Abstract.
Concolic testing combines program execution and symbolic analysis
to explore the execution paths of a software program. This
paper presents the first concolic testing approach for Deep Neural
Networks (DNNs). More specifically, we formalise coverage criteria
for DNNs that have been studied in the literature, and then develop
a coherent method for performing concolic testing to increase test
coverage. Our experimental results show the effectiveness of the
concolic testing approach in both achieving high coverage and
finding adversarial examples.
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