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Browserbite: Accurate Cross-Browser Testing via Machine Learning Over Image Features Nataliia Semenenko*, Tõnis Saar** and Marlon Dumas*

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Presentation on theme: "Browserbite: Accurate Cross-Browser Testing via Machine Learning Over Image Features Nataliia Semenenko*, Tõnis Saar** and Marlon Dumas*"— Presentation transcript:

1 Browserbite: Accurate Cross-Browser Testing via Machine Learning Over Image Features Nataliia Semenenko*, Tõnis Saar** and Marlon Dumas* Institute of Computer Science, University of Tartu, Estonia Browsrbite and STACC, Tallinn, Estonia

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

3 Cross-browser visual testing Internet Explorer 9Internet Explorer 8 Where’s that button?

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

5 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 pageStatic image

6 Cross-Browser Visual testing

7 Web page visual segmentation Image segmentation into regions of interest (ROI) ROI comparison

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

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 ROI comparison + ML Web pageStatic image Image segmentation (into ROIs) ROI comparison Classification

11 Machine learning 140 most popular websites of Estonia according to 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 MeasurePlain BrowserbiteMogotestClassification tree Neural network Precision Recall F-score

15 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– Choudhary, S.R., Versee, H., and Orso, A. (2010). WEBDIFF: Automated identification of cross-browser issues in web applications. (ICSM), pp. 1–10. ToolMogotestCrossCheck [1]WebDiff [2]BB+ML Precision75%36%21%96%

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

17 Thank You!


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