Testing complex Cyber Physical Systems (CPSs) can be expensive and time consuming. Current state-of-the-art methods that explore this problem are fully-supervised; i.e. they require that all examples are labeled. On the other hand, the GenClu system (introduced in this paper) takes a semi-supervised approach; i.e. (a) only a small subset of information is actually labeled (via simulation) and (b) those labels are then spread across the rest of the data. When applied to five open-source CPSs, GenClu's test generation can be multiple orders of magnitude faster than the prior state of the art. Further, when assessed via mutation testing, tests generated by GenClu were as good or better than anything else tested here. Hence, we recommend semi-supervised methods over prior methods (evolutionary search and fully-supervised learning).

翻译：测试复杂的信息物理系统（CPS）可能既昂贵又耗时。当前探索该问题的先进方法均为全监督方式，即要求所有样本均被标注。而本文提出的GenClu系统采用半监督方法，具体而言：(a) 仅对一小部分信息通过仿真进行实际标注，(b) 随后将这些标注扩散至其余数据。当应用于五个开源CPS时，GenClu的测试生成速度比先前最先进方法快多个数量级。此外，通过变异测试评估，GenClu生成的测试用例质量与本文测试的其他方法相当甚至更优。因此，我们推荐采用半监督方法（而非先前的进化搜索和全监督学习方法）。