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The original design of a software system is rarely prepared for every new requirement. Software systems should be updated frequently, which is usually accompanied by the decline in software modularity and quality. 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. In this article, we propose to use a global metric borrowed from the network science to detect the moving method refactoring. First, our approach uses a bipartite network to represent classes, features (i.e., methods and fields), and their dependencies. Second, a new metric is introduced to quantify the modularity of a software system as a whole. 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. Empirical results on the benchmark Java projects show that our approach can find meaningful methods that should be moved with a high stability. 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). Our approach provides a new way to do refactoring from the perspective of software structure.

Identifying important entities in software systems has many implications for effective resource allocation. Complex network research opens new opportunities for identifying important entities from software networks. However, the existing methods only focus on identifying important classes. Little work has been done on the identification of important packages. Moreover, the metrics they used to quantify the class importance are only designed for unweighted software networks and cannot fit in with the weighted software networks. To overcome these limitations, in this article, we introduce the weighted k-core decomposition method (Wk-core) to identify the important packages. First, we use a weighted software network to describe packages and their internal dependencies. Second, we use Wk-core to partition a software network into a layered structure. Then, the packages that are denoted by the nodes within the main core are the identified important packages. To evaluate our method, we use a variant of the susceptible–infectious–recovered model to examine the spreading influence of the nodes in six real weighted software networks. The results show that our method can well identify influential nodes, better than other four methods (i.e., original k-core decomposition, degree centrality, closeness centrality, and betweenness centrality methods). Furthermore, we demonstrate our method on two software networks and show that the important packages identified by our method are more meaningful from a software engineering perspective when compared with the other methods.