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Fastbot: A Multi-Agent Model-Based Test Generation System Beijing Bytedance Network Technology Co., Ltd.

Published: 07 October 2020 Publication History

Abstract

Model-based test (MBT) generation techniques for automated GUI testing are of great value for app testing. Existing GUI model-based testing tools may fall into cyclic operations and run out of resources, when applied to apps with industrial complexity and scalability. In this work, we present a multi-agent GUI MBT system named Fastbot. Fastbot performs model construction on the server end. It applies multi-agent collaboration mechanism to speed up the model construction procedure. The proposed approach was applied on more than 20 applications from Bytedance with more than 1500 million monthly active users. Higher code coverage in less testing time is achieved with comparison of three other automated testing tools including Droidbot, Humanoid and Android Monkey.

References

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Ke Mao, Mark Harman, and Yue Jia. 2016. Sapienz: multi-objective automated testing for Android applications. In Proceedings of the 25th International Symposium on Software Testing and Analysis (ISSTA 2016). Association for Computing Machinery, New York, NY, USA, 94--105.
[2]
Aravind Machiry, Rohan Tahiliani, and Mayur Naik. 2013. Dynodroid: an input generation system for Android apps. In Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2013). Association for Computing Machinery, New York, NY, USA, 224--234.
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K. Jamrozik and A. Zeller, "DroidMate: A Robust and Extensible Test Generator for Android," 2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft), Austin, TX, 2016, pp. 293--294.
[4]
Yuanchun Li, Ziyue Yang, Yao Guo and Xiangqun Chen, "DroidBot: a lightweight UI-Guided test input generator for android," 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C), Buenos Aires, 2017, pp. 23--26.
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Li, Yuanchun, et al. "A Deep Learning based Approach to Automated Android App Testing." arXiv preprint arXiv:1901.02633 (2019).
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Carpentier, Alexandra & Lazaric, Alessandro & Ghavamzadeh, Mohammad & Munos, Remi & Auer, Peter. (2011). Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits. 6925. 189--203. 10.1007/978-3-642-24412-4_17.
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cover image ACM Conferences
AST '20: Proceedings of the IEEE/ACM 1st International Conference on Automation of Software Test
October 2020
122 pages
ISBN:9781450379571
DOI:10.1145/3387903
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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Published: 07 October 2020

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Author Tags

  1. Model-based GUI testing
  2. automatic testing
  3. dynamic DAG exploration
  4. multi-agent collaboration
  5. traversal algorithm

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  • (2024)Beyond App Markets: Demystifying Underground Mobile App Distribution Via TelegramProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/37004328:3(1-25)Online publication date: 10-Dec-2024
  • (2024)Giving without Notifying: Assessing Compliance of Data Transmission in Android AppsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695528(1595-1606)Online publication date: 27-Oct-2024
  • (2024)Can Cooperative Multi-Agent Reinforcement Learning Boost Automatic Web Testing? An Exploratory StudyProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3694983(14-26)Online publication date: 27-Oct-2024
  • (2024)LIReDroid: LLM-Enhanced Test Case Generation for Static Sensitive Behavior ReplicationProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3671404(81-84)Online publication date: 24-Jul-2024
  • (2024)Enhancing GUI Exploration Coverage of Android Apps with Deep Link-Integrated MonkeyACM Transactions on Software Engineering and Methodology10.1145/366481033:6(1-31)Online publication date: 27-Jun-2024
  • (2024)Neat: Mobile App Layout Similarity Comparison Based on Graph Convolutional NetworksCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663832(104-114)Online publication date: 10-Jul-2024
  • (2024)Synthesis-Based Enhancement for GUI Test Case MigrationProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680327(869-881)Online publication date: 11-Sep-2024
  • (2024)Make LLM a Testing Expert: Bringing Human-like Interaction to Mobile GUI Testing via Functionality-aware DecisionsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639180(1-13)Online publication date: 20-May-2024
  • (2024)Testing the Limits: Unusual Text Inputs Generation for Mobile App Crash Detection with Large Language ModelProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639118(1-12)Online publication date: 20-May-2024
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