用量子自编码器与进化算法结合，提升软件缺陷预测准确率。
Software Defect Prediction using Autoencoder Transformer Model
- 融合量子自编码器与自适应差分进化算法，提取高维特征并保持序列关系
- 在90%训练集下，准确率98.08%，F1-score达98.12%，显著优于传统模型
- 适合软件质量工程、AI辅助测试团队快速定位潜在缺陷
一种基于AI-ML的质量工程方法利用人工智能与机器学习技术提升软件质量评估能力，实现缺陷预测。现有机器学习模型在噪声数据、类别不平衡、模式识别、特征提取和泛化能力方面存在挑战。为此，本文提出一种新型模型——基于自适应差分进化算法的量子变分自编码器-变压器模型（ADE-QVAET）。该模型通过自适应差分进化优化，增强收敛性与预测性能，结合量子自编码器提取高维潜在特征，并保持序列依赖性，从而提升缺陷预测精度。在90%训练比例下，ADE-QVAET模型在准确率、精确率、召回率和F1分数上分别达到98.08%、92.45%、94.67%和98.12%，显著优于传统差分进化（DE）模型。
原文摘要 · Abstract (English)
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.
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