Concolic Testing for Deep Neural Networks

Youcheng Sun, Min Wu, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska, Daniel Kroening

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Citations (Scopus)
128 Downloads (Pure)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering
PublisherAssociation for Computing Machinery (ACM)
Pages109-119
Number of pages11
ISBN (Print)978-1-4503-5937-5
DOIs
Publication statusPublished - 03 Sep 2018

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    Sun, Y., Wu, M., Ruan, W., Huang, X., Kwiatkowska, M., & Kroening, D. (2018). Concolic Testing for Deep Neural Networks. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (pp. 109-119). Association for Computing Machinery (ACM). https://doi.org/10.1145/3238147.3238172