Causality-Aided Falsification

TL;DR

This paper introduces a causality-aided approach to falsification in complex systems, using Bayesian networks to improve the efficiency of stochastic optimization in finding falsifying inputs.

Contribution

It proposes integrating causal information via Bayesian networks into the falsification process to enhance search efficiency in complex system verification.

Findings

Bayesian networks effectively guide the falsification search.

Causality aid improves the efficiency of falsification methods.

Experimental results demonstrate the approach's viability.

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.