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An attractive and visually appealing appearance is important for the success of a website. Presentation failures in a site's web pages can negatively impact end users' perception of the quality of the site and the services it delivers. Debugging such failures is challenging because testers must visually inspect large web pages and analyze complex interactions among the HTML elements of a page. In this paper we propose a novel automated approach for debugging web page user interfaces. Our approach uses computer vision techniques to detect failures and can then identify HTML elements that are likely to be responsible for the failure. We evaluated our approach on a set of real-world web applications and found that the approach was able to accurately and quickly identify faulty HTML elements.

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A consistent cross-browser user experience is crucial for the success of a website. Layout Cross Browser Issues (XBIs) can severely undermine a website's success by causing web pages to render incorrectly in certain browsers, thereby negatively impacting users' impression of the quality and services that the web page delivers. Existing Cross Browser Testing (XBT) techniques can only detect XBIs in websites. Repairing them is, hitherto, a manual task that is labor intensive and requires signi cant expertise. Addressing this concern, our paper proposes a technique for automatically repairing layout XBIs in websites using guided search-based techniques. Our empirical evaluation showed that our approach was able to successfully x 86% of layout XBIs reported for 15 di erent web pages studied, thereby improving their cross-browser consistency. CCS CONCEPTS•So ware and its engineering →So ware testing and debugging; Search-based so ware engineering; KEYWORDSCross-browser issues; automated search-based repair; web apps. ACM Reference format:

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