Software Defects Detection in Explainable Machine Learning Approach

In the era of ubiquitous software systems, the complexity and urgency in software production have often led to compromises in quality. Traditional testing methods are increasingly inadequate, demanding more automated solutions. This research explores the application of machine learning (ML) for Software Defect Prediction (SDP), specifically focusing on binary classification of defective and non-defective software components. Leveraging state-of-the-art ML models such as Random Forest, Artificial Neural Network (ANN), and XGBoost, the study rigorously evaluates their effectiveness on the Promise CM1 dataset. Moreover, the paper addresses the “black box” challenge by employing Explainable AI (XAI) techniques; SHapley Additive exPlanations (SHAP) is used to elucidate the models’ decision-making processes. This approach balances predictive accuracy with interpretability, fostering trust, and promoting responsible usage of automated defect prediction. The research findings offer significant advancements in software quality assurance and provide an insightful perspective on the alignment between prediction capabilities and comprehensible models.