The HTML5canvas> is widely used to display high quality graphics in web applications. However, the combination of web, GUI, and visual techniques that are required to buildcanvas> applications, together with the lack of testing and debugging tools, makes developing such applications very challenging. To help direct future research on testingcanvas> applications, in this paper we present a taxonomy of testablecanvas> issues. First, we extracted 2,403canvas>-related issue reports from 123 open source GitHub projects that use the HTML5canvas>. Second, we constructed our taxonomy by manually classifying a random sample of 332 issue reports. Our manual classification identified five broad categories of testablecanvas> issues, such as Visual and Performance issues. We found that Visual issues are the most frequent (35%), while Performance issues are relatively infrequent (5%). We also found that many testablecanvas> issues that present themselves visually on thecanvas> are actually caused by other components of the web application. Our taxonomy of testablecanvas> issues can be used to steer future research intocanvas> issues and 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 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 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 games is onerous, defeating the purpose of test automation. In this paper, we present a novel approach to automatically detect visual bugs in games. By leveraging an internal representation of objects on the , 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 game, and find that our approach achieves an accuracy of 100%, compared to an accuracy of 44.6% with traditional snapshot testing.