The Impact of Software Testing with Quantum Optimization Meets Machine Learning

TL;DR

This paper introduces a hybrid quantum-classical framework that significantly improves software test case prioritization efficiency, reducing testing time and increasing defect detection in CI/CD pipelines using quantum optimization techniques.

Contribution

It presents a novel integration of quantum annealing with machine learning for test prioritization, addressing scalability and hardware limitations in modern software testing.

Findings

25% increase in defect detection efficiency

30% reduction in test execution time

Robustness across evolving codebases

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.