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About this paper
Yuechen Li, Minqi Shao, Jianjun Zhao, Qichen Wang
Researchers from Beihang University and Kyushu University conducted a systematic methodological analysis of 59 empirical studies in Quantum Software Testing (QST), characterizing current research practices and identifying inconsistencies. The analysis provides recommendations for improving the design, execution, and reporting of future QST empirical studies, aiming for increased rigor and comparability.
Key insights
- Empirical QST studies frequently rely on Quantum Fourier Transform and custom mutation operators for generating buggy variants, with limited attention to quantum machine learning (QML) models.
- Many studies predominantly use ideal simulators (81.4%) and often do not adequately address test scalability or report statistical repetitions rigorously (only 35.6% reported repetitions).
- There is a prevalent lack of consistent baseline comparisons and statistical testing in empirical evaluations, alongside diverse approaches to handling the test oracle problem and defining performance metrics.
Summary by alphaXiv.
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