DefectGRANDE: Hybrid Approach for Class Imbalance in Software Defect Prediction

Software Defect Prediction (SDP) ensures software quality by identifying defects early in development. However, a major challenge is class imbalance, where defect data is vastly outnumbered by non-defect data, reducing prediction accuracy. To address this, we propose DefectGRANDE, a hybrid model leveraging SMOTE for data balancing, RandomForest for feature importance, and ensemble learning for stability. DefectGRANDE outperforms existing methods in metrics like Positive Detection (PD), Balance, and AUC. Through this, defects can be predicted more effectively in the early stages of development, enhancing software quality and reliability while reducing development costs and time.