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Since the development of computer science, our lives have become increasingly dependent on software. While we enjoy the benefits and convenience that software programs provide, we cannot ignore issues with software reliability, complexity, and security. Since the introduction of complex networks, people have been using software network to analyze software problems; however, traditional software network models are not currently capable of analyzing software with large scale and complex structures. In this paper, a new software network model is proposed, with which each node in the network can be assigned a set of coordinates that reflect its function-call information and make the disorder of the network graph more orderly. Additionally, characteristics and derivatives of the model are thoroughly examined and analyzed. A case study using the coordinate model combined with bug information is then conducted to analyze five different software programs. The results show that the proposed model can be used to analyze the relationship between nodes or defects distribution and software network parameters, as well as high-risk module excavation through a defect density analysis. Compared to traditional software network models, the model maintains the inner logic relationship of the software systems better, which makes it easier to analyze many aspects of software.

Identifying the most important classes in a software system is crucial for engineers to understand or maintain an unfamiliar system. Complex network theory provides a new way to study the characteristics of large-scale software. Unfortunately, most current studies consider only one or a limited number of factors affecting software structure, rendering inaccurate the results of mining the key classes of software. Thus, we propose an approach using various complex network metrics to automatically identify key classes from global and local aspects. From the global aspect, the location of a class and its ability to control the information flow of software are mainly considered. From local aspects, we focus on the interactions of classes with their neighbors, as well as the complexity of the class itself. Experiments are performed on two java open-source projects. Results show that this approach can accurately identify key classes compared with existing literature.