Testing Cyber-Physical Systems via Evolutionary Algorithms and Machine Learning

机译：通过进化算法和机器学习测试网络物理系统

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Cyber-Physical Systems (CPS) are systems or systems of systems made up of collaborating computational elements that control physical entities. CPS are developed in diverse domains ranging from automotive and aerospace to medical systems. This keynote argues that search-based techniques are a suitable match for testing CPS as they can handle complex continuous behaviors, scale to large test input spaces and are applicable to black-box systems such as physics-based simulators used in CPS development. In addition, the keynote demonstrates how search-based techniques can be flexibly combined with machine learning to improve search effectiveness and extend test results with explanatory information.

机译：网络物理系统（CPS）是由控制物理实体的协作计算元素组成的系统。 CPS在从汽车，航空航天到医疗系统的各个领域开发。该主题演讲认为，基于搜索的技术可以测试复杂的CPS，因为它们可以处理复杂的连续行为，可以扩展到较大的测试输入空间，并且适用于黑盒系统，例如在CPS开发中使用的基于物理的模拟器。此外，主题演讲演示了如何将基于搜索的技术灵活地与机器学习相结合，以提高搜索效率并扩展带有解释性信息的测试结果。

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《International Workshop on Search-Based Software Testing》|2019年|1-1|共1页
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Shiva Nejati;
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Testing; Machine learning; Evolutionary computation; Computational modeling; Tools; Cyber-physical systems; Heuristic algorithms;

机译：测试;机器学习;进化计算;计算建模;工具;网络物理系统;启发式算法;

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Testing Cyber-Physical Systems via Evolutionary Algorithms and Machine Learning

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