Software Defect Prediction using Autoencoder Transformer Model
This addresses software quality engineering by improving defect prediction accuracy, though it appears incremental as it builds on existing ML models.
The paper tackles software defect prediction by developing an Adaptive Differential Evolution based Quantum Variational Autoencoder-Transformer (ADE-QVAET) model, which achieved high accuracy of 98.08% and F1-score of 98.12% on a dataset with 90% training data.
An AI-ML-powered quality engineering approach uses AI-ML to enhance software quality assessments by predicting defects. Existing ML models struggle with noisy data types, imbalances, pattern recognition, feature extraction, and generalization. To address these challenges, we develop a new model, Adaptive Differential Evolution (ADE) based Quantum Variational Autoencoder-Transformer (QVAET) Model (ADE-QVAET). ADE combines with QVAET to obtain high-dimensional latent features and maintain sequential dependencies, resulting in enhanced defect prediction accuracy. ADE optimization enhances model convergence and predictive performance. ADE-QVAET integrates AI-ML techniques such as tuning hyperparameters for scalable and accurate software defect prediction, representing an AI-ML-driven technology for quality engineering. During training with a 90% training percentage, ADE-QVAET achieves high accuracy, precision, recall, and F1-score of 98.08%, 92.45%, 94.67%, and 98.12%, respectively, when compared to the Differential Evolution (DE) ML model.