Title: An improved twin support vector machine based on multi-objective cuckoo search for software defect prediction

Authors: Yang Cao; Zhiming Ding; Fei Xue; Xiaotao Rong

Addresses: Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China ' Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China ' School of Information, Beijing Wuzi University, Beijing, 101149, China ' Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Shanxi, 030024, China

Abstract: Recently, software defect prediction (SDP) has drawn much attention as software size becomes larger and consumers hold higher reliability expectations. The premise of SDP is to guide the detection of software bugs and to conserve computational resources. However, in prior research, data imbalances among software defect modules were largely ignored to focus instead on how to improve defect prediction accuracy. In this paper, a novel SDP model based on twin support vector machines (TSVM) and a multi-objective cuckoo search (MOCS) is proposed, called MOCSTSVM. We set the probability of detection and the probability of false alarm as the SDP objectives. We use TSVM to predict defected modules and employ MOCS to optimise TSVM for this dual-objective optimisation problem. To test our approach, we conduct a series of experiments on a public dataset from the PROMISE repository. The experimental results demonstrate that our approach achieves good performance compared with other SDP models.

Keywords: software defect prediction; SDP; twin support vector machine; TSVM; multi-objective optimisation; multi-objective cuckoo search; MOCS.

DOI: 10.1504/IJBIC.2018.092808

International Journal of Bio-Inspired Computation, 2018 Vol.11 No.4, pp.282 - 291

Received: 19 Dec 2017
Accepted: 21 Mar 2018
Published online: 29 Jun 2018 *

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