Quantum-Inspired Intelligence for Software Fault Prediction: A Hybrid Ensemble Learning Approach

Software defect prediction (SDP) is critical for ensuring software quality by identifying faulty modules early. However, traditional machine learning approaches can be limited by irrelevant features and model bias. In this paper, we propose a quantum-enhanced hybrid machine learning framework for SDP that integrates quantum algorithms for feature optimization with an ensemble of quantum and classical classifiers. We employ a Quantum Genetic Algorithm (QGA) for feature selection and a Quantum Wavelet Transform (QWT) for feature transformation to improve data representation. The selected and transformed features are then classified using a hybrid ensemble comprising a Quantum Support Vector Classifier (QSVC), a Quantum Random Forest (QRF) classifier, and a classical Support Vector Machine (SVM). A majority-voting mechanism combines these models' predictions. We evaluate the approach on five benchmark SDP datasets (MC1, CM1, KC2, KC3, PC1). Experimental results show that our quantum-enhanced ensemble achieves superior accuracy, precision, recall, and F1-score compared to traditional methods. We present confusion matrices, ROC curves, and training loss convergence plots for each dataset, demonstrating consistently improved defect prediction performance. The proposed approach contributes a novel integration of quantum computing into software engineering, highlighting the potential of quantum-classical hybrid models to advance defect prediction.