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Tue 10 Nov 2020 01:30 - 01:32 at Virtual room 1 - ML Testing 1

Inspired by the great success of using code coverage as guidance in software testing, a lot of neural network coverage criteria have been proposed to guide testing of neural network models (e.g., model accuracy under adversarial attacks). However, while the monotonic relation between code coverage and software quality has been supported by many seminal studies in software engineering, it remains largely unclear whether similar monotonicity exists between neural network model coverage and model quality. This paper sets out to answer this question. Specifically, this paper studies the correlation between DNN model quality and coverage criteria, effects of coverage guided adversarial example generation compared with gradient decent based methods, effectiveness of coverage based retraining compared with existing adversarial training, and the internal relationships among coverage criteria.

Tue 10 Nov
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01:30 - 01:32
Talk
Correlations between Deep Neural Network Model Coverage Criteria and Model Quality
Research Papers
Shenao YanRutgers University, USA, Guanhong TaoPurdue University, USA, Xuwei LiuPurdue University, USA, Juan ZhaiRutgers University, USA, Shiqing MaRutgers University, USA, Lei XuNanjing University, China, Xiangyu ZhangPurdue University
DOI
01:33 - 01:34
Talk
Deep Learning Library Testing via Effective Model GenerationACM SIGSOFT Distinguished Paper Award
Research Papers
Zan WangTianjin University, China, Ming YanTianjin University, China, Junjie ChenTianjin University, China, Shuang LiuTianjin University, China, Dongdi ZhangTianjin University, China
DOI
01:35 - 01:36
Talk
Detecting Numerical Bugs in Neural Network ArchitecturesACM SIGSOFT Distinguished Paper Award
Research Papers
Yuhao ZhangPeking University, Luyao RenPeking University, China, Liqian ChenNational University of Defense Technology, China, Yingfei XiongPeking University, Shing-Chi CheungHong Kong University of Science and Technology, China, Tao XiePeking University
DOI
01:37 - 01:38
Talk
Dynamic Slicing for Deep Neural Networks
Research Papers
Ziqi ZhangPeking University, China, Yuanchun LiMicrosoft Research, China, Yao GuoPeking University, Xiangqun ChenPeking University, Yunxin LiuMicrosoft Research, China
DOI
01:39 - 01:40
Talk
Grammar Based Directed Testing of Machine Learning Systems
Journal First
Sakshi UdeshiSingapore University of Technology and Design, Sudipta ChattopadhyaySingapore University of Technology and Design
01:41 - 01:42
Talk
Is Neuron Coverage a Meaningful Measure for Testing Deep Neural Networks?
Research Papers
Fabrice Harel-CanadaUniversity of California at Los Angeles, USA, Lingxiao WangUniversity of California at Los Angeles, USA, Muhammad Ali GulzarUniversity of California at Los Angeles, USA, Quanquan GuUniversity of California at Los Angeles, USA, Miryung KimUniversity of California at Los Angeles, USA
DOI
01:43 - 01:44
Talk
Operational Calibration: Debugging Confidence Errors for DNNs in the Field
Research Papers
Zenan LiNanjing University, China, Xiaoxing MaNanjing University, China, Chang XuNanjing University, China, Jingwei XuNanjing University, China, Chun CaoNanjing University, China, Jian LuNanjing University, China
DOI
01:45 - 02:00
Talk
Conversations on ML Testing 1
Research Papers
Fabrice Harel-CanadaUniversity of California at Los Angeles, USA, Ming YanTianjin University, China, Sakshi UdeshiSingapore University of Technology and Design, Shenao YanRutgers University, USA, Yuhao ZhangPeking University, Zenan LiNanjing University, China, Ziqi ZhangPeking University, China, M: Hamid BagheriUniversity of Nebraska-Lincoln, USA