Semenenko Nataliia (8 results)

Condition: New. pp. 68.

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.

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.

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.

Condition: New. PRINT ON DEMAND pp. 68.

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.

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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch.

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.