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
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 3roles
background 2polarities
background 2representative citing papers
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.
citing papers explorer
-
A Methodological Analysis of Empirical Studies in Quantum Software Testing
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.
-
Robust Mutation Analysis of Quantum Programs Under Noise
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.
-
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.