  Deep Learning (DL) has been successfully applied to a wide range of
application domains, including safety-critical ones. Several DL testing
approaches have been recently proposed in the literature but none of them aims
to assess how different interpretable features of the generated inputs affect
the system's behaviour. In this paper, we resort to Illumination Search to find
the highest-performing test cases (i.e., misbehaving and closest to
misbehaving), spread across the cells of a map representing the feature space
of the system. We introduce a methodology that guides the users of our approach
in the tasks of identifying and quantifying the dimensions of the feature space
for a given domain. We developed DeepHyperion, a search-based tool for DL
systems that illuminates, i.e., explores at large, the feature space, by
providing developers with an interpretable feature map where automatically
generated inputs are placed along with information about the exposed
behaviours.
