Causality-Aided Falsification

Abstract

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.

In Proceedings FVAV 2017, arXiv:1709.02126

References in corpus (2)

• Active Requirement Mining of Bounded-Time Temporal Properties of Cyber-Physical Systems
• Input Synthesis for Sampled Data Systems by Program Logic

Cited by in corpus (3)

• A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems
• Multi-Armed Bandits for Boolean Connectives in Hybrid System Falsification (Extended Version)
• Constraining Counterexamples in Hybrid System Falsification: Penalty-Based Approaches