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Diagnosis of Cross-Browser Compatibility Issues via Machine Learning

 

Marketed By :  LAP LAMBERT Academic Publishing   Sold By :  Kamal Books International  
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  • Product Description
 

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.

Product Specifications
SKU :COC76190
AuthorNataliia Semenenko
LanguageEnglish
BindingPaperback
Number of Pages68
Publishing Year2014-03-11T00:00:00.000
ISBN9783659185564
Edition1 st
Book TypeReal analysis, real variables
Country of ManufactureIndia
Product BrandLAP LAMBERT Academic Publishing
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-10-08 00:00:00
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