Automated visual classification of DOM-based presentation failure reports for responsive web pages

Since it is common for the users of a web page to access it through a wide variety of devices—including desktops, laptops, tablets and phones—web developers rely on responsive web design (RWD) principles and frameworks to create sites that are useful on all devices. A correctly implemented responsive web page adjusts its layout according to the viewport width of the device in use, thereby ensuring that its design suitably features the content. Since the use of complex RWD frameworks often leads to web pages with hard‐to‐detect responsive layout failures (RLFs), developers employ testing tools that generate reports of potential RLFs. Since testing tools for responsive web pages, like ReDeCheck, analyse a web page representation called the Document Object Model (DOM), they may inadvertently flag concerns that are not human visible, thereby requiring developers to manually confirm and classify each potential RLF as a true positive (TP), false positive (FP), or non‐observable issue (NOI)—a process that is time consuming and error prone. The conference version of this paper presented Viser, a tool that automatically classified three types of RLFs reported by ReDeCheck. Since Viser was not designed to automatically confirm and classify two types of RLFs that ReDeCheck's DOM‐based analysis could surface, this paper introduces Verve, a tool that automatically classifies all RLF types reported by ReDeCheck. Along with manipulating the opacity of HTML elements in a web page, as does Viser, the Verve tool also uses histogram‐based image comparison to classify RLFs in web pages. Incorporating both the 25 web pages used in prior experiments and 20 new pages not previously considered, this paper's empirical study reveals that Verve's classification of all five types of RLFs frequently agrees with classifications produced manually by humans. The experiments also reveal that Verve took on average about 4 s to classify any of the RLFs among the 469 reported by ReDeCheck. Since this paper demonstrates that classifying an RLF as a TP, FP, or NOI with Verve, a publicly available tool, is less subjective and error prone than the same manual process done by a human web developer, we argue that it is well‐suited for supporting the testing of complex responsive web pages

Automatically identifying potential regressions in the layout of responsive web pages

Providing a good user experience on the ever-increasing number and variety of devices being used to browse the web is a difficult, yet critical, task. With Responsive Web Design (RWD), front-end web developers design web pages so that they dynamically resize and rearrange content to best fit the dimensions of a device’s screen. However, when making code modifications to a responsive page, developers can easily introduce regressions from the correct layout that have detrimental effects at unpredictable screen sizes. For instance, the source code change that a developer makes to improve the layout at one screen size may obscure a page’s content at other sizes. Current approaches to testing are often insufficient because they rely on limited tools and error-prone manual inspections of a web page. As such, many unintended regressions in web page layout often go undetected and ultimately manifest in production web sites. To address the challenge of detecting regressions in responsive web pages, this paper presents an automated approach that extracts the responsive layout of two versions of a page and compares them, alerting developers to the differences in layout that they may wish to investigate further. We implemented the approach and empirically evaluated it on 15 real-world responsive web pages. Leveraging code mutations that a tool automatically injected into the pages as a systematic simulation of developer changes, the experiments show that the approach was highly effective. When compared with manual and automated baseline testing techniques, it detected 12.5% and 18.75% more injected changes, respectively. Along with identifying the best parameters for the method that extracts the responsive layout, the experiments show that the approach surpasses the baselines across changes that vary in their impact, but works particularly well for subtle, hard-to-detect mutants, showing the benefits of automatically identifying regressions in web page layout