An Improved Approach to Identifying Key Classes in Weighted Software Network
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-05
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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 h -index is suitable to identify the key classes in weighted software network.
In this paper, we introduced four h -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 k edges performs best as a whole.
American Psychological Association (APA)
Ding, Yi& Li, Bing& He, Peng. 2016. An Improved Approach to Identifying Key Classes in Weighted Software Network. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1112103
Modern Language Association (MLA)
Ding, Yi…[et al.]. An Improved Approach to Identifying Key Classes in Weighted Software Network. Mathematical Problems in Engineering No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1112103
American Medical Association (AMA)
Ding, Yi& Li, Bing& He, Peng. An Improved Approach to Identifying Key Classes in Weighted Software Network. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1112103
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112103