Title: A hybrid CRBA-SVM model for software defect prediction

Authors: Feixiang Li; Xiaotao Rong; Zhihua Cui

Addresses: Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Shanxi 030024, China ' Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Shanxi 030024, China ' Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Shanxi 030024, China

Abstract: Support vector machine (SVM) model is becoming an increasingly popular method in software defects prediction. This model has strong non-linear classifying ability. However, SVM model lacks effective method to determine the best parameters. In this paper, a modified bat algorithm, named changing range bat algorithm, is employed to optimise the parameters of SVM model. To test the performance of this new model, several public datasets of software defect prediction are employed and then the results are compared with other five approaches. Experimental results show that the classification ability of hybrid CRBA-SVM model surpasses all other approaches.

Keywords: support vector machines; SVM; software defects; defect prediction; bat algorithm; software faults; fault prediction; software development.

DOI: 10.1504/IJWMC.2016.076145

International Journal of Wireless and Mobile Computing, 2016 Vol.10 No.2, pp.191 - 196

Received: 22 Jul 2015
Accepted: 22 Oct 2015

Published online: 27 Apr 2016 *

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