ICSE 2019 (series) / New Ideas and Emerging Results / Towards Improved Testing For Deep Learning
Towards Improved Testing For Deep LearningNIER
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 traditional software testing criteria such as code coverage to them. 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 for deep neural networks. We also propose a coverage criterion for deep neural networks that tries to capture all possible parts of the deep neural network’s logic.
Fri 31 MayDisplayed time zone: Eastern Time (US & Canada) change
Fri 31 May
Displayed time zone: Eastern Time (US & Canada) change
14:00 - 15:30 | Testing of AI SystemsNew Ideas and Emerging Results / Demonstrations / Technical Track at Place du Canada Chair(s): Marija Mikic Google | ||
14:00 20mTalk | CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning LibrariesTechnical Track Technical Track Hung Viet Pham University of Waterloo, Thibaud Lutellier , Weizhen Qi University of Science and Technology of China, Lin Tan Purdue University Pre-print | ||
14:20 20mTalk | Guiding Deep Learning System Testing using Surprise AdequacyTechnical Track Technical Track Jinhan Kim KAIST, Robert Feldt Chalmers University of Technology, Shin Yoo Korea Advanced Institute of Science and Technology Authorizer link Pre-print | ||
14:40 20mTalk | DeepConcolic: Testing and Debugging Deep Neural NetworksDemos Demonstrations Youcheng Sun University of Oxford, Xiaowei Huang University of Liverpool, Daniel Kroening University of Oxford, James Sharp Defence Science and Technology Laboratory (Dstl), Matthew Hill Defence Science and Technology Laboratory (Dstl), Rob Ashmore Defence Science and Technology Laboratory (Dstl) | ||
15:00 10mTalk | Towards Improved Testing For Deep LearningNIER New Ideas and Emerging Results Pre-print | ||
15:10 10mTalk | Structural Coverage Criteria for Neural Networks Could Be MisleadingNIER New Ideas and Emerging Results Zenan Li Nanjing University, Xiaoxing Ma Nanjing University, Chang Xu Nanjing University, Chun Cao Nanjing University Pre-print | ||
15:20 10mTalk | Robustness of Neural Networks: A Probabilistic and Practical PerspectiveNIER New Ideas and Emerging Results |