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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 3858637, 9 pages
Research Article

An Improved Approach to Identifying Key Classes in Weighted Software Network

Yi Ding,1,2 Bing Li,1,3,4 and Peng He5

1State Key Lab of Software Engineering and School of Computer, Wuhan University, Wuhan 430072, China
2School of Computer, Wuhan Vocational College of Software and Engineering, Wuhan 430205, China
3International School of Software, Wuhan University, Wuhan 430072, China
4Research Center of Complex Network, Wuhan University, Wuhan 430072, China
5Faculty of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China

Received 3 June 2016; Accepted 7 August 2016

Academic Editor: Emilio Insfran

Copyright © 2016 Yi Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To help the newcomers understand a software system better during its development, the key classes are in general given priority to be focused on as soon as possible. There are numerous measures that have been proposed to identify key nodes in a network. As a metric successfully applied to evaluate the productivity of a scholar, little is known about whether -index is suitable to identify the key classes in weighted software network. In this paper, we introduced four -index variants to identify key classes on three open-source software projects (i.e., Tomcat, Ant, and JUNG) and validated the feasibility of proposed measures by comparing them with existing centrality measures. The results show that the measures proposed not only are able to identify the key classes but also perform better than some commonly used centrality measures (the improvement is at least 0.215). In addition, the finding suggests that mE-Weight defined by the weight of a node’s top edges performs best as a whole.