Concolic Testing for Deep Neural Networks

Sun, Youcheng, Wu, Min, Ruan, Wenjie, Huang, Xiaowei ORCID: 0000-0001-6267-0366, Kwiatkowska, Marta and Kroening, Daniel (2018) Concolic Testing for Deep Neural Networks. In: 33rd IEEE/ACM International Conference on Automated Software Engineering, 2018-9-3 - 2018-9-7, Montpellier, France.

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Abstract

Concolic testing alternates between CONCrete program execution and symbOLIC analysis to explore the execution paths of a software program and to increase code coverage. 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.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	cs.LG, cs.LG, cs.SE, stat.ML
Depositing User:	Symplectic Admin
Date Deposited:	31 Jul 2018 09:04
Last Modified:	19 Jan 2023 01:30
DOI:	10.1145/3238147.3238172
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3024224