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Web applications can be accessed through a variety of user agent configurations, in which the browser, platform, and device capabilities are not under the control of developers. In order to grant the compatibility of a web application in each environment, developers must manually inspect their web application in a wide variety of devices, platforms...
Cross-Browser Incompatibilities, namely XBIs, are differences in the behavior of web applications as they are rendered in distinct browser implementations. Web applications can be rendered in a wide variety of configuration environments, varying their browser implementation (eg. Google Chrome, Microsoft Internet Explorer and Mozilla Firefox). Even...
Cross Browser Incompatibilities (XBIs) stands for compatibility issues which can be observed while rendering the same Web application in different browsers. The increasing number of browser implementations and the continuous evolving characteristic of Web technologies, lead to differences in how browsers behave and render Web applications. Every el...
Cross-Browser Incompatibilities (XBIs) represent inconsistencies in Web Application when introduced in different browsers. The growing number of implementation of browsers (Internet Explorer, Microsoft Edge, Mozilla Firefox, Google Chrome) and the constant evolution of the specifications of Web technologies provided differences in the way that the...
Web applications can be accessed through a variety of user agents (eg. Mozilla Firefox, Internet Explorer, Apple Safari) and platforms (eg. Android, iOS, Windows, Linux). Even though the community has proposed specifications for standardizing these environments, the variety of browser and platform implementations can still present differences while rendering web applications. The detection of Cross-browser and Cross-platform incompatibilities is labor intensive and expensive task in the Web engineering process and is frequently conducted by web developers and testers. This research project goal is to develop automatic detection of Cross-browser and Cross-platform incompatibilities strategies using classification techniques, based on machine learning. This project activities will be conducted by means of manual Cross-browser and Cross-platform incompatibilities investigation and mining this data for identifying patterns which could be used in the development of automatic detection strategies for these incompatibilities. The main contribution of this project work towards enhancing the Web engineering process, reducing costs associated to the manual identification of these incompatibilities.