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Genova, Italy Italy
Mar. 5, 2013 to Mar. 8, 2013
ISBN: 978-1-4673-5833-0
pp: 154-163
ABSTRACT
In this paper we propose a method for reverse engineering the features of Ajax-enabled web applications. The method first collects instances of the DOM trees underlying the application web pages, using a state-of-the-art crawling framework. Then, it clusters these instances into groups, corresponding to distinct features of the application. The contribution of this paper lies in the novel DOM-tree similarity metric of the clustering step, which makes a distinction between simple and composite structural changes. We have evaluated our method on three real web applications. In all three cases, the proposed distance metric leads to a number of clusters that is closer to the actual number of features and classifies web page instances into these feature-specific clusters more accurately than other traditional distance metrics. We therefore conclude that it is a reliable distance metric for reverse engineering the features of Ajax-enabled web applications.
INDEX TERMS
Silhouette coefficient, web page similarity metrics, hierarchical agglomerative clustering, L method
CITATION
Natalia Negara, Nikolaos Tsantalis, Eleni Stroulia, "Feature Detection in Ajax-Enabled Web Applications", CSMR, 2013, 2011 15th European Conference on Software Maintenance and Reengineering, 2011 15th European Conference on Software Maintenance and Reengineering 2013, pp. 154-163, doi:10.1109/CSMR.2013.25
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