  The growing use of deep neural networks in safety-critical applications makes
it necessary to carry out adequate testing to detect and correct any incorrect
behavior for corner case inputs before they can be actually used. Deep neural
networks lack an explicit control-flow structure, making it impossible to apply
to them traditional software testing criteria such as code coverage. In this
paper, we examine existing testing methods for deep neural networks, the
opportunities for improvement and the need for a fast, scalable, generalizable
end-to-end testing method. We also propose a coverage criterion for deep neural
networks that tries to capture all possible parts of the deep neural network's
logic.
