Abstract-Cross-browser (and cross-platform) issues are prevalent in modern web based applications and range from minor cosmetic bugs to critical functional failures. In spite of the relevance of these issues, cross-browser testing of web applications is still a fairly immature field. Existing tools and techniques require a considerable manual effort to identify such issues and provide limited support to developers for fixing the underlying cause of the issues. To address these limitations, we propose a technique for automatically detecting cross-browser issues and assisting their diagnosis. Our approach is dynamic and is based on differential testing. It compares the behavior of a web application in different web browsers, identifies differences in behavior as potential issues, and reports them to the developers. Given a page to be analyzed, the comparison is performed by combining a structural analysis of the information in the page's DOM and a visual analysis of the page's appearance, obtained through screen captures. To evaluate the usefulness of our approach, we implemented our technique in a tool, called WEBDIFF, and used WEBDIFF to identify cross-browser issues in nine real web applications. The results of our evaluation are promising, in that WEBDIFF was able to automatically identify 121 issues in the applications, while generating only 21 false positives. Moreover, many of these false positives are due to limitations in the current implementation of WEBDIFF and could be eliminated with suitable engineering.

Web applications tend to evolve quickly, resulting in errors and failures in test automation scripts that exercise them. Repairing such scripts to work on the updated application is essential for maintaining the quality of the test suite. Updating such scripts manually is a time consuming task, which is often difficult and is prone to errors if not performed carefully. In this paper, we propose a technique to automatically suggest repairs for such web application test scripts. Our technique is based on differential testing and compares the behavior of the test case on two successive versions of the web application: first version in which the test script runs successfully and the second version in which the script results in an error or failure. By analyzing the difference between these two executions, our technique suggests repairs that can be applied to repair the scripts. To evaluate our technique, we implemented it in a tool called WA-TER and exercised it on real web applications with test cases. Our experiments show that WATER can suggest meaningful repairs for practical test cases, many of which correspond to those made later by developers themselves.

Abstract-Web applications have gained increased popularity in the past decade due to the ubiquity of the web browser across platforms. With the rapid evolution of web technologies, the complexity of web applications has also grown, making maintenance tasks harder. In particular, maintaining crossbrowser compliance is a challenging task for web developers, as they must test their application on a variety of browsers and platforms. Existing tools provide some support for this kind of test, but developers are still required to identify and fix crossbrowser issues mainly through manual inspection. Our WEBDIFF tool addresses the limitations of existing tools by (1) automatically comparing the structural and visual characteristics of web pages when they are rendered in different browsers, and (2) reporting potential differences to developers. When used on nine real web pages, WEBDIFF automatically identified 121 issues, out of which 100 were actual problems. In this demo, we will present WEBDIFF, its underlying technology, and several examples of its use on real applications.

Modern social media have increasingly helped people separate themselves by worldview. We watch television shows and follow blogs that agree with our views, and read Twitter streams of people we like. The result is often called the echo chamber. Scholars cite political echo chambers as partly to blame for the divisive and destructive U.S. political climate. In this paper, we introduce a mobile application called Political Blend designed to combat echo chambers: it brings people with different political beliefs together for a cup of coffee. Based on interviews, we discovered that people are open to this kind of application and feel it may help the broader political environment. The primary contribution of this work is evidence that people are open to meeting those different from them, even those who ideologically oppose them. In an environment dominated by applications matching based on similarities, we see that this is an important finding.