Ensemble Kernel-Mapping-Based Ranking Support Vector Machine for Software Defect Prediction

Rank-oriented software defect prediction (ROSDP) aims to establish a model to predict the testing priority of software modules according to defect severity for the reasonable allocation of limited testing resources. Some ROSDP methods construct the prediction model by a linear model with respect to software features. However, in software repositories, the linear condition between testing priority and software feature is not satisfied, and the ranking performance of the linear prediction model is limited. Thus, in order to relax the limitation of the linear prediction model and improve the ranking performance, ensemble kernel-mapping-based ranking support vector machine (EKMRSVM) is developed based on the theories of ranking SVM, which builds a nonlinear ranking function approximated by the kernel-mapping-based method. Furthermore, the sequential minimal optimization algorithm is developed to derive the ideal parameters of the nonlinear ranking function, and ensemble learning is introduced to reduce time costs and guarantee ranking performance. Experimental results on 20 open source datasets indicate that introducing the kernel mapping method in EKMRSVM is very effective in performance improvement, and ensemble learning makes the proposed ranking algorithm very competitive in terms of time costs. Thus, based on the comparative results of some baseline methods, EKMRSVM with the appropriate kernel function can achieve better ranking performance.