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ISSTA 2020
Sat 18 - Wed 22 July 2020
Mon 20 Jul 2020 16:30 - 16:50 at Zoom - MACHINE LEARNING I Chair(s): Divya Gopinath

We present Adapt, a new white-box testing technique for deep neural networks. As deep neural networks are increasingly used in safety-first applications, testing their behavior systematically has become a critical problem. Accordingly, various testing techniques for deep neural networks have been proposed in recent years. However, neural network testing is still at an early stage and existing techniques are not yet sufficiently effective. In this paper, we aim to advance this field, in particular white-box testing approaches for neural networks, by identifying and addressing a key limitation of existing state-of-the-arts. We observe that the so-called neuron-selection strategy is a critical component of white-box testing and propose a new technique that effectively employs the strategy by continuously adapting it to the ongoing testing process. Experiments with real-world network models and datasets show that Adapt is remarkably more effective than existing testing techniques in terms of coverage and adversarial inputs found.

Mon 20 Jul
Times are displayed in time zone: Tijuana, Baja California change

16:10 - 17:10: MACHINE LEARNING ITechnical Papers at Zoom
Chair(s): Divya GopinathNASA Ames (KBR Inc.)

Public Live Stream/Recording. Registered participants should join via the Zoom link distributed in Slack.

16:10 - 16:30
Reinforcement Learning Based Curiosity-Driven Testing of Android ApplicationsACM SIGSOFT Distinguished Paper Award
Technical Papers
Minxue PanNanjing University, An Huang, Guoxin Wang, Tian ZhangNanjing University, Xuandong LiNanjing University
DOI Media Attached
16:30 - 16:50
Effective White-Box Testing of Deep Neural Networks with Adaptive Neuron-Selection StrategyArtifacts Evaluated – ReusableArtifacts AvailableArtifacts Evaluated – FunctionalACM SIGSOFT Distinguished Paper Award
Technical Papers
Seokhyun LeeKorea University, South Korea, Sooyoung ChaKorea University, South Korea, Dain Lee, Hakjoo OhKorea University, South Korea
DOI Media Attached
16:50 - 17:10
DeepGini: Prioritizing Massive Tests to Enhance the Robustness of Deep Neural Networks
Technical Papers
Yang FengNanjing University, Qingkai ShiThe Hong Kong University of Science and Technology, Xinyu Gao, Muhammed Kerem Kahraman, Chunrong FangNanjing University, Zhenyu ChenNanjing University