用量子自编码器与进化算法优化缺陷预测，提升软件质量检测精度。
Enhancing Software Quality Assurance with an Adaptive Differential Evolution based Quantum Variational Autoencoder-Transformer Model
- 结合量子自编码器与变压器捕捉高维特征和序列关系
- 自适应差分进化优化参数，提升模型收敛与预测性能
- 适合需要高精度缺陷预测的软件质量工程团队使用
一种基于人工智能的质量工程平台通过自动化缺陷预测与性能优化，提升软件质量评估能力。现有模型在处理噪声数据、类别不平衡、模式识别复杂性、特征提取效率及泛化能力方面存在不足。为此，本文提出自适应差分进化优化的量子变分自编码器-变换器模型（ADE-QVAET），融合量子变分自编码器-变换器（QVAET）以提取高维潜在特征并保持序列依赖性与上下文关系，显著提升缺陷预测准确率。自适应差分进化（ADE）通过动态参数调整增强模型收敛性与预测表现。ADE-QVAET整合先进AI技术，构建可扩展且高精度的软件缺陷预测方案，代表顶级质量工程应用的AI技术。在训练比例90%时，模型达到98.08%准确率、92.45%精确率、94.67%召回率和98.12%F1分数。
原文摘要 · Abstract (English)
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%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。