Causality-Aided Falsification
Causality-Aided Falsification
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DOI：
10.4204/eptcs.257.2
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发表时间：
2017-09
影响因子：
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通讯作者：
Takumi Akazaki;Yoshihiro Kumazawa;I. Hasuo
中科院分区：
文献类型：
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作者：
Takumi Akazaki;Yoshihiro Kumazawa;I. Hasuo
证伪在异构系统的质量保证中引起了人们的关注，异构系统的复杂性超出了大多数验证技术的可扩展性。在本文中，我们介绍了因果关系援助伪造的想法：通过提供一个伪造的解决方案-依赖于一定的成本函数的随机优化-与适当的因果信息表示的贝叶斯网络，搜索伪造的输入值可以是有效的。我们的实验结果证明了这一想法的可行性。
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.