  Cyber-Physical Systems (CPS) continuously interact with their physical
environments through software controllers that observe the environments and
determine actions. Engineers can verify to what extent the CPS under analysis
can achieve given goals by analyzing its Field Operational Test (FOT) logs.
However, it is challenging to repeat many FOTs to obtain statistically
significant results due to its cost and risk in practice. To address this
challenge, simulation-based verification can be a good alternative for
efficient CPS goal verification, but it requires an accurate virtual
environment model that can replace the real environment that interacts with the
CPS in a closed loop. This paper proposes a novel data-driven approach that
automatically generates the virtual environment model from a small amount of
FOT logs. We formally define the environment model generation problem and solve
it using Imitation Learning (IL) algorithms. In addition, we propose three
specific use cases of our approach in the evolutionary CPS development. To
validate our approach, we conduct a case study using a simplified autonomous
vehicle with a lane-keeping system. The case study results show that our
approach can generate accurate virtual environment models for CPS goal
verification at a low cost through simulations.
