Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ipm.2006.07.019
Title: Supervised categorization of JavaScriptTM using program analysis features
Authors: Lu, W.
Kan, M.-Y. 
Keywords: Automated code classification
ECMAScript
Information retrieval
JavaScript
Machine learning
Program classification
Program comprehension
Program pattern
Software metrics
Source clone
Issue Date: 2007
Source: Lu, W.,Kan, M.-Y. (2007). Supervised categorization of JavaScriptTM using program analysis features. Information Processing and Management 43 (2) : 431-444. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ipm.2006.07.019
Abstract: Web pages often embed scripts for a variety of purposes, including advertising and dynamic interaction. Understanding embedded scripts and their purpose can often help to interpret or provide crucial information about the web page. We have developed a functionality-based categorization of JavaScript, the most widely used web page scripting language. We then view understanding embedded scripts as a text categorization problem. We show how traditional information retrieval methods can be augmented with the features distilled from the domain knowledge of JavaScript and software analysis to improve classification performance. We perform experiments on the standard WT10G web page corpus, and show that our techniques eliminate over 50% of errors over a standard text classification baseline. © 2006 Elsevier Ltd. All rights reserved.
Source Title: Information Processing and Management
URI: http://scholarbank.nus.edu.sg/handle/10635/39494
ISSN: 03064573
DOI: 10.1016/j.ipm.2006.07.019
Appears in Collections:Staff Publications

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