The html5 is used to display high quality graphics in web applications such as web games (i.e., games). However, automatically testing games is not possible with existing web testing techniques and tools, and manua...
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ISBN:
(纸本)9781450394758
The html5 <canvas> is used to display high quality graphics in web applications such as web games (i.e., <canvas> games). However, automatically testing <canvas> games is not possible with existing web testing techniques and tools, and manual testing is laborious. Many widely used web testing tools rely on the Document Object Model (DOM) to drive web test automation, but the contents of the <canvas> are not represented in the DOM. The main alternative approach, snapshot testing, involves comparing oracle snapshot images with test-time snapshot images using an image similarity metric to catch visual bugs, i.e., bugs in the graphics of the web application. However, creating and maintaining oracle snapshot images for <canvas> games is onerous, defeating the purpose of test automation. In this paper, we present a novel approach to automatically detect visual bugs in <canvas> games. By leveraging an internal representation of objects on the <canvas>, we decompose snapshot images into a set of object images, each of which is compared with a respective oracle asset (e.g., a sprite) using four similarity metrics: percentage overlap, mean squared error, structural similarity, and embedding similarity. We evaluate our approach by injecting 24 visual bugs into a custom <canvas> game, and find that our approach achieves an accuracy of 100%, compared to an accuracy of 44.6% with traditional snapshot testing.
The html5 is used to display high quality graphics in web applications such as web games (i.e., games). However, automatically testing games is not possible with existing web testing techniques and tools, and manua...
详细信息
ISBN:
(纸本)9781450394758
The html5 <canvas> is used to display high quality graphics in web applications such as web games (i.e., <canvas> games). However, automatically testing <canvas> games is not possible with existing web testing techniques and tools, and manual testing is laborious. Many widely used web testing tools rely on the Document Object Model (DOM) to drive web test automation, but the contents of the <canvas> are not represented in the DOM. The main alternative approach, snapshot testing, involves comparing oracle snapshot images with test-time snapshot images using an image similarity metric to catch visual bugs, i.e., bugs in the graphics of the web application. However, creating and maintaining oracle snapshot images for <canvas> games is onerous, defeating the purpose of test automation. In this paper, we present a novel approach to automatically detect visual bugs in <canvas> games. By leveraging an internal representation of objects on the <canvas>, we decompose snapshot images into a set of object images, each of which is compared with a respective oracle asset (e.g., a sprite) using four similarity metrics: percentage overlap, mean squared error, structural similarity, and embedding similarity. We evaluate our approach by injecting 24 visual bugs into a custom <canvas> game, and find that our approach achieves an accuracy of 100%, compared to an accuracy of 44.6% with traditional snapshot testing.
Several web-native information visualization methods (SVG, html5's canvas, native html) are studied to contrast their performances at different data scales. Using Java implementations of parallel coordinates and s...
详细信息
ISBN:
(纸本)9781568814230
Several web-native information visualization methods (SVG, html5's canvas, native html) are studied to contrast their performances at different data scales. Using Java implementations of parallel coordinates and squarified treemaps for comparison, we explore the design space of these web-based technologies in order to determine what design trade-offs are required.
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