Diagnosis of Cross-Browser Compatibility Issues via Machine Learning

Language: English

Published by LAP LAMBERT Academic Publishing Mär 2014, 2014

3659185566 / 9783659185564

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This item is printed on demand - Print on Demand Titel. Neuware -Due to the rapid evolution of Web technologies and the failure of Web standards to uniformize every single technology evolution, Web developers are faced with the challenge of ensuring that their applications are correctly rendered across a broad range of browsers and platforms. To detect cross-browser incompatibilities, developers often resort to visually checking that each document produced by their application is consistently rendered across all relevant browsers. This manual testing approach is time consuming and error-prone. Existing cross-browser compatibility testing tools speed up this process. However, existing tools in this space suffer from over-sensitivity. Reducing the number of false positives produced by these testing tools is challenging, since defining criteria for classifying a difference as an incompatibility is to some extent subjective. This work presents a machine learning approach to improve the accuracy of two techniques for cross-browser compatibility testing - one based on image analysis and one based on DOM analysis. Two classification algorithms were used, namely classification trees and neural networks.OmniScriptum SRL, Str. Armeneasca 28/1, office 1, 2012 Chisinau 68 pp. Englisch.…

Seller Inventory # 9783659185564

Title
Diagnosis of Cross-Browser Compatibility Issues via Machine Learning
Author
Nataliia Semenenko
Publisher
LAP LAMBERT Academic Publishing Mär 2014
Publication year
2014
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3659185566
ISBN 13
9783659185564
Item weight
119 grams
Dimensions
220x150x5 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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AbeBooks seller since January 23, 2017

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Impressum Thorsten Retsch Buchversand Mimpf2000 Oberölschnitz 16 95517 Emtmannsberg Deutschland Telefon: 09209-2023188 Email: mimpf2000@online.de USt-ID-Nr.: DE 235096871 Wir führen gebrauchte Bücher aus allen Sparten der Literatur

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