Web applications have become the most popular type of software in the past decade, attracting the attention of both the academia and the industry. In parallel with their popularity, the complexity of aesthetics and functionality of web applications have also increased significantly, creating a big challenge for maintenance and cross-browser compliance testing. Since such testing and verification activities require visual analysis, web application testing has not been sufficiently automated. In this paper, we propose a novel pairwise image comparison approach suitable for web application testing where the location of layout faults needs to be detected efficiently while insignificant variations being neglected. This technique is developed based on the characteristics of fault patterns of browser layouts. An empirical study conducted with the industry partner shows our approach is more effective and efficient than existing methods in this area.
As part of a software testing process, output verification poses a challenge when the output is not numeric or textual, such as graphical. The industry practice of using human oracles (testers) to observe and verify the correctness of the actual results is both expensive and error-prone. In particular, this practice is usually unsustainable when developing web applications — the most popular software of our era. This is because web applications change frequently due to the fast-evolving requirements amid popular demand. To improve the cost effectiveness of browser output verification, in this study we design failure-based testing techniques and evaluate the effectiveness and efficiency thereof in the context of web testing. With a novel application of the concept of adaptive random sequence (ARS), our approach leverages peculiar characteristics of failure patterns found in browser layout rendering. An empirical study shows that the use of failure patterns and inclination to guide the testing flow leads to more cost-effective results than other classic methods. This study extends the application of ARSs from the input space of programs to their output space, and also shows that adaptive random testing (ART) can outperform random testing (RT) in both failure detection effectiveness (in terms of F-measure) and failure detection efficiency (in terms of execution time).
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