{"ID":2856923,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10840","arxiv_id":"2510.10840","title":"Software Defect Prediction using Autoencoder Transformer Model","abstract":"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.","short_abstract":"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 E...","url_abs":"https://arxiv.org/abs/2510.10840","url_pdf":"https://arxiv.org/pdf/2510.10840v1","authors":"[\"Seshu Barma\",\"Mohanakrishnan Hariharan\",\"Satish Arvapalli\"]","published":"2025-10-12T23:03:50Z","proceeding":"cs.SE","tasks":"[\"cs.SE\",\"cs.AI\"]","methods":"[\"Transformer\",\"Variational Autoencoder\"]","has_code":false}