Classification of source code archives

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Classification of source code archives

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Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval table of contents

Toronto, Canada

POSTER SESSION: Posters table of contents

Pages: 425 - 426

Year of Publication: 2003

ISBN:1-58113-646-3

Authors

Robert Krovetz	NEC Laboratories America, Princeton, NJ
Secil Ugurel	NEC Laboratories America, Princeton, NJ
C. Lee Giles	Pennsylvania State University, University Park, PA

Sponsor

ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 10, Downloads (12 Months): 50, Citation Count: 1

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ABSTRACT

The World Wide Web contains a number of source code archives. Programs are usually classified into various categories within the archive by hand. We report on experiments for automatic classification of source code into these categories. We examined a number of factors that affect classification accuracy. Weighting features by expected entropy loss makes a significant improvement in classification accuracy. We show a Support Vector Machine can be trained to classify source code with a high degree of accuracy. We feel these results show promise for software reuse.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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