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

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,

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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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