Browserbite: Accurate Cross-Browser

assoverwroughtΤεχνίτη Νοημοσύνη και Ρομποτική

6 Νοε 2013 (πριν από 3 χρόνια και 10 μήνες)

88 εμφανίσεις



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
comp
a
rison


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 image
Image
segmentation
(
into ROIs
)
ROI
comparison
Example of true positive

Example of false positive

ROI comparison + ML

Web page

Static image

Image

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!