  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).
