  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 we propose a data mining approach for training
classifiers on the OSS meta-data 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).
