The Impact of Software Testing with Quantum Optimization Meets Machine Learning
This work addresses software testing efficiency for developers and QA teams, offering a transformative approach that is incremental in combining quantum and classical methods.
The paper tackles the challenge of efficient software testing by developing a hybrid framework that integrates Quantum Annealing with machine learning to optimize test case prioritization in CI/CD pipelines, achieving a 25% increase in defect detection efficiency and a 30% reduction in test execution time compared to classical ML methods.
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.