The AI-ML quality engineering approach uses AI-ML to improve software quality testing by predicting defects. Existing ML models struggle with noisy data types, non-uniformity, pattern recognition, feature extraction, and normalization. In order to address these challenges, we develop a new model, a differential variable model (Ade) based on the Quantum End Autoencoder-Transformer (QVAET) model (Ade-Qvaet). Ade integrates with QVAET to find the highest latent factors and preserve the sequential dependence, resulting in a given factor prediction accuracy. Ade Optimization improves model transformation and predictive performance. Ade-QVaet combines AI-ML techniques such as tuning hyperparameters for limited and accurate software predictions, representing AI-ML-driven technology with advanced engineering. During training with 90% of training, Ade-qvaet achieves high accuracy, precision, recall, finding F1-Crece of 98.08%, with 98.65%, respectively, respectively, compared to the variation model (de) of ML.
