Optimizing Pi–Sigma Neural Networks for software defect prediction via correlation-based feature selection and robust evaluation
TL;DR
Comparative analysis shows that the developed PSNN-FS, despite its simplicity, achieves strong performance competitive with more complex architectures on the CM1 dataset.
Abstract
Software defect prediction is essential for maintaining code quality in critical domains, yet it remains challenging due to feature redundancy and class imbalance. This study proposes an optimized Pi–Sigma Neural Network (PSNN) framework leveraging Correlation-Based Feature Selection (CBFS) and Min-Max normalization. Utilizing the NASA PROMISE CM1 dataset, a 5-fold stratified cross-validation pipeline was implemented to ensure statistical robustness and prevent data leakage. Experimental results on the CM1 dataset show the refined PSNN achieves high performance (99.80% accuracy on CM1) after aggressive feature reduction to 3–5 features and a precision of 1.000. To address class imbalance, the model achieved a Matthews Correlation Coefficient (MCC) of 0.988 and a G-Mean of 0.990. Comparative analysis shows that the developed PSNN-FS, despite its simplicity, achieves strong performance competitive with more complex architectures on the CM1 dataset.