Testing complex simulation models 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 simulation models of cyber-physical systems, 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：测试复杂仿真模型可能成本高昂且耗时。当前探索该问题的最先进方法均为全监督方法，即要求所有示例均被标注。而本文提出的GenClu系统采用半监督方法，即：(a)仅极小部分信息通过仿真实际标注，(b)随后将这些标签扩散至其余数据。当应用于五个网络物理系统开源仿真模型时，GenClu的测试生成速度可比先前最先进方法快多个数量级。此外，通过变异测试评估时，GenClu生成的测试用例质量与本文测试的其他方法相当或更优。因此，我们推荐采用半监督方法而非先前方法（进化搜索与全监督学习）。