A Taxonomy of Testable HTML5 Canvas Issues

January 18, 2022 ยท The Cartographer ยท ๐Ÿ› IEEE Transactions on Software Engineering

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

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"Title-pattern auto-detect: A Taxonomy of Testable HTML5 Canvas Issues"

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Authors Finlay Macklon, Markos Viggiato, Natalia Romanova, Chris Buzon, Dale Paas, Cor-Paul Bezemer arXiv ID 2201.07351 Category cs.SE: Software Engineering Citations 5 Venue IEEE Transactions on Software Engineering Last Checked 3 days ago
Abstract
The HTML5 <canvas> is widely used to display high quality graphics in web applications. However, the combination of web, GUI, and visual techniques that are required to build <canvas> applications, together with the lack of testing and debugging tools, makes developing such applications very challenging. To help direct future research on testing <canvas> applications, in this paper we present a taxonomy of testable <canvas> issues. First, we extracted 2,403 <canvas>-related issue reports from 123 open-source GitHub projects that use the HTML5 <canvas>. Second, we constructed our taxonomy by manually classifying a random sample of 332 issue reports. Our manual classification identified five broad categories of testable <canvas> 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 testable <canvas> issues that present themselves visually on the <canvas> are actually caused by other components of the web application. Our taxonomy of testable <canvas> issues can be used to steer future research into <canvas> issues and testing.
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