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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
Outline
• Introduction• Visual cross-browser testing• Machine learning model• Results and future work
Cross-browser visual testing
Internet Explorer 9 Internet Explorer 8
Where’s that button?
Goal
• Develop method for cross-browser visual layout testing
• Replace human labor in visual testing• Evaluate detected errors
Methods• DOM (Document Object Model) based:
Mogotest (www.mogotest.com), Browsera (www.browsera.com)
• Image processing – non-invasive black box testing – Our current approach
Web page Static image
Cross-Browser Visual testing
Web page visual segmentation
• Image segmentation into regions of interest (ROI)
• ROI comparison
www.htcomp.ee
ROI comparison
• Position• Size• Geometry• Correlation
ROI from WIN7 Chrome
ROI from WIN7 IE8
VS
Visual testing results
• Test set of 140 web pages from alexa.com• 98% recall• 66% precision
Web page Static imageImage
segmentation(into ROIs)
ROI comparison
Example of true positive Example of false positive
ROI comparison + ML
Web page Static imageImage
segmentation(into ROIs)
ROI comparison
Classification
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
ROI features
• 10 histogram bins• Correlation index• Horizontal and vertical position• Horizontal and vertical size• Configuration index• Mismatch Density
Machine learning
• Neural network• Three layers• 11 neurons in hidden layer• Five-fold cross-validation
• Classification tree
Results and Conclusions
Measure Plain Browserbite
Mogotest Classification tree
Neural network
Precision 0.66 0.75 0.844 0.964
Recall 0.98 0.82 0.792 0.886
F-score 0.79 0.78 0.81 0.923
Results and conclusions
1. 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.
2. Choudhary, S.R., Versee, H., and Orso, A. (2010). WEBDIFF: Automated identification of cross-browser issues in web applications. (ICSM), pp. 1–10.
Tool Mogotest CrossCheck [1] WebDiff [2] BB+ML
Precision 75% 36% 21% 96%
Future work
• Combination of image processing and DOM methods
• Dynamic content suppression
Thank You!
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