Using Computer Vision to Detect Web Browser Display Errors

As the functionality and complexity of the WWW continues to grow so does the need for WWW quality assurance and testing. Although there have been numerous approaches to automated Web testing, existing techniques analyze primarily textual information, and the final judgment on correctness of layout is via human observation. The motivation of this paper is to employ computer vision techniques to detect Web display errors. To do this, we analyze images of the rendered pages rather than the HTML to discover errors. Our approach includes page segmentation, dynamic matching and outlier identification. We show that the approach successfully detects layout errors in the Opera browser on Microsoft Websites, while minimizing false alarms.