On Software Defect Prediction Using Machine Learning
Joint Authors
Ren, Jinsheng
Luo, Guangchun
Ma, Ying
Qin, Ke
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-23
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Ren, Jinsheng& Qin, Ke& Ma, Ying& Luo, Guangchun. 2014. On Software Defect Prediction Using Machine Learning. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-497928
Modern Language Association (MLA)
Ren, Jinsheng…[et al.]. On Software Defect Prediction Using Machine Learning. Journal of Applied Mathematics No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-497928
American Medical Association (AMA)
Ren, Jinsheng& Qin, Ke& Ma, Ying& Luo, Guangchun. On Software Defect Prediction Using Machine Learning. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-497928
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-497928