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Calculating the Usage Probabilities of Statistical Usage Models by Constraints Optimization

机译：通过约束优化计算统计使用模型的使用概率

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摘要

The systematic generation of test cases from statistical usage models has been investigated recently for specific application domains, such as wireless communications or automotive applications. For Markov chain usage models, the expected usage of a hardware/software system is represented by transitions between usage states and a usage profile, meaning probability values that are attached to the state transitions. In this paper, we explain how to calculate the profile probabilities for the Markov chain usage model from a set of linear usage constraints and by optimizing a convex polyhedron that represents the constrained solution space. Comparing the computed probability distributions of our polyhedron approach with the maximum entropy technique, which is the main technique used so far, illustrates that our results are more obvious to the intented constraint semantics. In order to demonstrate the applicability of our approach, workflow testing of a complex RIS/PACS system in the medical domain was carried through and has provided promising results.
机译：最近，已经针对特定的应用领域，例如无线通信或汽车应用，研究了从统计使用模型中系统生成测试用例的情况。对于马尔可夫链使用模型，硬件/软件系统的预期使用由使用状态和使用情况配置文件之间的转换表示，这意味着附加到状态转换的概率值。在本文中，我们解释了如何根据一组线性使用约束并通过优化表示约束解空间的凸多面体来计算Markov链使用模型的轮廓概率。将我们的多面体方法的计算概率分布与到目前为止使用的主要技术最大熵技术进行比较，表明我们的结果对于意图约束语义更加明显。为了证明我们方法的适用性，在医学领域对复杂的RIS / PACS系统进行了工作流测试，并提供了可喜的结果。

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