Quality Classifiers for Open Source Software Repositories
Open Source Software (OSS) often relies on large repositories, like SourceForge, for initial incubation. The OSS repositories offer a large variety of meta-data providing interesting information about projects and their success. In this paper the authors propose a data mining approach for training classifiers on the OSS metadata provided by such data repositories. The classifiers learn to predict the successful continuation of an OSS project. The 'successfulness' of projects is defined in terms of the classifier confidence with which it predicts that they could be ported in popular OSS projects (such as FreeBSD, Gentoo Portage).