Automated visual classification of DOM‐based presentation failure reports for responsive web pages. (14th February 2021)

• Record Type: Journal Article
• Authors: Althomali, Ibrahim; Kapfhammer, Gregory M.; McMinn, Phil
• Other Names: Memon Atif M. guestEditor.; Cohen Myra B. guestEditor.
• Is Part Of: Software testing, verification & reliability. Volume 31:Number 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-14
• Subjects: automated web testing -- automated layout failure classification -- empirical studies -- responsive layout failures -- responsive web design -- web presentation failures; Computer software -- Testing -- Periodicals; Computer software -- Verification -- Periodicals; Computer software -- Reliability -- Periodicals; 005.14
• DOI: 10.1002/stvr.1756
• Languages: English
• ISSNs: 0960-0833

Abstract

Summary: 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. Abstract : Although tools like REDECHECK automatically analyze a web page to detect presentation failures, they may inadvertently flag issues that are either not human visible or incorrect, thereby requiring developers to manually confirm and classify each reported failure. The presented tool, VERVE, automates this process by automatically analyzing images of a web page. For any of 469 web page failures, VERVE took about 4 seconds to automatically classify a failure report, frequently agreeing with a human's manual classification.