用机器学习自动识别量子程序中的不稳定测试，提升量子软件可靠性。
Identifying Flaky Tests in Quantum Code: A Machine Learning Approach
- 基于梯度提升与决策树的多模型平台，自动检测量子代码中的不稳定性测试。
- 在平衡与非平衡数据集上分别取得最高F1分数和马修斯相关系数。
- 扩展了量子不稳定性测试数据集，适合量子软件测试与可靠性研究者使用。
由于量子力学固有的复杂性（如叠加态和纠缠），量子软件的测试与调试面临重大挑战。其中，不确定性作为量子系统的基本特征，增加了量子程序中出现不稳定测试的可能性。据我们所知，现有文献尚缺乏对量子不稳定性测试的全面研究。本文提出一种新型机器学习平台，利用多种机器学习模型自动检测量子程序中的不稳定测试。评估表明，极端梯度提升（XGBoost）和基于决策树的模型在平衡数据集和非平衡数据集上分别优于随机森林、K近邻和支持向量机，分别获得最高F1分数和马修斯相关系数。此外，我们扩充了当前有限的量子不稳定测试数据集，为相关研究提供支持。未来计划探索无监督学习技术以更有效地检测和分类量子不稳定测试。这些进展旨在提高量子软件测试的可靠性和鲁棒性。
原文摘要 · Abstract (English)
Testing and debugging quantum software pose significant challenges due to the inherent complexities of quantum mechanics, such as superposition and entanglement. One challenge is indeterminacy, a fundamental characteristic of quantum systems, which increases the likelihood of flaky tests in quantum programs. To the best of our knowledge, there is a lack of comprehensive studies on quantum flakiness in the existing literature. In this paper, we present a novel machine learning platform that leverages multiple machine learning models to automatically detect flaky tests in quantum programs. Our evaluation shows that the extreme gradient boosting and decision tree-based models outperform other models (i.e., random forest, k-nearest neighbors, and support vector machine), achieving the highest F1 score and Matthews Correlation Coefficient in a balanced dataset and an imbalanced dataset, respectively. Furthermore, we expand the currently limited dataset for researchers interested in quantum flaky tests. In the future, we plan to explore the development of unsupervised learning techniques to detect and classify quantum flaky tests more effectively. These advancements aim to improve the reliability and robustness of quantum software testing.
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