![]() ![]() The advantages of our approach are illustrated in comparison with some other approaches, specifically one refactoring approach, namely search-based refactoring approach (SBRA), and two community detection algorithms, namely a graph theoretic clustering algorithm (MCODE) and a fast algorithm for community detection (FG). Empirical results on the benchmark Java projects show that our approach can find meaningful methods that should be moved with a high stability. Finally, a crossover-only evolutionary algorithm that uses the metric as its fitness function is introduced to optimize the class structure of a software system and detect the methods that should be moved. Second, a new metric is introduced to quantify the modularity of a software system as a whole. First, our approach uses a bipartite network to represent classes, features (i.e., methods and fields), and their dependencies. ![]() In this article, we propose to use a global metric borrowed from the network science to detect the moving method refactoring. Although many approaches have been proposed to improve the quality of software, a majority of them are guided by metrics defined on the local properties of software. Software systems should be updated frequently, which is usually accompanied by the decline in software modularity and quality. ![]() The original design of a software system is rarely prepared for every new requirement. ![]()
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