A Comparative Report on Web Browsers: X-pert, Webdiff, and Performance

This report provides a comparative analysis of web browsers, specifically focusing on the tools X-pert and Webdiff. The report details how X-pert, an automated process, detects cross-browser issues using various technologies, and how Webdiff evaluates applications to identify similar issues. Webdiff utilizes screen captures and DOM information to analyze visual discrepancies, employing computer vision and graph theory for issue detection. The report highlights the selection of Webdiff for its ability to identify issues by matching DOM components across different browsers and applying machine learning to render visual discrepancies. The report underscores the importance of these tools in automating the detection of cross-browser issues, providing detailed information about affected HTML elements to aid developers. Additionally, the report references relevant sources on behavioral experiments and educational testing to provide a comprehensive overview of the topic.

Running head: COMPARISON INBETWEEN WEB BROWSERS
Comparison in between web browsers
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Author Note

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COMPARISON INBETWEEN WEB BROWSERS
The comparison of X-pert and Webdiff
 X-pert is a new automated process used for the detection of XBI and the process various
new automated, precise and comprehensive technologies for the detection of XBI. On the
other hand Webdiff is tool used for the evaluation of the application and also used for the
identification of the issues in the browsers.
 Both the tools are used for the identification of the cross browser issues where x-pert
provides the accurate identification of the cross browser issues and web diff provides an
automated identification of the cross browser issues in web browsers.
 The X-pert tool is use for providing the users with an efficient support for the developers
and helps the users to eliminate the XBIs from the system. The webdif tool provides the
user with a visual analysis of the page’s appearance, obtained through screen captures.
 X-PERT is designed to be a comprehensive and accurate framework for detecting XBIs.
It integrates differencing techniques proposed in previous work with a novel technique
for detecting layout errors, by far the most common class of XBIs whereas The module of
WEBDIFF that performs the screen capture and the collection of DOM information
consists of a Python script. The script uses the win32api and the python image library to
automate image capturing, page scrolling, and combination of partial visual information.
To collect the information from the DOM, the script runs, within each web browser, a
JavaScript URL program that queries and collects the information. For image processing,
WEBDIFF leverages the OpenCV computer vision library.
Tool Selection and justification
The webdiff tool is selected for the identification of the issues of the web browser as the
tool matches the components of the trees in the DOM generated in the different browsers and

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COMPARISON INBETWEEN WEB BROWSERS
then the tool is used for computing the visual differences in the matching components. The tool
also incorporates the machine learning techniques and the visual discrepancies in between the
elements and the browsers are rendered in the system. Although the X-pert tool can be used for
providing important information to the developers for analyzing the XBIs manually. Hence the
webdiff tool can be used for the detection of the issues regarding the web browsers. WEBDIFF is
the first technique to apply concepts from computer vision and graph theory to identify cross-
browser issues in web applications. Our results show that WEBDIFF is practical and can find
issues in real world web applications. Cross-browser issues are prevalent in web applications.
However, existing tools require considerable manual effort from developers to detect such issues.
Our technique and prototype tool - WEBDIFF detects such issues automatically and reports them
to the developer. Along with each issue reported, the tool also provides details about the affected
HTML element, thereby helping the developer to fix the issue.

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COMPARISON INBETWEEN WEB BROWSERS
Bibliography
De Leeuw, J.R., 2015. jsPsych: A JavaScript library for creating behavioral experiments in a
Web browser. Behavior research methods, 47(1), pp.1-12.
Kubiszyn, T. and Borich, G., 2015. Educational testing and measurement. John Wiley & Sons
Incorporated.