skip to main content
10.1145/3624032.3624040acmotherconferencesArticle/Chapter ViewAbstractPublication PagessastConference Proceedingsconference-collections
research-article

Automating Android Rotation Vector Testing in Google's Compatibility Test Suite Using a Robotic Arm

Published:17 October 2023Publication History

ABSTRACT

Software testing is one of the essential phases of software development. In this context, test automation has gained significant traction for meeting immediate requirements while upholding result quality. While both automated and manual methods are employed for software testing, manual approaches are susceptible to inaccuracies and human errors despite the benefits of automation. This research is focused on Google’s Android OS, pivotal for a variety of global mobile devices, which must adhere to specific quality requirements. Our study focuses on the Compatibility Test Suite tool, specifically the Rotation Vector Computer Vision Crosscheck (RV) Test case designed for evaluating smartphone sensor capabilities. We introduce a three-axis robotic arm designed to automate the RV test execution, thereby minimizing operational failures and expediting Android smartphones’ quality testing processes. We compared RV test execution in a real-world company using our automated solution against human testers. The proposed robotic arm demonstrated a 75% accuracy rate, surpassing the 37% accuracy achieved by human testers. This significant disparity underscores the potential of our automation approach to mitigate manual errors while ensuring robust and effective testing processes.

Skip Supplemental Material Section

Supplemental Material

References

  1. Daniel Asfaw. 2015. Benefits of automated testing over manual testing. International Journal of Innovative Research in Information Security 2, 1 (2015), 5–13.Google ScholarGoogle Scholar
  2. Debdeep Banerjee and Kevin Yu. 2018. Robotic Arm-Based Face Recognition Software Test Automation. IEEE Access 6 (2018), 37858–37868. https://doi.org/10.1109/ACCESS.2018.2854754Google ScholarGoogle ScholarCross RefCross Ref
  3. Debdeep Banerjee, Kevin Yu, and Garima Aggarwal. 2018. Image Rectification Software Test Automation Using a Robotic ARM. IEEE Access 6 (2018), 34075–34085. https://doi.org/10.1109/ACCESS.2018.2846761Google ScholarGoogle ScholarCross RefCross Ref
  4. Stefan Berner, Roland Weber, and Rudolf K Keller. 2005. Observations and lessons learned from automated testing. In Proceedings of the 27th international conference on Software engineering. 571–579.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. SM Bindu Bhargavi and V Suma. 2022. A Survey of the Software Test Methods and Identification of Critical Success Factors for Automation. SN Computer Science 3, 6 (2022), 449.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Android Developers. 2023. Compatibility Test Suite. url = https://source.android.com/compatibility/cts. "[Accessed: April/2023]".Google ScholarGoogle Scholar
  7. Android Developers. 2023. Compatibility Test Suite Verifier. url = https://source.android.com/docs/compatibility/cts/verifier. "[Accessed: April/2023]".Google ScholarGoogle Scholar
  8. Android Developers. 2023. Rotation Computer Vision Crosscheck Vector. url = https://source.android.com/compatibility/cts/rotation-vector. "[Accessed: April/2023]".Google ScholarGoogle Scholar
  9. Demian Frister, Andreas Oberweis, and Aleksandar Goranov. 2020. Automated Testing of Mobile Applications Using a Robotic Arm. In 2020 International Conference on Computational Science and Computational Intelligence (CSCI). 1729–1735. https://doi.org/10.1109/CSCI51800.2020.00321Google ScholarGoogle ScholarCross RefCross Ref
  10. Heidilyn Veloso Gamido and Marlon Viray Gamido. 2019. Comparative review of the features of automated software testing tools. International Journal of Electrical and Computer Engineering 9, 5 (2019), 4473.Google ScholarGoogle Scholar
  11. Klaus Haller. 2013. Mobile testing. ACM SIGSOFT Software Engineering Notes 38, 6 (2013), 1–8.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Mona Erfani Joorabchi, Ali Mesbah, and Philippe Kruchten. 2013. Real challenges in mobile app development. In 2013 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. IEEE, 15–24.Google ScholarGoogle ScholarCross RefCross Ref
  13. Wazir Zada Khan, Yang Xiang, Mohammed Y Aalsalem, and Quratulain Arshad. 2012. Mobile phone sensing systems: A survey. IEEE Communications Surveys & Tutorials 15, 1 (2012), 402–427.Google ScholarGoogle ScholarCross RefCross Ref
  14. Divya Kumar and Krishn Kumar Mishra. 2016. The impacts of test automation on software’s cost, quality and time to market. Procedia Computer Science 79 (2016), 8–15.Google ScholarGoogle ScholarCross RefCross Ref
  15. Pedro Ivo Pereira Lancellotta, Heryck Michael dos Santos Barbosa, João Gabriel Castro Santos, Klirssia Matos Isaac Sahdo, and Janislley Oliveira De Sousa. 2022. An Industry Case Study: Methodology Application to the Reviewing Process on Android Releases Homologation. In Anais Estendidos do XIII Congresso Brasileiro de Software: Teoria e Prática. SBC, 13–16.Google ScholarGoogle Scholar
  16. Mario Linares-Vásquez, Carlos Bernal-Cárdenas, Kevin Moran, and Denys Poshyvanyk. 2017. How do developers test android applications?. In 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 613–622.Google ScholarGoogle ScholarCross RefCross Ref
  17. Mika V Mäntylä, Bram Adams, Foutse Khomh, Emelie Engström, and Kai Petersen. 2015. On rapid releases and software testing: a case study and a semi-systematic literature review. Empirical Software Engineering 20 (2015), 1384–1425.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Ke Mao, Mark Harman, and Yue Jia. 2017. Robotic Testing of Mobile Apps for Truly Black-Box Automation. IEEE Software 34, 2 (2017), 11–16. https://doi.org/10.1109/MS.2017.49Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Leckraj Nagowah and Gayeree Sowamber. 2012. A novel approach of automation testing on mobile devices. In 2012 international conference on computer & information science (ICCIS), Vol. 2. IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  20. Dudekula Mohammad Rafi, Katam Reddy Kiran Moses, Kai Petersen, and Mika V. Mäntylä. 2012. Benefits and limitations of automated software testing: Systematic literature review and practitioner survey. (2012), 36–42. https://doi.org/10.1109/IWAST.2012.6228988Google ScholarGoogle ScholarCross RefCross Ref
  21. RM Sharma. 2014. Quantitative analysis of automation and manual testing. International journal of engineering and innovative technology 4, 1 (2014).Google ScholarGoogle Scholar
  22. Ian Sommerville. 2016. Software Engineering (10 ed.). Addison-Wesley, Harlow, England.Google ScholarGoogle Scholar
  23. Yuqing Wang, Mika V Mäntylä, Zihao Liu, and Jouni Markkula. 2022. Test automation maturity improves product quality—Quantitative study of open source projects using continuous integration. Journal of Systems and Software 188 (2022), 111259.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Automating Android Rotation Vector Testing in Google's Compatibility Test Suite Using a Robotic Arm

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        SAST '23: Proceedings of the 8th Brazilian Symposium on Systematic and Automated Software Testing
        September 2023
        133 pages
        ISBN:9798400716294
        DOI:10.1145/3624032

        Copyright © 2023 ACM

        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 the author(s) 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].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 17 October 2023

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        Overall Acceptance Rate45of92submissions,49%
      • Article Metrics

        • Downloads (Last 12 months)16
        • Downloads (Last 6 weeks)1

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format