What's the code?: automatic classification of source code archives

Secil Ugurel, Robert Krovetz, C. Lee Giles

Abstract: There are various source code archives on the World Wide Web. These archives are usually organized by application categories and programming languages. However, manually organizing source code repositories is not a trivial task since they grow rapidly and are very large (on the order of terabytes). We demonstrate machine learning methods for automatic classification of archived source code into eleven application topics and ten programming languages. For topical classification, we concentrate on C and C++ programs from the Ibiblio and the Sourceforge archives. Support vector machine (SVM) classifiers are trained on examples of a given programming language or programs in a specified category. We show that source code can be accurately and automatically classified into topical categories and can be identified to be in a specific programming language class.