Predicting Software Defects Using Self-Organizing Data Mining

Abstract

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. INDEX TERMS Label function, software defect prediction, software metrics, self-organizing data mining. x 49 = 1.212z 82 −0.1291z 81 z 82 +0.04893z 2 82 +0.178 z 81 = 1.737x 45 −0.6062 z 82 = 1.076z 72 +0.1631z 71 z 72 z 71 = 0.03764x 22 −4.945 z 72 = −0.7929z 61 +1.735z 62 z 61 = 0.7773z 22 +0.05725z 21 z 22 z 21 = 0.9019z 12 +0.08939z 11 z 12 z 11 = 0.1439x 19 −0.9916 z 12 = 0.003072x 2 −0.05708x 9 +0.001641x 2 x 9 −0.3778 z 22 = −0.04946z 11 +1.15z 12 −0.06492z 11 z 12 z 11 = 0.4201x 36 −0.6082 z 12 = 0.003072x 2 −0.05708x 9 +0.001641x 2 x 9 −0.3778 z 62 = 0.5069z 51 +0.5196z 52 z 51 = 1.088z 42 +0.3919z 41 z 42 z 41 = 2.005x 43 −0.2611 z 42 = 0.9622z 32 −0.221z 31 z 32 +0.1441z 2 32 z 31 = 0.002816x 1 +0.0001271x 1 x 46 −0.4549 z 32 = 0.7712z 22 +0.1186z 21 z 22 +0.05782z 2 22 z 21 = 343.2x 38 −0.0417 z 22 = −0.04946z 11 +1.15z 12 −0.06492z 11 z 12 z 11 = 0.4201x 36 −0.6082 z 12 = 0.003072x 2 −0.05708x 9 +0.001641x 2 x 9 −0.3778 z 52 = 0.3663z 41 +0.4985z 42 +0.0544z 2 42 z 41 = 0.004447x 2 +0.1135x 6 −0.0008241x 2 6 −0.4806 z 42 = 0.9329z 32 −0.08852z 2 31 +0.0607z 2 32

Software Defect Prediction Utilizing Deterministic and Probabilistic Approach for Optimizing Performance through Defect Association Learning

“…This technique mainly focuses on identifying the important correlation between records [4]. There are many tasks that are important in data mining technique and one of those are to construct a fast and accurate classifier for defective records [5]. Existing association rules mostly based on classifiers having higher accuracy in classification and also based on objects set's confidence and support rules.…”