  One of the advantages of adopting a Model Based Development (MBD) process is
that it enables testing and verification at early stages of development.
However, it is often desirable to not only verify/falsify certain formal system
specifications, but also to automatically explore the properties that the
system satisfies. In this work, we present a framework that enables property
exploration for Cyber-Physical Systems. Namely, given a parametric
specification with multiple parameters, our solution can automatically infer
the ranges of parameters for which the property does not hold on the system. In
this paper, we consider parametric specifications in Metric or Signal Temporal
Logic (MTL or STL). Using robust semantics for MTL, the parameter mining
problem can be converted into a Pareto optimization problem for which we can
provide an approximate solution by utilizing stochastic optimization methods.
We include algorithms for the exploration and visualization of multi-parametric
specifications. The framework is demonstrated on an industrial size,
high-fidelity engine model as well as examples from related literature.
