What is it about?
This paper proposes an automated UI glitch detection approach based on computer vision and bug analysis. Our empirical study on the root causes of game UI glitches facilitates a code-based data augmentation approach. Experimental results show that our GLIB is effective and shows great advantage in a real-world game testing scenario, way better than the existing rule-based approach.
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Why is it important?
As the first work on game UI glitch detection, we contribute to a systematical investigation of UI glitches in real-world mobile game apps, as well as a large-scale dataset of game app UIs with display issues for follow-up studies. We propose an efficient feature-based test oracle and a code-based data augmentation approach that can generate large quantities of representative UI glitchy images for training. Our proposed test oracle for automated UI glitch detection could facilitate further study on game UI testing.
Perspectives
This work uses a straightforward approach for solving a hard practical problem.
YUFEI LI
University of Texas at Dallas
Read the Original
This page is a summary of: GLIB: towards automated test oracle for graphically-rich applications, August 2021, ACM (Association for Computing Machinery),
DOI: 10.1145/3468264.3468586.
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