Method of Coupling Metrics for Object-Oriented Software System Based on CSBG Approach. (19th March 2020)

• Record Type: Journal Article
• Authors: Gu, Aihua; Li, Lu; Li, Shujun; Xun, Qifeng; Dong, Jian; Lin, Jianhong
• Other Names: Pan Weifeng Guest Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2020(2020)
• Publication Date: 2020-03-19
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2020/3428604
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Context . Coupling between classes is an important metric for software complexity in software systems. Objective . In order to overcome the shortcomings of the existing coupling methods and fully investigate the weighted coupling of classes in different cases in large-scale software systems, this study analyzed the relationship between classes at package level, class level, and method level. Method . The software system is considered as a set of special bipartite graphs in complex networks, and an effective method for coupling measurement is proposed as well. Furthermore, this method is theoretically proved to satisfy the mathematical properties of coupling measurement, leading to overcome the disadvantages of the majority of existing methods. In addition, it was revealed that the proposed method was efficient according to the analyses of existing methods for coupling measurement. Eventually, an algorithm was designed and a program was developed to calculate coupling between classes in three open-source software systems. Results . The results indicated the scale-free characteristic of complex networks in the statistical data. Additionally, the calculated power-law value was used as a metric for coupling measurement, so as to calculate coupling of the three open-source software. It indicated that coupling degrees of the open-source software systems contained a certain impact on evaluation of software complexity. Conclusions . It indicated that coupling degrees of the open-source software systems contained a certain impact on evaluation of software complexity. Moreover, statistical characteristics of some complex networks provided a reliable reference for further in-depth study of coupling. The empirical evidence showed that within a certain range, reducing the coupling was helpful to attenuate the complexity of the software, while excessively blindly pursuit of low coupling increases the complexity of software systems.