Diagnosis of Cross-Browser Compatibility Issues via Machine Learning - Softcover

Semenenko, Nataliia

Softcover

ISBN 10: 3659185566 ISBN 13: 9783659185564

Verlag: LAP LAMBERT Academic Publishing, 2014

Inhaltsangabe

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.

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Über die Autorin bzw. den Autor

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.

Details zur Ausgabe

• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsdatum: 2014
• Sprache: Englisch
• ISBN 10: 3659185566
• ISBN 13: 9783659185564
• Einband: Taschenbuch
• Anzahl der Seiten: 68
• Kontakt zum Hersteller: preigu GmbH & Co. KG; mail@preigu.de; ; Lengericher Landstr. 19; Osnabrück; Niedersachsen; 49078; Deutschland