Successful deployment of Deep Neural Networks (DNNs), particularly in safety-critical systems, requires their validation with an adequate test set to ensure a sufficient degree of confidence in test outcomes. Mutation analysis, one of the main techniques for measuring test adequacy in traditional software, has been adapted to DNNs in recent years. This technique is based on generating mutants that aim to be representative of actual faults and thus can be used for test adequacy assessment. In this paper, we investigate for the first time whether mutation operators that directly modify the trained DNN model (i.e., post-training) can be used for reliably assessing the test inputs of DNNs. We propose and evaluate TEASMA, an approach based on post-training mutation for assessing the adequacy of DNN's test sets. In practice, TEASMA allows engineers to decide whether they will be able to trust test results and thus validate the DNN before its deployment. Based on a DNN model's training set, TEASMA provides a methodology to build accurate prediction models of the Fault Detection Rate (FDR) of a test set from its mutation score, thus enabling its assessment. Our large empirical evaluation, across multiple DNN models, shows that predicted FDR values have a strong linear correlation (R2 >= 0.94) with actual values. Consequently, empirical evidence suggests that TEASMA provides a reliable basis for confidently deciding whether to trust test results or improve the test set.

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