Optimized Refactoring Sequence for Object-Oriented Code Smells

Abstract

maintenance challenges, reduced performance, and increased technical debt. Refactoring these smells is essential to improving software quality. However, the process of sequencing refactoring’s efficiently remains a complex optimization problem. We analyse existing research on refactoring strategies, highlighting how heuristic, metaheuristic, and machine learning-based techniques have been combined to optimize refactoring decisions. Various hybrid models such as genetic algorithms, particle swarm optimization, ant colony optimization, and deep learning have been compared with our suggested hybrid metaheuristic method to balance code maintainability, modularity, and performance. Our study categorizes these methods based on their effectiveness in detecting and mitigating different types of code smells, including long methods, large classes, and feature envy. We also discuss empirical evaluations that compare different hybrid approaches, shedding light on their strengths and limitations.