software complexity and enhancements in developer productivity. Our results indicate that machine learningfacilitated automated refactoring considerably improves software maintainability while lessening technical debt.