Mining concepts from code with probabilistic topic models

Erik Linstead, Paul Rigor, Sushil Krishna Bajracharya, Cristina Videira Lopes, Pierre Baldi

Abstract

We develop and apply statistical topic models to software as a means of extracting concepts from source code. The effectiveness of the technique is demonstrated on 1,555 projects from SourceForge and Apache consisting of 113,000 files and 19 million lines of code. In addition to providing an automated, unsupervised, solution to the problem of summarizing program functionality, the approach provides a probabilistic framework with which to analyze and visualize source file similarity. Finally, we introduce an information-theoretic approach for computing tangling and scattering of extracted concepts, and present preliminary results

BibTeX

@inproceedings{Linstead-al:ASE07,
  author    = {Erik Linstead and
               Paul Rigor and
               Sushil Krishna Bajracharya and
               Cristina Videira Lopes and
               Pierre Baldi},
  title     = {Mining concepts from code with probabilistic topic models},
  booktitle = {ASE},
  pages     = {461--464},
  publisher = {{ACM}},
  year      = {2007},
}

Mining concepts from code with probabilistic topic models

Abstract

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