A software DP (defects prediction) model based on SVM (support vector machine)

Software defects prediction can help raise the effectiveness and efficiency of testing activities by constructing predictive classification models from static code attributes which can identify software modules with a higher than usual probability of defects. Our aim is to find the best performance predictive classification model through introducing SVM into DP. Sections 1 through 4 of the full paper explain our SVM-DP model and its application to analyzing the 13 data sets of NASA Metrics Data Program (MDP). Sections 1 through 4 are entitled: Iterative and Incremental Prediction Model SVM-DP (section 1); Benchmarking Data Sets and Code Metrics (section 2); Effectiveness Indicators (section 3); Experimental Method and Analysis of Test Results (section 4). Experimental results, presented in Table 4 and Figs. 4 through 7, and their analysis, show preliminarily the effectiveness of our SVM-DP model.