Title: Adaptive two-SVM multi-objective cuckoo search algorithm for software defect prediction

Authors: Yun Niu; Zeyu Tian; Maoqing Zhang; Xingjuan Cai; Jianwei Li

Addresses: Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China ' School of Optics and Photonics, Beijing Institute of Technology, Beijing, China ' Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China ' Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China ' Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, China

Abstract: Two-support vector machine is a new prediction model for software defect. For this model, one multi-objective oriented cuckoo search is designed to optimise several objects simultaneously to improve the defect accuracy, and the ratio of dataset plays an important role to determine the number of big/small modules. In this paper, we provide one extension for the multi-objective oriented cuckoo search, so that it can also adaptive optimise this ratio. Simulation results show our modification achieves the best performance when compared with two other software defect prediction models.

Keywords: software defect prediction; support vector machine; SVM; multi-objective oriented cuckoo search.

DOI: 10.1504/IJCSM.2018.10017541

International Journal of Computing Science and Mathematics, 2018 Vol.9 No.6, pp.547 - 554

Received: 16 Mar 2018
Accepted: 03 Apr 2018

Published online: 16 Nov 2018 *

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