# The Impact of Software Testing with Quantum Optimization Meets Machine Learning

arXiv: https://arxiv.org/abs/2506.02090 · HTML: https://mlsanity.com/paper/2506.02090/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [AI-Driven Code Refactoring: Using Graph Neural Networks to Enhance Software Maintainability](https://mlsanity.com/paper/2504.10412/) (2025)
- [Code Reborn AI-Driven Legacy Systems Modernization from COBOL to Java](https://mlsanity.com/paper/2504.11335/) (2025)

## Abstract (arXiv)

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.