Predicting Software Defects Using Self-Organizing Data Mining

The study predicts the software defect of ranking and classification by utilizing the self-organizing data mining method. The causal relation between software metrics and defects in software modules is established. In the analysis, software metric parameters are considered as the influencing factors and independent variables; defect label values of software modules are considered as dependent variables. When ranking is predicted during the model training process, the bugs of the defect-free modules are replaced with a negative value and those of the defective modules remain unchanged. During classification predictions, the false values of the defect-free modules are replaced with a negative value, whereas the true values of the defective modules are replaced with a positive value ≥1.5. Then, case studies and comparison based on data sets of NASA, SoftLab and Promise are conducted by imposing different algorithms. The results show that in the ranking tests, the self-organizing data mining method achieves the smallest errors. In the classification tests, the F-measure values obtained in self-organizing data mining method are the most optimal among the tested algorithms. The self-organizing data mining method is high efficiency and feasible for predicting the software defects.