Detection of Fault Events in Software Tools Integrated with Human–Computer Interface Using Machine Learning

Jasem Alostad, Fayez Eid Alazmi, Ali Alfayly, Abdullah Jasim Alshehab

Software defect prediction (SDP) has emerged as a crucial task in ensuring software quality and reliability. The early and accurate identification of defect-prone modules significantly reduces maintenance costs and improves system performance. In this study, we introduce a novel hybrid model that combines Restricted Boltzmann Machines (RBM) for nonlinear feature extraction with Logistic Regression (LR) for classification. The model is validated across 21 benchmark datasets from the PROMISE and OpenML repositories. We conducted extensive experiments, including analyses of computational complexity and runtime comparisons, to assess performance in terms of accuracy, precision, recall, F1-score, and AUC. The results indicate that the RBM-LR model consistently outperforms baseline LR, as well as other leading classifiers such as Random Forest, XGBoost, and SVM. Statistical significance was affirmed using paired t-tests (p < 0.05). The proposed framework strikes a balance between interpretability and performance, with future work aimed at extending this approach through hybrid deep learning techniques and validation on industrial datasets to enhance scalability.