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[SWR+18] Y. Sun, M. Wu, W. Ruan, X. Huang, M. Kwiatkowska and D. Kroening. Concolic Testing for Deep Neural Networks. In Proc. 33rd IEEE/ACM International Conference on Automated Software Engineering, ACM. To appear. 2018. [pdf] [bib]
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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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