GLIB: Towards Automated Test Oracle for Graphically-Rich Applications
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Abstract (arXiv)
Graphically-rich applications such as games are ubiquitous with attractive visual effects of Graphical User Interface (GUI) that offers a bridge between software applications and end-users. However, various types of graphical glitches may arise from such GUI complexity and have become one of the main component of software compatibility issues. Our study on bug reports from game development teams in NetEase Inc. indicates that graphical glitches frequently occur during the GUI rendering and severely degrade the quality of graphically-rich applications such as video games. Existing automated testing techniques for such applications focus mainly on generating various GUI test sequences and check whether the test sequences can cause crashes. These techniques require constant human attention to captures non-crashing bugs such as bugs causing graphical glitches. In this paper, we present the first step in automating the test oracle for detecting non-crashing bugs in graphically-rich applications. Specifically, we propose textttGLIB based on a code-based data augmentation technique to detect game GUI glitches. We perform an evaluation of textttGLIB on 20 real-world game apps (with bug reports available) and the result shows that textttGLIB can achieve 100% precision and 99.5% recall in detecting non-crashing bugs such as game GUI glitches. Practical application of textttGLIB on another 14 real-world games (without bug reports) further demonstrates that textttGLIB can effectively uncover GUI glitches, with 48 of 53 bugs reported by textttGLIB having been confirmed and fixed so far.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 10 of its 46 references that are in the map. First posted on arXiv 19 Jun 2021.
Builds on
10 of 46 references.
References · 10
- 2016Densely Connected Convolutional Networkscited by 6,720
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- 2020Convolutional Networks with Dense Connectivitycited by 98
- 2018Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Appscited by 36
- 2020Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?cited by 29
- 2020Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learningcited by 25
- 2021Wireframe-Based UI Design Search Through Image Autoencodercited by 17
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Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 2; 2026: 3.
9 papers.
- 2023GlitchBench: Can large multimodal models detect video game glitches?cited by 23
- 2024PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videoscited by 22
- 2024Less Cybersickness, Please: Demystifying and Detecting Stereoscopic Visual Inconsistencies in Virtual Reality Appscited by 3
- 2026AI for Games in the Foundation Model Eracited by 3
- 2022The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Studycited by 1
- 2025Human-AI Collaborative Game Testing with Vision Language Modelscited by 1
- 2026Order from Chaos: Physical World Understanding from Glitchy Gameplay Videoscited by 1
- 2025Runtime Failure Hunting for Physics Engine Based Software Systems: How Far Can We Go?cited by 0
- 2026CA2: Code-Aware Agent for Automated Game Testingcited by 0
- 2026AI for Games in the Foundation Model Era15 Sep 2026
- 2026CA2: Code-Aware Agent for Automated Game Testing13 May 2026
- 2026Order from Chaos: Physical World Understanding from Glitchy Gameplay Videos23 Jan 2026
- 2025Runtime Failure Hunting for Physics Engine Based Software Systems: How Far Can We Go?29 Jul 2025
- 2025Human-AI Collaborative Game Testing with Vision Language Models20 Jan 2025
- 2024PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos2 Dec 2024
- 2024Less Cybersickness, Please: Demystifying and Detecting Stereoscopic Visual Inconsistencies in Virtual Reality Apps13 Jun 2024
- 2023GlitchBench: Can large multimodal models detect video game glitches?8 Dec 2023
- 2022The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study21 Jun 2022