HashC: Making deep learning coverage testing finer and faster.

Though Deep Neural Networks (DNNs) have been widely deployed and achieved great success in many domains, they have severe safety and reliability concerns. To provide testing evidence for DNNs' reliable behaviors, various coverage testing techniques inspired by traditional software testing have been proposed. However, the coverage criteria in these techniques are either not fine enough to capture subtle behaviors of DNNs, or too time-consuming to be applied on large-scale DNNs. In this paper, we propose a coverage testing framework named HashC, which makes mainstream coverage criteria (e.g., NC and KMNC) much finer. Meanwhile, HashC reduces the time complexity of combinatorial coverage testing from polynomial time to linear time. We also develop the corresponding test sample selection method. Our experiments show that, (1) the existing mainstream coverage criteria are becoming finer after being equipped with HashC, (2) HashC greatly accelerates combinatorial coverage testing and can handle the testing of large-scale DNNs. [ABSTRACT FROM AUTHOR]