科研之友
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摘要
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.
