A systematic analysis of 59 quantum software testing empirical studies reveals highly diverse designs, inconsistent reporting, and open methodological challenges, leading to recommendations for future work.
In: Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering
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Noise from quantum hardware simulators significantly alters mutant detection distances, making equivalent mutants harder to separate from faults, with output-distribution metrics reaching 73.03% accuracy and 74.89% F1-score under device-specific thresholds.
DynaHug trains an OCSVM on dynamic runtime behaviors of benign PTMs and achieves up to 44% higher F1-score than static, dynamic, and LLM-based baselines on over 25,000 models.
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Malicious ML Model Detection by Learning Dynamic Behaviors
DynaHug trains an OCSVM on dynamic runtime behaviors of benign PTMs and achieves up to 44% higher F1-score than static, dynamic, and LLM-based baselines on over 25,000 models.