Deep neural networks (DNNs) have been deployed in a wide range of applications, including mission-critical systems. We introduce a DNN testing and debugging tool, called DeepConcolic, which is able to detect errors with sufficient rigour so as to be applicable to the testing of DNNs in safety related applications. DeepConcolic is the first tool that implements a concolic testing technique for DNNs, and the first testing tool that provides users with the functionality of investigating particular parts of a DNN. The tool has been made publicly available and a demo video can be found at \url{https://youtu.be/rliynbhoNLM}.
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 |