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Concolic testing for deep neural networks. / Sun, Youcheng; Wu, Min
; Ruan, Wenjie et al.
Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, ASE 2018, Montpellier, France, September 3-7, 2018. New York: Association for Computing Machinery (ACM), 2018. p. 109-119.
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
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, ASE 2018, Montpellier, France, September 3-7, 2018. Association for Computing Machinery (ACM), New York, pp. 109-119.
https://doi.org/10.1145/3238147.3238172APA
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, ASE 2018, Montpellier, France, September 3-7, 2018 (pp. 109-119). Association for Computing Machinery (ACM).
https://doi.org/10.1145/3238147.3238172Vancouver
Sun Y, Wu M
, Ruan W, Huang X, Kwiatkowska M, Kroening D.
Concolic testing for deep neural networks. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, ASE 2018, Montpellier, France, September 3-7, 2018. New York: Association for Computing Machinery (ACM). 2018. p. 109-119 doi: 10.1145/3238147.3238172
Author
Sun, Youcheng ; Wu, Min
; Ruan, Wenjie et al. /
Concolic testing for deep neural networks. Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, ASE 2018, Montpellier, France, September 3-7, 2018. New York : Association for Computing Machinery (ACM), 2018. pp. 109-119
Bibtex
@inproceedings{66b7d9cce00b4ca4a95ca073e6ef4971,
title = "Concolic testing for deep neural networks",
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.",
author = "Youcheng Sun and Min Wu and Wenjie Ruan and Xiaowei Huang and Marta Kwiatkowska and Daniel Kroening",
year = "2018",
month = sep,
day = "3",
doi = "10.1145/3238147.3238172",
language = "English",
isbn = "9781450359375",
pages = "109--119",
booktitle = "Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, ASE 2018, Montpellier, France, September 3-7, 2018",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",
}
RIS
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.
U2 - 10.1145/3238147.3238172
DO - 10.1145/3238147.3238172
M3 - Conference contribution/Paper
SN - 9781450359375
SP - 109
EP - 119
BT - Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, ASE 2018, Montpellier, France, September 3-7, 2018
PB - Association for Computing Machinery (ACM)
CY - New York
ER -
