Enhancing NSGA‐II for Refactoring Recommendations: A Constraint‐Based Initialization and Adaptive Crossover Approach

Search‐based refactoring recommendation methods, particularly those utilizing the non‐dominated sorting genetic algorithm II (NSGA‐II), have shown significant promise in automating software refactoring. However, existing approaches often rely on random initialization of the population and simplistic crossover operators, which can lead to suboptimal solutions and inefficient exploration of the search space. This paper proposes ReReC, a novel refactoring recommendation approach that enhances NSGA‐II through constraint‐based initialization and an adaptive crossover operator. ReReC introduces heuristic constraint rules for common refactoring types to generate a high‐quality initial population, thereby reducing invalid refactoring operations. Furthermore, it employs an adaptive crossover strategy that distinguishes between common and differential refactoring operations, incorporating an elite gene guidance mechanism to accelerate convergence toward optimal solutions. To evaluate the effectiveness of ReReC, we evaluated it on six open‐source projects. ReReC achieves an average of 81.95% F1 score, outperforming existing tools JMove and QMove by 7.48% and 5.50%, respectively. The results demonstrate that ReReC effectively improves both the accuracy and efficiency of refactoring recommendations.