Unity Application Testing Automation with Appium and Image Recognition

Mozgovoy, Maxim; Pyshkin, Evgeny

doi:10.1007/978-3-319-71734-0_12

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…Xue et al [47] proposed a supervised deep learning approach to assist in performing record-and-replay GUI testing in a mobile or web application. Mozgovoy and Pyshkin [27] used template matching to recognize objects and GUI elements in a screenshot of a mobile game, which allow test assertions to be made against the visual content of the game. Ye et al [49] proposed a similar GUI widget detection approach for mobile games, in which they instrumented the source code of a mobile game to automatically extract samples of GUI widgets, and subsequently trained a supervised deep learning model for GUI widget detection.…”

Section: Visual Web and Gui Testingmentioning

confidence: 99%

Automatically Detecting Visual Bugs in HTML5 Games

Macklon¹,

Taesiri²,

Viggiato³

et al. 2022

Preprint

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The HTML5

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Section: Visual Web and Gui Testingmentioning

confidence: 99%

Automatically Detecting Visual Bugs in HTML5 Games

Macklon¹,

Taesiri²,

Viggiato³

et al. 2022

Preprint

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The HTML5

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“…In some limited settings, like for simple mobile games, it is possible to write conditions and rely on simple vision based methods [10,20] to identify some perceptual issues, like with positioning or with textures. To identify the more complex perceptual issues that many videos games with richer observations present, more sophisticated approaches are required.…”

Section: Bug Identificationmentioning

confidence: 99%

Learning to Identify Perceptual Bugs in 3D Video Games

Wilkins¹,

Stathis²

2022

Preprint

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Automated Bug Detection (ABD) in video games is composed of two distinct but complementary problems: automated game exploration and bug identification. Automated game exploration has received much recent attention, spurred on by developments in fields such as reinforcement learning. The complementary problem of identifying the bugs present in a player's experience has for the most part relied on the manual specification of rules. Although it is widely recognised that many bugs of interest cannot be identified with such methods, little progress has been made in this direction. In this work we show that it is possible to identify a range of perceptual bugs using learning-based methods by making use of only the rendered game screen as seen by the player. To support our work, we have developed World of Bugs (WOB), an open platform for testing ABD methods in 3D game environments.

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Youkai: A Cross-Platform Framework for Testing VR/AR Apps

Figueira¹,

Gil²

2022

Lecture Notes in Computer Science

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Unity Application Testing Automation with Appium and Image Recognition

Cited by 6 publications

References 2 publications

Automatically Detecting Visual Bugs in HTML5 Games

Automatically Detecting Visual Bugs in HTML5 Games

Learning to Identify Perceptual Bugs in 3D Video Games

Youkai: A Cross-Platform Framework for Testing VR/AR Apps

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