Abstract—Software defect predictive model can efficiently help improve software quality and lessen testing effort. A large number of predictive models are proposed in a software engineering literature, but this paper presents the proposed method in software defect prediction with the comparative results based on two classifiers, i.e., back propagation neural network and radial basis functions with Gaussian kernels as classifiers. Comparative results on NASA dataset are demonstrated and analyzed on the basis of mean square error and percent of accuracy. Experimental results show that the neural network performs better prediction than the RBF in almost subsets of data from 5.76% to 6.75%.
Index Terms—Software quality, software defect prediction, Software classifiers, fault-prone software modules
The authors are with the Department of Computer Science, Faculty of Science, Prince of Songkla University Hatyai, Songkhla, Thailand 90112(e-mail:firstname.lastname@example.org,email@example.com,firstname.lastname@example.org,email@example.com )
Cite: Sunida Ratanothayanon, Chouvanee Srivisal, Sirirut Vanichayobonand, and Ladda Preechaveerakul, "Comparative Classifiers for Software Quality Assessment," International Journal of Engineering and Technology vol. 4, no. 4, pp. 404-408, 2012.