Analysis of Important Features in Software Defect Prediction Using Synthetic Minority Oversampling Techniques (SMOTE), Recursive Feature Elimination (RFE) and Random Forest - Ghinaya, Helma; Herteno, Rudy; Faisal, Mohammad Reza; Farmadi, Andi; Indriani, Fatma (Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA, 2024)
Software Defect Prediction (SDP) is essential for improving software quality during testing. As software systems grow more complex, accurately predicting defects becomes increasingly challenging. One of the challenges faced is dealing with imbalanced class distributions, where the number of defective instances is significantly lower than non-defective ones. To tackle the imbalanced class issue, use the SMOTE technique. Random Forest as a classification algorithm is due to its ability to handle non-linear data, its resistance to overfitting, and its ability to provide information about the importance of features in classification. This research aims to evaluate important features and measure accuracy in SDP using the SMOTE+RFE+Random Forest technique. The dataset used in this study is NASA MDP D