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arXiv · 2506.02090

The Impact of Software Testing with Quantum Optimization Meets Machine Learning

Gopichand Bandarupalli
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Abstract

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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Gopichand Bandarupalli. 2025-06-02. The Impact of Software Testing with Quantum Optimization Meets Machine Learning. https://arxiv.org/abs/2506.02090

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