Abstract: This research work deals with the challenges in software fault prediction (SFP) such as class imbalance in benchmark datasets, noisy features, and high-dimensional feature spaces. To overcome the above limitations, we propose a novel hybrid feature selection framework, FS-BWOA-COA, which incorporates Coati Optimization Algorithm (COA) for local exploitation and Beluga Whale Optimization Algorithm (BWOA) for global exploration. The two-phase optimization approach helps to avoid duplication and improves the stability of the classifier, while also helping to maintain the balance between exploration and exploitation. The framework was tested using several classifiers such as Decision Tree, SVM, KNN, and Naïve Bayes on eleven NASA PROMISE datasets. The hybrid outperforms single BWOA and COA, with an average accuracy of 0.9033 and peak values of 0.95 on the MC1 and JM1 datasets. The results of the statistical validation using the Friedman test, Wilcoxon signed-rank test, and paired t-tests confirm the same.

Key words: software fault prediction (SFP), feature selection (FS), hybrid metaheuristic optimization, exploration-exploitation balance, NASA PROMISE datasets