ASE 2019 (series) / Demonstrations /
Humanoid: A Deep Learning-based Approach to Automated Black-box Android App Testing
Tue 12 Nov 2019 12:10 - 12:20 at Hillcrest - Mobile 1 Chair(s): Marouane Kessentini
Thu 14 Nov 2019 10:00 - 10:03 at Kensington Ballroom - Poster Session: Tool Demonstrations 3
Thu 14 Nov 2019 10:00 - 10:03 at Kensington Ballroom - Poster Session: Tool Demonstrations 3
Automated input generators must constantly choose which UI element to interact with and how to interact with it, in order to achieve high coverage with a limited time budget. Currently, most black-box input generators adopt pseudo-random or brute-force searching strategies, which may take very long to find the correct combination of inputs that can drive the app into new and important states. We propose Humanoid, a deep learning-based approach to automated black-box Android app testing, which can explore the app more efficiently. The key technique behind Humanoid is a deep neural network model that can learn how human users choose actions based on an app’s GUI from human interaction traces . The learned model can be used to guide test input generation to achieve higher coverage. Experiments on both open-source apps and market apps demonstrate that Humanoid is able to reach higher coverage, and faster as well, than the state-of-the-art test input generators. Humanoid is open-sourced at https://github.com/yzygitzh/Humanoid and a demo video can be found at https://youtu.be/PDRxDrkyORs.
Tue 12 NovDisplayed time zone: Tijuana, Baja California change
Tue 12 Nov
Displayed time zone: Tijuana, Baja California change
10:40 - 12:20 | Mobile 1Demonstrations / Research Papers / Journal First Presentations at Hillcrest Chair(s): Marouane Kessentini University of Michigan | ||
10:40 20mTalk | Test Transfer Across Mobile Apps Through Semantic Mapping Research Papers Jun-Wei Lin University of California, Irvine, Reyhaneh Jabbarvand University of California, Irvine, Sam Malek University of California, Irvine | ||
11:00 20mTalk | Test Migration Between Mobile Apps with Similar Functionality Research Papers | ||
11:20 20mTalk | DaPanda: Detecting Aggressive Push Notification in Android Apps Research Papers Tianming Liu Beijing University of Posts and Telecommunications, China, Haoyu Wang Beijing University of Posts and Telecommunications, China, Li Li Monash University, Australia, Guangdong Bai Griffith University, Yao Guo Peking University, Guoai Xu Beijing University of Posts and Telecommunications | ||
11:40 20mTalk | Automatic, highly accurate app permission recommendation Journal First Presentations Zhongxin Liu Zhejiang University, Xin Xia Monash University, David Lo Singapore Management University, John Grundy Monash University Link to publication | ||
12:00 10mDemonstration | LIRAT: Layout and Image Recognition Driving Automated Mobile Testing of Cross-Platform Demonstrations Shengcheng Yu Nanjing University, China, Chunrong Fang Nanjing University, Yang Feng University of California, Irvine, Wenyuan Zhao Nanjing University, Zhenyu Chen Nanjing University File Attached | ||
12:10 10mDemonstration | Humanoid: A Deep Learning-based Approach to Automated Black-box Android App Testing Demonstrations Yuanchun Li Peking University, Ziyue Yang Peking University, Yao Guo Peking University, Xiangqun Chen Peking University | ||
Thu 14 NovDisplayed time zone: Tijuana, Baja California change
Thu 14 Nov
Displayed time zone: Tijuana, Baja California change
10:00 - 10:40 | |||
10:00 40mDemonstration | PraPR: Practical Program Repair via Bytecode Mutation Demonstrations | ||
10:00 40mDemonstration | Kotless: a Serverless Framework for Kotlin Demonstrations Vladislav Tankov JetBrains, ITMO University, Yaroslav Golubev JetBrains Research, Timofey Bryksin JetBrains Research, Saint-Petersburg State University | ||
10:00 40mDemonstration | PeASS: A Tool for Identifying Performance Changes at Code Level Demonstrations David Georg Reichelt Universität Leipzig, Stefan Kühne Universität Leipzig, Wilhelm Hasselbring Kiel University Pre-print Media Attached File Attached | ||
10:00 40mDemonstration | MutAPK: Source-Codeless Mutant Generation for Android Apps Demonstrations Camilo Escobar-Velásquez Universidad de los Andes, Michael Osorio-Riaño Universidad de los Andes, Mario Linares-Vásquez Systems and Computing Engineering Department , Universidad de los Andes , Bogotá, Colombia | ||
10:00 40mDemonstration | CocoQa: Question Answering for Coding Conventions over Knowledge Graphs Demonstrations Tianjiao Du Shanghai JiaoTong University, Junming Cao Shanghai JiaoTong University, Qinyue Wu Shanghai JiaoTong University, Wei Li Shanghai JiaoTong University, Beijun Shen School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Yuting Chen Shanghai Jiao Tong University | ||
10:00 3mDemonstration | Humanoid: A Deep Learning-based Approach to Automated Black-box Android App Testing Demonstrations Yuanchun Li Peking University, Ziyue Yang Peking University, Yao Guo Peking University, Xiangqun Chen Peking University | ||
10:00 40mDemonstration | Developer Reputation Estimator (DRE) Demonstrations Sadika Amreen University of Tennessee Knoxville, Andrey Karnauch University of Tennessee Knoxville, Audris Mockus University of Tennessee - Knoxville | ||
10:00 40mDemonstration | NeuralVis: Visualizing and Interpreting Deep Learning Models Demonstrations Xufan Zhang State Key Laboratory for Novel Software Technology Nanjing University, Nanjing, China, Ziyue Yin State Key Laboratory for Novel Software Technology Nanjing University, Nanjing, China, Yang Feng University of California, Irvine, Qingkai Shi Hong Kong University of Science and Technology, Jia Liu State Key Laboratory for Novel Software Technology Nanjing University, Nanjing, China, Zhenyu Chen Nanjing University | ||
10:00 40mDemonstration | Visual Analytics for Concurrent Java Executions Demonstrations Cyrille Artho KTH Royal Institute of Technology, Sweden, Monali Pande KTH Royal Institute of Technology, Qiyi Tang University of Oxford | ||
10:00 40mDemonstration | Sip4J: Statically Inferring Access Permission Contracts for Parallelising Sequential Java Programs Demonstrations Ayesha Sadiq Monash University, Li Li Monash University, Australia, Yuan-Fang Li Monash University, Ijaz Ahmed University of Lahore, Sea Ling Monash University | ||
10:00 40mDemonstration | SWAN_ASSIST: Semi-Automated Detection of Code-Specific, Security-Relevant Methods Demonstrations Goran Piskachev Fraunhofer IEM, Lisa Nguyen Quang Do Google, Oshando Johnson Fraunhofer IEM, Eric Bodden Heinz Nixdorf Institut, Paderborn University and Fraunhofer IEM Pre-print Media Attached File Attached | ||
10:00 40mDemonstration | VisFuzz: Understanding and Intervening Fuzzing with Interactive Visualization Demonstrations Chijin Zhou Tsinghua University, Mingzhe Wang Tsinghua University, Jie Liang Tsinghua University, Zhe Liu Nanjing University of Aeronautics and Astronautics, Chengnian Sun Waterloo University, Yu Jiang Tsinghua University | ||