Automatic Software Clustering via Latent Semantic Analysis

DOI 10.1109/ase.1999.802296 dblp

Abstract

The paper describes the initial results of applying Latent Semantic Analysis (LSA) 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). The intent of applying Latent Semantic Analysis to software components is to automatically induce a specific semantic meaning of a given component. Here LSA is used as the basis to cluster software components. Results of applying this method to the LEDA library and MINIX operating system are given. Applying Latent Semantic Analysis to the domain of source code and internal documentation for the support of software reuse is a new application of this method and a departure from the normal application domain of natural language.

BibTeX

@inproceedings{Maletic-Valluri:ASE99,
  author    = {Jonathan I. Maletic and
               Naveen Valluri},
  title     = {Automatic Software Clustering via Latent Semantic Analysis},
  booktitle = {ASE},
  pages     = {251--254},
  publisher = {{IEEE} Computer Society},
  year      = {1999},
}

Automatic Software Clustering via Latent Semantic Analysis

Abstract

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