Diagnosis of Cross-Browser Compatibility Issues via Machine Learning - Couverture souple

Semenenko, Nataliia

 
9783659185564: Diagnosis of Cross-Browser Compatibility Issues via Machine Learning
Couverture souple
ISBN 10 : 3659185566 ISBN 13 : 9783659185564
Editeur : LAP LAMBERT Academic Publishing, 2014

Synopsis

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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

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.

Biographie de l'auteur

Nataliia started her studies in NTUU 'KPI', Kyiv, Ukraine. After completing her bachelor degree in Computer Science she moved to Estonia for Software Engineering Master programme in Tartu University which she succesfully completed in 2013.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2014
Langue
anglais
ISBN 10 
3659185566
ISBN 13 
9783659185564
Reliure
Broché
Nombre de pages
68
Coordonnées du fabricant
non disponible
Personne responsable
KreGa Onlinehandel GmbH
4950000000000
info@krega-onlinehandel.de

Birkenweg 31
Brakel
33034
Allemagne