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Concolic Testing for Deep Neural Networks
April 30, 2018 · Declared Dead · 🏛 International Conference on Automated Software Engineering
"No code URL or promise found in abstract"
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Authors
Youcheng Sun, Min Wu, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska, Daniel Kroening
arXiv ID
1805.00089
Category
cs.LG: Machine Learning
Cross-listed
cs.SE,
stat.ML
Citations
344
Venue
International Conference on Automated Software Engineering
Last Checked
2 months ago
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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