Causality-Aided Falsification
This addresses quality assurance challenges for complex systems, but appears incremental as it builds on existing falsification methods with causal information.
The paper tackled the problem of efficiently finding falsifying inputs for complex heterogeneous systems by introducing causality-aided falsification, which uses a Bayesian network to guide stochastic optimization, and experimental results demonstrated its viability.
Falsification is drawing attention in quality assurance of heterogeneous systems whose complexities are beyond most verification techniques' scalability. In this paper we introduce the idea of causality aid in falsification: by providing a falsification solver -- that relies on stochastic optimization of a certain cost function -- with suitable causal information expressed by a Bayesian network, search for a falsifying input value can be efficient. Our experiment results show the idea's viability.