Multi objective binary Rao feature optimization for software defect prediction using machine learning models

Software defect prediction (SDP) is crucial for enhancing software quality and reliability, but class imbalance and irrelevant features hinder its performance, necessitating dataset optimization. In this work, MOBR first generates solutions using the Rao algorithm, and then a diverse set of Pareto optimal solutions are produced for decision-making. A multi-criteria decision-making technique called the weighted sum method selects the optimal feature set from the Pareto front. To evaluate the effectiveness of the MOBR approach, a comparison is conducted with Chi-Square (CS), Information Gain (IG), Single Objective Binary Rao (BR), and Multi-objective NSGA-II over twenty-four benchmark Promise and NASA datasets with imbalanced and balanced data using Decision Tree (DT), K Nearest Neighbour (KNN), Naive Bayes (NB), and Support Vector Machine (SVM) prediction models. Our MOBR model is evaluated using five assessment measures and statistically compared, further win-draw-loss comparison performed to compare each prediction model. The experimental and statistical analysis demonstrates that In terms of FOR, Recall, F1-Score, AUC, and G-mean, MOBR gets top ranks of 3.5, 1, 5, 2.5, and 1 over unbalanced data and 3, 1.5, 1.5, 6, 2.5 over balanced data, respectively. Moreover, MOBR with DT gives superior performance. Hence, it can be concluded that the MOBR can effectively improve model performance while minimizing the number of features.