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.

Software testing is still a manual process in many industries, despite the recent improvements in automated testing techniques. As a result, test cases (which consist of one or more test steps that need to be executed manually by the tester) are often specified in natural language by different employees and many redundant test cases might exist in the test suite. This increases the (already high) cost of test execution. Manually identifying similar test cases is a time-consuming and error-prone task. Therefore, in this paper, we propose an unsupervised approach to identify similar test cases. Our approach uses a combination of text embedding, text similarity and clustering techniques to identify similar test cases. We evaluate five different text embedding techniques, two text similarity metrics, and two clustering techniques to cluster similar test steps and three techniques to identify similar test cases from the test step clusters. Through an evaluation in an industrial setting, we showed that our approach achieves a high performance to cluster test steps (an F-score of 87.39%) and identify similar test cases (an F-score of 86.13%). Furthermore, a validation with developers indicates several different practical usages of our approach (such as identifying redundant test cases), which help to reduce the testing manual effort and time.