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Journal of Applied Mathematics
Volume 2014, Article ID 785435, 8 pages
http://dx.doi.org/10.1155/2014/785435
Research Article

On Software Defect Prediction Using Machine Learning

1University of Electronic Science and Technology of China, Chengdu 611731, China
2Xiamen University of Technology, Xiamen 361024, China

Received 19 October 2013; Revised 2 January 2014; Accepted 16 January 2014; Published 23 February 2014

Academic Editor: Chin-Yu Huang

Copyright © 2014 Jinsheng Ren 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.

Abstract

This paper mainly deals with how kernel method can be used for software defect prediction, since the class imbalance can greatly reduce the performance of defect prediction. In this paper, two classifiers, namely, the asymmetric kernel partial least squares classifier (AKPLSC) and asymmetric kernel principal component analysis classifier (AKPCAC), are proposed for solving the class imbalance problem. This is achieved by applying kernel function to the asymmetric partial least squares classifier and asymmetric principal component analysis classifier, respectively. The kernel function used for the two classifiers is Gaussian function. Experiments conducted on NASA and SOFTLAB data sets using F-measure, Friedman’s test, and Tukey’s test confirm the validity of our methods.