Robotic Visual GUI Testing for Truly Non-Intrusive Test Automation of Touch Screen Applications
Test automation intrusive to the devices under test is difficult to apply on closed or uncommon touch screen systems, e.g., a Switch game console or a digital instrument running a self-defined operating system. There is a lack of non-intrusive test automation techniques for situations where intrusive testing is impossible or not easy to apply. This paper presents RoScript, a novel robotic visual GUI testing system for truly non-intrusive test automation of touch screen applications. RoScript expresses GUI actions in visual test scripts and executes them via a physical robot. A key innovation of RoScript is a test engine armed with environment calibration techniques to achieve automated test execution without manually setting any environment parameter or adjusting the robot arms for a new subject under test. Additionally, two complementary computer vision-based methods are also introduced to record test scripts from videos of human actions on a touch screen. The RoScript test automation does not rely on the internal system of a device under test, making it truly non-intrusive and suitable for touch screen applications running on almost any platform. We evaluated RoScript on a diverse range of devices--including three Android/iOS phones, a Windows tablet, a Linux-based Raspberry Pi, a GoPro camera, and a Switch game console--across over 1100 GUI actions in 160 test scenarios. The results demonstrate RoScript’s high accuracy in test execution: 94% for executing test scripts and 97% for replicating GUI actions. Furthermore, RoScript accurately recorded about 85% of human touch screen actions into test code. These results highlight RoScript’s potential as a truly non-intrusive, cross-platform solution for GUI test automation.
Authors
- Shuoyan Yan
- Ju Qian (ORCID: https://orcid.org/0000-0001-8028-7213)
- Yan Wang (ORCID: https://orcid.org/0000-0002-4352-1450)
- Zhengyu Shang
- Yiming Jin
- Guizhou Lv
- Lin Chen (ORCID: https://orcid.org/0000-0003-2352-2226)
Institutions
- Nanjing University (CN)
- Nanjing University of Aeronautics and Astronautics (CN)
Publication Details
- Journal
- IEEE Transactions on Software Engineering
- Published
- 2025-02-21
- DOI
- https://doi.org/10.1109/tse.2025.3544441
- Citations
- 2
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48