Abstract
Model-based verification of automotive electronic control units (ECUs) must ensure compliance with the target requirements in a short time frame. On the other hand, an increasing number of sources of variability (e.g. operating conditions, block parameters) impact system performance. To reduce the overall verification effort, much focus has been put on performance in simulation, and little on how to plan simulation experiments to yield maximum information with minimum number of runs. This paper shows how the classical framework of statistical Design of Experiments can be extended to perform faster and more reliable multivariable sensitivity and worst-case studies. Simulation experiments of a state of the art ECU modelled in SystemC/SystemC-AMS show significant increase in efficiency as compared to traditional approaches.
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