Classification of source code archives

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.