DeCaF combines counterfactual generators and causal models to identify minimal input signal changes that fix CPS failures and derives interpretable assertions that generalize the recovery conditions.
On a test of whether one of two random variables is stochastically larger than the other,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
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cs.SE 2years
2026 2representative citing papers
Failure-guided local fuzzing around non-convergent seeds improves detection of faulty HQC configurations over random testing, with concolic seeding adding workload-dependent benefits on VQE versus QAOA.
citing papers explorer
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Towards Counterfactual Explanation and Assertion Inference for CPS Debugging
DeCaF combines counterfactual generators and causal models to identify minimal input signal changes that fix CPS failures and derives interpretable assertions that generalize the recovery conditions.
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Failure-Guided Fuzzing for Hybrid Quantum-Classical Programs
Failure-guided local fuzzing around non-convergent seeds improves detection of faulty HQC configurations over random testing, with concolic seeding adding workload-dependent benefits on VQE versus QAOA.