  Surprise Adequacy (SA) is one of the emerging and most promising adequacy
criteria for Deep Learning (DL) testing. As an adequacy criterion, it has been
used to assess the strength of DL test suites. In addition, it has also been
used to find inputs to a Deep Neural Network (DNN) which were not sufficiently
represented in the training data, or to select samples for DNN retraining.
However, computation of the SA metric for a test suite can be prohibitively
expensive, as it involves a quadratic number of distance calculations. Hence,
we developed and released a performance-optimized, but functionally equivalent,
implementation of SA, reducing the evaluation time by up to 97\%. We also
propose refined variants of the SA omputation algorithm, aiming to further
increase the evaluation speed. We then performed an empirical study on MNIST,
focused on the out-of-distribution detection capabilities of SA, which allowed
us to reproduce parts of the results presented when SA was first released. The
experiments show that our refined variants are substantially faster than plain
SA, while producing comparable outcomes. Our experimental results exposed also
an overlooked issue of SA: it can be highly sensitive to the non-determinism
associated with the DNN training procedure.
