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Traditional reliability assessment methods based on physical testing can require prohibitively large sample sizes in many applications. This has led manufacturers to employ virtual testing using CAE models in place of physical testing. However, when the CAE models are not valid, the resulting reliability assessment may be unreliable. In this paper we develop theory and methodology in which traditional physical testing can be used in conjunction with CAE models to create a new type of accelerated testing that requires smaller sample sizes than traditional test plans while exhibiting robustness with respect to inaccuracies in the CAE models. These test plans are implemented by physically testing a biased sample of products and employing a variance reduction technique such as importance sampling. The CAE model is used as a prior belief for failure probability from which one can derive the sampling plan which minimizes the variance.