Guizhou Lv

dblp:403/6500 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0000-6428-7766ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Software testing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing
automated testing
0.912025
Robotic Visual GUI Testing for Truly Non-Intrusive Test Automation of Touch Screen Applications · IEEE Trans. Software Eng. 2025
Software testing
GUI testing
0.912025
Robotic Visual GUI Testing for Truly Non-Intrusive Test Automation of Touch Screen Applications · IEEE Trans. Software Eng. 2025

Methods — techniques the papers use, named apart from their topics

robotic test execution · 0.9computer vision · 0.9
YearPublicationVenuePosition
2025 Robotic Visual GUI Testing for Truly Non-Intrusive Test Automation of Touch Screen Applications
abstract
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.
Ju Qian, Guizhou Lv, Yiming Jin 0001, Zhengyu Shang, Shuoyan Yan, Yan Wang 0125, Lin Chen 0015
IEEE Trans. Software Eng.2