Model-based testing of stochastic systems with IOCO theory

M. Gerhold, M. Stoelinga
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引用次数: 2

Abstract

We present essential concepts of a model-based testing framework for probabilistic systems with continuous time. Markov automata are used as an underlying model. Key result of the work is the solid core of a probabilistic test theory, that incorporates real-time stochastic behaviour. We connect ioco theory and hypothesis testing to infer about trace probabilities. We show that our conformance relation conservatively extends ioco and discuss the meaning of quiescence in the presence of exponentially distributed time delays.
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基于IOCO理论的随机系统模型测试
提出了一种基于模型的连续时间概率系统测试框架的基本概念。使用马尔可夫自动机作为底层模型。这项工作的关键成果是概率测试理论的坚实核心,它包含了实时随机行为。我们将ioco理论和假设检验联系起来,推断出跟踪概率。我们证明了我们的一致性关系保守地扩展了ioco,并讨论了存在指数分布时滞时的静态的意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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