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Efficient Guiding Strategies for Testing of Temporal Properties of Hybrid Systems

机译:混合系统时间特性测试的有效指导策略

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Techniques for testing cyberphysical systems (CPS) currently use a combination of automatic directed test generation and random testing to find undesirable behaviors. Existing techniques can fail to efficiently identify bugs because they do not adequately explore the space of system behaviors. In this paper, we present an approach that uses the rapidly exploring random trees (RRT) technique to explore the state-space of a CPS. Given a Signal Temporal Logic (STL) requirement, the RRT algorithm uses two quantities to guide the search: The first is a robustness metric that quantifies the degree of satisfaction of the STL requirement by simulation traces. The second is a metric for measuring coverage for a dense state-space, known as the star discrepancy measure. We show that our approach scales to industrial-scale CPSs by demonstrating its efficacy on an automotive powertrain control system.
机译:目前,用于测试网络物理系统(CPS)的技术结合使用了自动定向测试生成和随机测试来发现不良行为。现有技术可能无法有效地识别错误,因为它们无法充分探索系统行为的空间。在本文中,我们提出一种使用快速探索随机树(RRT)技术探索CPS状态空间的方法。在给定信号时序逻辑(STL)要求的情况下,RRT算法使用两个量来指导搜索:第一个是鲁棒性度量,它通过仿真跟踪量化STL要求的满足程度。第二个是用于度量密集状态空间的覆盖率的度量,称为星际差异度量。通过展示其在汽车动力总成控制系统上的功效,我们证明了我们的方法可扩展至工业规模的CPS。

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