Causality-Aided Falsification
Systems and Control
2017-09-11 v1 Artificial Intelligence
Machine Learning
Logic in Computer Science
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.
Keywords
Cite
@article{arxiv.1709.02555,
title = {Causality-Aided Falsification},
author = {Takumi Akazaki and Yoshihiro Kumazawa and Ichiro Hasuo},
journal= {arXiv preprint arXiv:1709.02555},
year = {2017}
}
Comments
In Proceedings FVAV 2017, arXiv:1709.02126