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ICSE 2020
Wed 24 June - Thu 16 July 2020
Tue 7 Jul 2020 08:35 - 08:47 at Goguryeo - I5-Deep Learning Testing and Debugging Chair(s): Pooyan Jamshidi

The growing application of deep neural networks in safety-critical domains makes the analysis of faults that occur in such systems of enormous importance. In this paper we introduce a large taxonomy of faults in deep learning (DL) systems. We have manually analysed 1059 artefacts gathered from GitHub commits and issues of projects that use the most popular DL frameworks (TensorFlow, Keras and PyTorch) and from related Stack Overflow posts. Structured interviews with 20 researchers and practitioners describing the problems they have encountered in their experience have enriched our taxonomy with a variety of additional faults that did not emerge from the other two sources. Our final taxonomy was validated with a survey involving an additional set of 21 developers, confirming that almost all fault categories (13/15) were experienced by at least 50% of the survey participants.

Tue 7 Jul

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08:05 - 09:05
I5-Deep Learning Testing and DebuggingTechnical Papers / Demonstrations at Goguryeo
Chair(s): Pooyan Jamshidi University of South Carolina
08:05
12m
Talk
DISSECTOR: Input Validation for Deep Learning Applications by Crossing-layer DissectionTechnical
Technical Papers
Huiyan Wang State Key Lab. for Novel Software Tech. and Dept. of Comp. Sci. and Tech., Nanjing University, Nanjing, China, Jingwei Xu Nanjing University, Chang Xu Nanjing University, Xiaoxing Ma Nanjing University, Jian Lu Nanjing University
08:17
12m
Talk
White-box Fairness Testing through Adversarial SamplingACM SIGSOFT Distinguished Paper AwardsTechnical
Technical Papers
Peixin Zhang Zhejiang University, Jingyi Wang National University of Singapore, Singapore, Jun Sun Singapore Management University, Guoliang Dong Computer College of Zhejiang University, Xinyu Wang Zhejiang University, Xingen Wang Zhejiang University, Jin Song Dong National University of Singapore, Dai Ting Huawei Corporation
08:29
3m
Talk
FeatureNET: Diversity-driven Generation of Deep Learning ModelsDemo
Demonstrations
Salah Ghamizi SntT - University of Luxembourg, Maxime Cordy SnT, University of Luxembourg, Mike Papadakis University of Luxembourg, Yves Le Traon University of Luxembourg
08:32
3m
Talk
EvalDNN: A Toolbox for Evaluating Deep Neural Network ModelsDemo
Demonstrations
Yongqiang TIAN The Hong Kong University of Science and Technology, Zhihua Zeng Zhejiang University, Ming Wen Huazhong University of Science and Technology, China, Yepang Liu Southern University of Science and Technology, Tzu-yang Kuo The Hong Kong University of Science and Technology, Shing-Chi Cheung Department of Computer Science and Engineering, The Hong Kong University of Science and Technology
08:35
12m
Talk
Taxonomy of Real Faults in Deep Learning SystemsACM SIGSOFT Distinguished Artifact AwardsArtifact ReusableTechnicalArtifact Available
Technical Papers
Nargiz Humbatova Università della Svizzera italiana, Gunel Jahangirova Università della Svizzera italiana, Gabriele Bavota Università della Svizzera italiana, Vincenzo Riccio Università della Svizzera italiana, Andrea Stocco Università della Svizzera italiana, Paolo Tonella Università della Svizzera italiana
08:47
12m
Talk
An Empirical Study on Program Failures of Deep Learning JobsACM SIGSOFT Distinguished Paper AwardsTechnical
Technical Papers
Ru Zhang Microsoft Research, Wencong Xiao Alibaba, Hongyu Zhang University of Newcastle, Australia, Yu Liu Microsoft Research, Haoxiang Lin Microsoft Research, Mao Yang Microsoft Research