An AI-powered quality engineering platform uses artificial intelligence to boost software quality assessments through automated defect prediction and optimized performance alongside improved feature extraction. Existing models result in difficulties addressing noisy data types together with imbalances, pattern recognition complexities, ineffective feature extraction, and generalization weaknesses. To overcome those existing challenges in this research, we develop a new model Adaptive Differential Evolution based Quantum Variational Autoencoder-Transformer Model (ADE-QVAET), that combines a Quantum Variational Autoencoder-Transformer (QVAET) to obtain high-dimensional latent features and maintain sequential dependencies together with contextual relationships, resulting in superior defect prediction accuracy. Adaptive Differential Evolution (ADE) Optimization utilizes an adaptive parameter tuning method that enhances model convergence and predictive performance. ADE-QVAET integrates advanced AI techniques to create a robust solution for scalable and accurate software defect prediction that represents a top-level AI-driven technology for quality engineering applications. The proposed ADE-QVAET model attains high accuracy, precision, recall, and f1-score during the training percentage (TP) 90 of 98.08%, 92.45%, 94.67%, and 98.12%.

翻译：人工智能驱动的质量工程平台通过自动化缺陷预测与性能优化，结合改进的特征提取技术，利用人工智能提升软件质量评估能力。现有模型在处理噪声数据类型与数据不平衡、模式识别复杂性、特征提取效率低下以及泛化能力薄弱等方面存在不足。为克服这些挑战，本研究提出一种新型模型——基于自适应差分进化的量子变分自编码器-Transformer模型（ADE-QVAET）。该模型融合量子变分自编码器-Transformer（QVAET）以获取高维潜在特征，同时保持序列依赖性与上下文关联，从而实现卓越的缺陷预测精度。自适应差分进化（ADE）优化采用自适应参数调谐方法，有效提升模型收敛速度与预测性能。ADE-QVAET通过集成先进人工智能技术，构建了可扩展且精确的软件缺陷预测解决方案，代表了质量工程应用领域的尖端人工智能驱动技术。所提出的ADE-QVAET模型在训练比例（TP）达到90%时，准确率、精确率、召回率与F1分数分别达到98.08%、92.45%、94.67%和98.12%。