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Automated test input generation for Android: are we really there yet in an industrial case?

Published: 01 November 2016 Publication History

Abstract

Given the ever increasing number of research tools to automatically generate inputs to test Android applications (or simply apps), researchers recently asked the question "Are we there yet?" (in terms of the practicality of the tools). By conducting an empirical study of the various tools, the researchers found that Monkey (the most widely used tool of this category in industrial practices) outperformed all of the research tools that they studied. In this paper, we present two significant extensions of that study. First, we conduct the first industrial case study of applying Monkey against WeChat, a popular messenger app with over 762 million monthly active users, and report the empirical findings on Monkey's limitations in an industrial setting. Second, we develop a new approach to address major limitations of Monkey and accomplish substantial code-coverage improvements over Monkey, along with empirical insights for future enhancements to both Monkey and our approach.

References

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Android 64k method limit. https: //developer.android.com/studio/build/multidex.html.
[2]
Emma: a free Java code-coverage tool. http://emma.sourceforge.net/.
[3]
S. Anand, M. Naik, M. J. Harrold, and H. Yang. Automated concolic testing of smartphone apps. In FSE’12, pages 599–609.
[4]
T. Azim and I. Neamtiu. Targeted and depth-first exploration for systematic testing of Android apps. In OOPSLA’13, pages 641–660.
[5]
W. Choi, G. Necula, and K. Sen. Guided GUI testing of Android apps with minimal restart and approximate learning. In OOPSLA’13, pages 623–640.
[6]
S. R. Choudhary, A. Gorla, and A. Orso. Automated test input generation for Android: Are we there yet? In ASE’15, pages 429–440.
[7]
A. Machiry, R. Tahiliani, and M. Naik. Dynodroid: An input generation system for Android apps. In FSE’13, pages 224–234.
[8]
R. Mahmood, N. Mirzaei, and S. Malek. Evodroid: Segmented evolutionary testing of Android apps. In FSE’14, pages 599–609.
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S. Yang, H. Zhang, H. Wu, Y. Wang, D. Yan, and A. Rountev. Static window transition graphs for Android. In FSE’12, pages 658–668.
[10]
W. Yang, M. R. Prasad, and T. Xie. A grey-box approach for automated GUI-model generation of mobile applications. In FASE’13, pages 250–265.

Cited By

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  • (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)Navigating Mobile Testing Evaluation: A Comprehensive Statistical Analysis of Android GUI Testing MetricsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695476(944-956)Online publication date: 27-Oct-2024
  • (2023)Automata-Based Trace Analysis for Aiding Diagnosing GUI Testing Tools for AndroidProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616361(592-604)Online publication date: 30-Nov-2023
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    cover image ACM Conferences
    FSE 2016: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering
    November 2016
    1156 pages
    ISBN:9781450342186
    DOI:10.1145/2950290
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 01 November 2016

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    Author Tags

    1. Android
    2. Code coverage
    3. GUI testing
    4. Test case generating

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    Overall Acceptance Rate 17 of 128 submissions, 13%

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    Cited By

    View all
    • (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)Navigating Mobile Testing Evaluation: A Comprehensive Statistical Analysis of Android GUI Testing MetricsProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695476(944-956)Online publication date: 27-Oct-2024
    • (2023)Automata-Based Trace Analysis for Aiding Diagnosing GUI Testing Tools for AndroidProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616361(592-604)Online publication date: 30-Nov-2023
    • (2023)Appaction: Automatic GUI Interaction for Mobile Apps via Holistic Widget PerceptionProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3613885(1786-1797)Online publication date: 30-Nov-2023
    • (2023)A Unified Framework for Mini-game Testing: Experience on WeChatProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3613868(1623-1634)Online publication date: 30-Nov-2023
    • (2023)CydiOS: A Model-Based Testing Framework for iOS AppsProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598033(1-13)Online publication date: 12-Jul-2023
    • (2023)Test Report Generation for Android App Testing Via Heterogeneous Data AnalysisIEEE Transactions on Software Engineering10.1109/TSE.2023.323724749:5(3032-3051)Online publication date: 1-May-2023
    • (2023)Automatically Distilling Storyboard With Rich Features for Android AppsIEEE Transactions on Software Engineering10.1109/TSE.2022.315954849:2(667-683)Online publication date: 1-Feb-2023
    • (2023)LLM for Test Script Generation and Migration: Challenges, Capabilities, and Opportunities2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS)10.1109/QRS60937.2023.00029(206-217)Online publication date: 22-Oct-2023
    • (2023)Ex pede Herculem: Augmenting Activity Transition Graph for Apps via Graph Convolution Network2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)10.1109/ICSE48619.2023.00168(1983-1995)Online publication date: May-2023
    • Show More Cited By

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