TY - GEN
T1 - Concolic Testing for Deep Neural Networks
AU - Sun, Youcheng
AU - Wu, Min
AU - Ruan, Wenjie
AU - Huang, Xiaowei
AU - Kwiatkowska, Marta
AU - Kroening, Daniel
PY - 2018/9/3
Y1 - 2018/9/3
N2 - Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. In this paper, we develop the first concolic testing approach for Deep Neural Networks (DNNs). More specifically, we utilise quantified linear arithmetic over rationals to express test requirements that have been studied in the literature, and then develop a coherent method to perform concolic testing with the aim of better coverage. Our experimental results show the effectiveness of the concolic testing approach in both achieving high coverage and finding adversarial examples.
AB - Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. In this paper, we develop the first concolic testing approach for Deep Neural Networks (DNNs). More specifically, we utilise quantified linear arithmetic over rationals to express test requirements that have been studied in the literature, and then develop a coherent method to perform concolic testing with the aim of better coverage. Our experimental results show the effectiveness of the concolic testing approach in both achieving high coverage and finding adversarial examples.
UR - https://pure.qub.ac.uk/en/publications/concolic-testing-for-deep-neural-networks(d28e4073-8af2-4591-9ed1-0d4a5c476786).html
U2 - 10.1145/3238147.3238172
DO - 10.1145/3238147.3238172
M3 - Conference contribution
SN - 978-1-4503-5937-5
SP - 109
EP - 119
BT - Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering
ER -