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Cyber-Physical Systems (CPS) continuously interact with their physicalenvironments through software controllers that observe the environments anddetermine actions. Engineers can verify to what extent the CPS under analysiscan achieve given goals by analyzing its Field Operational Test (FOT) logs.However, it is challenging to repeat many FOTs to obtain statisticallysignificant results due to its cost and risk in practice. To address thischallenge, simulation-based verification can be a good alternative forefficient CPS goal verification, but it requires an accurate virtualenvironment model that can replace the real environment that interacts with theCPS in a closed loop. This paper proposes a novel data-driven approach thatautomatically generates the virtual environment model from a small amount ofFOT logs. We formally define the environment model generation problem and solveit using Imitation Learning (IL) algorithms. In addition, we propose threespecific use cases of our approach in the evolutionary CPS development. Tovalidate our approach, we conduct a case study using a simplified autonomousvehicle with a lane-keeping system. The case study results show that ourapproach can generate accurate virtual environment models for CPS goalverification at a low cost through simulations.

Environment Imitation: Data-Driven Environment Model Generation Using Imitation Learning for Efficient CPS Goal Verification