X-PERT: Accurate Identification of Cross-Browser Issues in Web Applications
Shauvik Roy Choudhary, Mukul Prasad, and Alessandro Orso
Georgia Tech, USA; Fujitsu Labs, USA
Track: Technical Research
Session: Empirical Studies
Due to the increasing popularity of web applications, and the number of browsers and platforms on which such applications can be executed, cross-browser incompatibilities (XBIs) are becoming a serious concern for organizations that develop web-based software. Most of the techniques for XBI detection developed to date are either manual, and thus costly and error-prone, or partial and imprecise, and thus prone to generating both false positives and false negatives. To address these limitations of existing techniques, we developed X-PERT, a new automated, precise, and comprehensive approach for XBI detection. X-PERT combines several new and existing differencing techniques and is based on our findings from an extensive study of XBIs in real-world web applications. The key strength of our approach is that it handles each aspects of a web application using the differencing technique that is best suited to accurately detect XBIs related to that aspect. Our empirical evaluation shows that X-PERT is effective in detecting real-world XBIs, improves on the state of the art, and can provide useful support to developers for the diagnosis and (eventually) elimination of XBIs.