Quality classifiers for open source software repositories

  • Tsatsaronis G
  • Halkidi M
  • Giakoumakis E
  • 1


    Mendeley users who have this article in their library.
  • N/A


    Citations of this article.


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 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).

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

There are no full text links


  • G.a Tsatsaronis

  • M.b Halkidi

  • E.A.a Giakoumakis

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free