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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.
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About the Author
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.
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Diagnosis of Cross-Browser Compatibility Issues via Machine Learning
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LAP LAMBERT Academic Publishing Mär 2014, 2014
ISBN 10: 3659185566
ISBN 13: 9783659185564
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 68 pp. Englisch. Seller Inventory # 9783659185564
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Diagnosis of Cross-Browser Compatibility Issues Via Machine Learning
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3659185566
ISBN 13: 9783659185564
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Diagnosis of Cross-Browser Compatibility Issues Via Machine Learning
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3659185566
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Condition: New. Print on Demand pp. 68 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam. Seller Inventory # 132677753
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Diagnosis of Cross-Browser Compatibility Issues via Machine Learning
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ISBN 10: 3659185566
ISBN 13: 9783659185564
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783659185564
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Diagnosis of Cross-Browser Compatibility Issues Via Machine Learning
Published by
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3659185566
ISBN 13: 9783659185564
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Seller: Biblios, Frankfurt am main, HESSE, Germany
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Condition: New. PRINT ON DEMAND pp. 68. Seller Inventory # 18127877036
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Diagnosis of Cross-Browser Compatibility Issues via Machine Learning
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ISBN 13: 9783659185564
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Semenenko NataliiaNataliia 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 succes. Seller Inventory # 5137913
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Diagnosis of Cross-Browser Compatibility Issues via Machine Learning
Published by
LAP LAMBERT Academic Publishing Mär 2014, 2014
ISBN 10: 3659185566
ISBN 13: 9783659185564
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Taschenbuch. Condition: Neu. 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
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Diagnosis of Cross-Browser Compatibility Issues via Machine Learning
Published by
LAP LAMBERT Academic Publishing, 2014
ISBN 10: 3659185566
ISBN 13: 9783659185564
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Taschenbuch. Condition: Neu. Diagnosis of Cross-Browser Compatibility Issues via Machine Learning | Nataliia Semenenko | Taschenbuch | 68 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659185564 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 105355339