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Journal of Applied Mathematics
Volume 2014, Article ID 675368, 14 pages
Research Article

Discrimination Analysis for Predicting Defect-Prone Software Modules

1School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
2School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

Received 19 October 2013; Revised 23 December 2013; Accepted 14 January 2014; Published 25 February 2014

Academic Editor: Osamu Mizuno

Copyright © 2014 Ying Ma 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.


Software defect prediction studies usually build models without analyzing the data used in the procedure. As a result, the same approach has different performances on different data sets. In this paper, we introduce discrimination analysis for providing a good method to give insight into the inherent property of the software data. Based on the analysis, we find that the data sets used in this field have nonlinearly separable and class-imbalanced problems. Unlike the prior works, we try to exploit the kernel method to nonlinearly map the data into a high-dimensional feature space. By combating these two problems, we propose an algorithm based on kernel discrimination analysis called KDC to build more effective prediction model. Experimental results on the data sets from different organizations indicate that KDC is more accurate in terms of -measure than the state-of-the-art methods. We are optimistic that our discrimination analysis method can guide more studies on data structure, which may derive useful knowledge from data science for building more accurate prediction models.