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12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00)
Using latent semantic analysis to identify similarities in source code to support program understanding
Vancouver, British Columbia, Canada
November 13-November 15
ISBN: 0-7695-0909-6
ASCII Text
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J.I. Maletic, A. Marcus, "Using latent semantic analysis to identify similarities in source code to support program understanding," Tools with Artificial Intelligence, IEEE International Conference on, pp. 0046, 12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00), 2000.
 
 
BibTex
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@article{ 10.1109/TAI.2000.889845,
author = {J.I. Maletic and A. Marcus},
title = {Using latent semantic analysis to identify similarities in source code to support program understanding},
journal ={Tools with Artificial Intelligence, IEEE International Conference on},
volume = {0},
year = {2000},
isbn = {0-7695-0909-6},
pages = {0046},
doi = {http://doi.ieeecomputersociety.org/10.1109/TAI.2000.889845},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Tools with Artificial Intelligence, IEEE International Conference on
TI - Using latent semantic analysis to identify similarities in source code to support program understanding
SN - 0-7695-0909-6
SP
EP
A1 - J.I. Maletic,
A1 - A. Marcus,
PY - 2000
KW - reverse engineering; information retrieval; natural languages; computational linguistics; system documentation; statistical analysis; latent semantic analysis; source code similarities; program understanding; information retrieval method; source code; corpus based statistical method; natural language; software components; LSA; software component clustering; nontrivial software system; Mosaic; internal documentation
VL - 0
JA - Tools with Artificial Intelligence, IEEE International Conference on
ER -
 
J.I. Maletic, Div. of Comput. Sci., Memphis Univ., Memphis, TN, USA
A. Marcus, Div. of Comput. Sci., Memphis Univ., Memphis, TN, USA
Abstract: The paper describes the results of applying Latent Semantic Analysis (LSA), an advanced information retrieval method, to program source code and associated documentation. Latent semantic analysis is a corpus based statistical method for inducing and representing aspects of the meanings of words and passages (of natural language) reflective in their usage. This methodology is assessed for application to the domain of software components (i.e., source code and its accompanying documentation). Here LSA is used as the basis to cluster software components. This clustering is used to assist in the understanding of a nontrivial software system, namely a version of Mosaic. Applying latent semantic analysis to the domain of source code and internal documentation for the support of program understanding is a new application of this method and a departure from the normal application domain of natural language.
Index Terms:
reverse engineering; information retrieval; natural languages; computational linguistics; system documentation; statistical analysis; latent semantic analysis; source code similarities; program understanding; information retrieval method; source code; corpus based statistical method; natural language; software components; LSA; software component clustering; nontrivial software system; Mosaic; internal documentation
Citation:
J.I. Maletic, A. Marcus, "Using latent semantic analysis to identify similarities in source code to support program understanding," ictai, pp.0046, 12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00), 2000
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