Causality-Aided Falsification

Takumi Akazaki Mr.
(The University of Tokyo, JSPS Research Fellow)
Yoshihiro Kumazawa Mr.
(The University of Tokyo)
Ichiro Hasuo Dr.
(National Institute of Informatics)

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 Lukas Bulwahn, Maryam Kamali and Sven Linker: Proceedings First Workshop on Formal Verification of Autonomous Vehicles (FVAV 2017), Turin, Italy, 19th September 2017, Electronic Proceedings in Theoretical Computer Science 257, pp. 3–18.
Published: 7th September 2017.

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