The Impact of Software Testing with Quantum Optimization Meets Machine Learning

--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% increase in defect detection efficiency and a 30% 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. Software testing is integral to ensuring software quality, accounting for 40-50% of development resources in large-scale systems [1]. The rise of microservices, cloud-native architectures, and continuous integration/continuous deployment (CI/CD) practices has intensified the demand for rapid, reliable testing methods [2].