  Deep Learning (DL) components are routinely integrated into software systems
that need to perform complex tasks such as image or natural language
processing. The adequacy of the test data used to test such systems can be
assessed by their ability to expose artificially injected faults (mutations)
that simulate real DL faults. In this paper, we describe an approach to
automatically generate new test inputs that can be used to augment the existing
test set so that its capability to detect DL mutations increases. Our tool
DeepMetis implements a search based input generation strategy. To account for
the non-determinism of the training and the mutation processes, our fitness
function involves multiple instances of the DL model under test. Experimental
results show that \tool is effective at augmenting the given test set,
increasing its capability to detect mutants by 63% on average. A leave-one-out
experiment shows that the augmented test set is capable of exposing unseen
mutants, which simulate the occurrence of yet undetected faults.
