Learning-Based Testing of Cyber-Physical Systems-of-Systems: A Platooning Study [chapter]

Karl Meinke
<span title="">2017</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Learning-based testing (LBT) is a paradigm for fully automated requirements testing that combines machine learning with modelchecking techniques. LBT has been shown to be effective for unit and integration testing of safety critical components in cyber-physical systems, e.g. automotive ECU software. We consider the challenges faced, and some initial results obtained in an effort to scale up LBT to testing co-operative open cyber-physical systems-of-systems (CO-CPS). For this we focus on a case
more &raquo; ... tudy of testing safety and performance properties of multi-vehicle platoons.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-66583-2_9">doi:10.1007/978-3-319-66583-2_9</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4yxfzw2vnnfvhokvlxumd4lh44">fatcat:4yxfzw2vnnfvhokvlxumd4lh44</a> </span>
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