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2013 17th European Conference on Software Maintenance and Reengineering
Feature Detection in Ajax-Enabled Web Applications
Genova, Italy Italy
March 05-March 08
ISBN: 978-1-4673-5833-0
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, pp.154-163, 2013 17th European Conference on Software Maintenance and Reengineering, 2013
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