1	Supervised Categorization of JavaScript^TM using Program Analysis Features Wei Lu and Min-Yen Kan luwei@nus.edu.sg kanmy@comp.nus.edu.sg Computer Science Program School of Computing Singapore-MIT Alliance National University of Singapore
2	Why to categorize JavaScript? Most crawlers and indexers ignore crucial information conveyed by external programs like JavaScript™ Can we have them summarized automatically?
3	Categorization Scheme
4	Outline Introduction Categorization Scheme Feature Extraction Techniques Related Work Our Work Lexical Analysis Syntax Analysis Metrics Analysis Object Communication Analysis Contextual Analysis Conclusion
5	Related Work Source code categorization: Ugurel et.al.’s work published in 2002 Seminal work on the subject Consists of two tasks Programming Language classification: find out the type of programming language – keywords, bi-grams… non-interesting! Topic classification: find out the topic related to the code - relies heavily on external resources (e.g. README file, code header), did not analyze source code itself much Can we extract features from source code itself?
6	Text Categorization Baseline Vector Space Model (VSM) is used in Text Categorization Bag of words approach Our baseline using Text Categorization Bag-of-Word approach: accuracy 87.47% A high baseline, but still has a gap of 12.5% to improve Also important to measure error reduction rate
7	Lexical Analysis How to tokenize JavaScript more reasonably? First attempt of improvement over baseline - we believe using a compiler-based approach is more reasonable Similar to POS tags used in Natural Language Processing , we introduce a tagset for JavaScript tokenization/tagging The tagging process comes with a token normalization Tags used: KEY:keyword; VAR:variable; SYM:symbol; NUM:number; STR:string; CMT:comment; REG:regexp
8	Evaluation on Lexical Analysis
9	Syntax Analysis
10	Evaluation on Syntax Analysis
11	Code Metrics Analysis Metrics: measurement of program source code Complexity: statistical measurements, e.g. CC, IFIN We proposed our own metrics in addition to published metrics
12	Evaluation on Metrics Analysis
13	Outline Introduction Categorization Scheme Feature Extraction Techniques Related Work Our Work Lexical Analysis Syntax Analysis Metrics Analysis Object Communication Analysis Contextual Analysis Conclusion
14	Object Communication Analysis var msg = "Welcome to this page"; banner(0); function banner (index){ var newWin = window.open(); frm.txt.value="ok"; window.status = msg.substring(0, index); index = index++; if (index >= msg.length) index = 0; window.setTimeout("banner("+index+" ) " , 100); }
15	Contextual Analysis Extracting information from the context of the enclosing web page
16	Evaluation on Object Communication and Contextual Analysis
17	Outline Introduction Categorization Scheme Feature Extraction Techniques Related Work Our Work Overall Evaluation Conclusion
18	Evaluation on All Components
19	Outline Categorization Scheme Feature Extraction Techniques Conclusion
20	Contributions Shown that program analysis can enhance source code categorization performance Both context-free and context-sensitive analysis Case study of JavaScript categorization New, functionality-based categorization Tool for feature extraction from JavaScript
21	Conclusions Limitations: Annotator Agreement Dynamic Analysis Incompleteness Choice of Classifier Future Work Source code classification of other languages Firefox extension / IE plug-in Dataset and system prototype available at: http://wing.comp.nus.edu.sg/~luwei/SMART
22	Question? Dataset and system prototype available at: http://wing.comp.nus.edu.sg/~luwei/SMART First author’s undergraduate honours year project thesis: http://wing.comp.nus.edu.sg/publications/theses/weiLuThesis.pdf Contacts: Wei LU: luwei@nus.edu.sg Min-Yen KAN: kanmy@comp.nus.edu.sg
23	Guidelines for talk 20 minutes 5 minutes for questions