用量子优化提升测试效率，缺陷发现快25%，执行时间降30%。
The Impact of Software Testing with Quantum Optimization Meets Machine Learning
- 结合量子退火与机器学习，优化持续集成中的测试用例优先级。
- 在Defects4J数据集上，缺陷检测效率提升25%，测试时间减少30%。
- 适合关注未来量子-经典混合系统的软件质量保障研究者。
现代软件系统复杂性给高效测试带来挑战，传统机器学习在大规模测试用例中表现不佳。本研究提出一种融合量子退火与机器学习的混合框架，用于优化持续集成与部署（CI/CD）流水线中的测试用例优先级。利用量子优化技术，在Defects4J数据集上实现了缺陷检测效率提升25%，测试执行时间减少30%。模拟的CI/CD环境验证了其在代码库演化中的鲁棒性。通过缺陷热力图和性能图等可视化手段增强可解释性。该框架应对了量子硬件限制、CI/CD集成及面向2025年混合量子-经典生态系统的可扩展性问题，为软件质量保障提供了一种变革性方法。
原文摘要 · Abstract (English)
Modern software systems complexity challenges efficient testing, as traditional machine learning (ML) struggles with large test suites. This research presents a hybrid framework integrating Quantum Annealing with ML to optimize test case prioritization in CI/CD pipelines. Leveraging quantum optimization, it achieves a 25 percent increase in defect detection efficiency and a 30 percent reduction in test execution time versus classical ML, validated on the Defects4J dataset. A simulated CI/CD environment demonstrates robustness across evolving codebases. Visualizations, including defect heatmaps and performance graphs, enhance interpretability. The framework addresses quantum hardware limits, CI/CD integration, and scalability for 2025s hybrid quantum-classical ecosystems, offering a transformative approach to software quality assurance.
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