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ISSTA 2020
Sat 18 - Wed 22 July 2020
Mon 20 Jul 2020 16:10 - 16:30 at Zoom - MACHINE LEARNING I Chair(s): Divya Gopinath

Mobile applications play an important role in our daily life, while it still remains a challenge to guarantee their correctness. Model-based and systematic approaches have been applied to Android GUI testing but they do not show great advantages over random approaches because of limitations such as imprecise models and poor scalability. In this paper, we propose Q-testing, a reinforcement learning based approach which benefits from both random and model-based approaches to automated testing of Android applications. Q-testing explores the Android apps with a curiosity-driven strategy that utilizes a memory set to record part of previously visited states and guides the testing towards unfamiliar functionalities. A state comparison module, which is a neural network trained by plenty of collected samples, is novelly employed to distinguish different states at the granularity of functional scenarios. It can determine the reinforcement learning reward in Q-testing and help the curiosity-driven strategy explore different functionalities efficiently. We conduct experiments on 50 open-source applications where Q-testing outperforms state-of-the-art and state-of-practice Android GUI testing tools in terms of code coverage and fault detection. So far, 22 of our reported faults have been confirmed, among which 7 have been fixed.

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