  In system analysis, conformance indicates that two systems simultaneously
satisfy the same set of specifications of interest; thus, the results from
analyzing one system automatically transfer to the other, or one system can
safely replace the other in practice. In this work, we study the probabilistic
conformance of cyber-physical systems (CPS). We propose a notion of
(approximate) probabilistic conformance for sets of complex specifications
expressed by the Signal Temporal Logic (STL). Based on a novel statistical
test, we develop the first statistical verification methods for the
probabilistic conformance of a wide class of CPS. Using this method, we verify
the conformance of the startup time of the widely-used full and simplified
model of Toyota powertrain systems, the settling time of
model-predictive-control-based and neural-network-based automotive lane-keeping
controllers, as well as the maximal voltage deviation of full and simplified
power grid systems.
