Implementation of LSSVM in Classification of Software Defect Prediction Data with Feature Selection

Software defect prediction enhances the quality, efficiency, and effectiveness of time and expenses for software testing by focusing on defect modules. Software defect prediction technology uses machine learning to predict defect modules, making allocating limited resources easier quickly. Software defect prediction datasets naturally have imbalanced class problems with significantly few defective modules compared to non-defective modules. In this study, software defect prediction data was classified by implementing the LSSVM algorithm with ReliefF (K=10) feature selection and applying the SMOTE method to overcome the imbalanced class problem in the dataset. The datasets used are software defect prediction datasets of the public NASA MDP Promise project, namely CM1, PC1, KC1, and KC2. Dataset divided into training and testing data using Fold Cross-Validation with ten folds. The classifier achieved the highest average accuracy on the PC1 dataset, which was 93,87%, while the highest Area Under the ROC Curve (AUC) was achieved by the classifier for the KC2 dataset, which was 78,35%. The results also indicate that AUC values for classifiers that use SMOTE always higher than non-SMOTE in each dataset