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 building
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). The
classifiers can assist with predicting the future of any submitted
OSS project(i.e. whether the project will be ported by
other popular OSS projects). We argue that this new aspect
of measuring successfulness of OSS projects can be added
as an additional metric in previously proposed models of
OSS successfulness. We have experimentally evaluated the
proposed approach in the SourceForge and the FreshMeat
project data collected by the FLOSS project. The reported
results are promising and demonstrate the significance of
the information that OSS repository meta-data can provide.
|
