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Browserbite: Accurate Cross-Browser Testing via Machine Learning Over Image Features

This paper presents a novel approach to cross-browser visual layout testing through machine learning. By focusing on image features, we aim to automate the visual testing process, reducing the reliance on human labor. Our methodology employs web page segmentation into regions of interest (ROIs) and analyzes positional and size correlations across different browsers. With a detection recall rate of 98% and a precision of 66%, our results show promise for identifying visual discrepancies effectively. Future work will explore integrating image processing with traditional DOM methods for enhanced testing.

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Browserbite: Accurate Cross-Browser Testing via Machine Learning Over Image Features

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  1. Browserbite: Accurate Cross-Browser Testing via Machine Learning Over Image Features Nataliia Semenenko*, Tõnis Saar** and Marlon Dumas* *{nataliia,marlon.dumas}@ut.ee, Institute of Computer Science, University of Tartu, Estonia **tonis.saar@stacc.ee, Browsrbite and STACC, Tallinn, Estonia

  2. Outline • Introduction • Visual cross-browser testing • Machine learning model • Results and future work

  3. Cross-browser visual testing Where’sthatbutton? Internet Explorer 9 Internet Explorer 8

  4. Goal • Develop method for cross-browser visual layout testing • Replace human labor in visual testing • Evaluate detected errors

  5. Web page Static image Methods • DOM (Document Object Model) based: Mogotest (www.mogotest.com), Browsera (www.browsera.com) • Image processing – non-invasive black box testing – Our current approach

  6. Cross-Browser Visual testing

  7. Web page visual segmentation • Image segmentation into regions of interest (ROI) • ROI comparison www.htcomp.ee

  8. ROI comparison • Position • Size • Geometry • Correlation VS ROI from WIN7 Chrome ROI from WIN7 IE8

  9. Visual testing results • Test set of 140 web pages from alexa.com • 98% recall • 66% precision Example of true positive Example of false positive

  10. Image ROI Web page Static image segmentation comparison ( into ROIs ) ROI comparison + ML Classification

  11. Machine learning • 140 most popular websites of Estonia according to www.alexa.com • 1200 potential incompatibilities • 40 subjects from 6 countries • Two classes :False positive vs True postive • Each ROI pair had 8 judgments • Inter-rater reliability 0,94

  12. ROI features • 10 histogram bins • Correlation index • Horizontal and vertical position • Horizontal and vertical size • Configuration index • Mismatch Density

  13. Machine learning • Neural network • Three layers • 11 neurons in hidden layer • Five-fold cross-validation • Classification tree

  14. Results and Conclusions

  15. Results and conclusions • Choudhary, S.R., Prasad, M.R., and Orso, A. (2012). CrossCheck: Combining Crawling and Differencing to Better Detect Cross-browser Incompatibilities in Web Applications. (ICST), 2012 IEEE Fifth International Conference On, pp. 171–180. • Choudhary, S.R., Versee, H., and Orso, A. (2010). WEBDIFF: Automated identification of cross-browser issues in web applications. (ICSM), pp. 1–10.

  16. Future work • Combination of image processing and DOM methods • Dynamic content suppression

  17. Thank You!

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