Journal
2019 IEEE/ACM 41ST INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: NEW IDEAS AND EMERGING RESULTS (ICSE-NIER 2019)
Volume -, Issue -, Pages 85-88Publisher
IEEE COMPUTER SOC
DOI: 10.1109/ICSE-NIER.2019.00030
Keywords
deep neural networks; whitebox testing; cover-age criterion
Categories
Funding
- National Science Foundation [CNS: 1650512]
- Northrop Grumman Mission Systems University Research Program
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The growing use of deep neural networks in safety-critical applications makes it necessary to carry out adequate testing to detect and correct any incorrect behavior for corner case inputs before they can be actually used. Deep neural networks lack an explicit control-flow structure, making it impossible to apply to them traditional software testing criteria such as code coverage. In this paper, we examine existing testing methods for deep neural networks, the opportunities for improvement and the need for a fast, scalable, generalizable end-to-end testing method. We also propose a coverage criterion for deep neural networks that tries to capture all possible parts of the deep neural network's logic.
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