  Deep neural network (DNN) models, including those used in safety-critical
domains, need to be thoroughly tested to ensure that they can reliably perform
well in different scenarios. In this article, we provide an overview of
structural coverage metrics for testing DNN models, including neuron coverage
(NC), k-multisection neuron coverage (kMNC), top-k neuron coverage (TKNC),
neuron boundary coverage (NBC), strong neuron activation coverage (SNAC) and
modified condition/decision coverage (MC/DC). We evaluate the metrics on
realistic DNN models used for perception tasks (including LeNet-1, LeNet-4,
LeNet-5, and ResNet20) as well as on networks used in autonomy (TaxiNet). We
also provide a tool, DNNCov, which can measure the testing coverage for all
these metrics. DNNCov outputs an informative coverage report to enable
researchers and practitioners to assess the adequacy of DNN testing, compare
different coverage measures, and to more conveniently inspect the model's
internals during testing.
