Improving Ranking-Oriented Defect Prediction Using a Cost-Sensitive Ranking SVM.
Abstract
Context: Ranking-oriented defect prediction (RODP) ranks software modules to allocate limited testing resources to each module according to the predicted number of defects. Most RODP methods overlook that ranking a module with more defects incorrectly makes it difficult to successfully find all of the defects in the module due to fewer testing resources being allocated to the module, which results in much higher costs than incorrectly ranking the modules with fewer defects, and the numbers of defects in software modules are highly imbalanced in defective software datasets. Cost-sensitive learning is an effective technique in handling the cost issue and data imbalance problem for software defect prediction. However, the effectiveness of cost-sensitive learning has not been investigated in RODP models. Aims: In this article, we propose a cost-sensitive ranking support vector machine (SVM) (CSRankSVM) algorithm to improve the performance of Manuscript received June 1, 2018; revised October 20, 2018 and April 21, 2019; accepted July 14, 2019. Date of publication August 22, 2019; date of current version March 2, 2020. This work was supported in part by the National Key R&D Program of China under Grant 2018YFC1604000, in part by the National Natural Science Foundation of China under Grant 61572374, Grant U163620068, Grant U1135005, Grant 61572371, and Grant 61772525, in part by the Open Fund of Key Laboratory of Network Assessment Technology from CAS, Guangxi Key Laboratory of Trusted Software under Grant kx201607, in part by the Academic Team Building Plan for Young Scholars from Wuhan University under Grant WHU2016012, in part by the General Research Fund of the Research Grants Council of Hong Kong under Grant 11208017, in part by the research funds of City University of Hong Kong under Grant 9678149, Grant 7005028, and Grant 9678149, in part by the Research Support Fund by Intel under Grant 9220097, and in part by a start-up fund under Grant 9B0V from the Hong Kong Polytechnic University. Associate Editor: NASAC Special. (Corresponding authors: Jin Liu; Jacky Wai Keung.) X. Yu is with the School of Computer Science, Wuhan University, Wuhan 430072, China, and also