Abstract
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).
| Original language | English |
|---|---|
| Pages (from-to) | 179-188 |
| Number of pages | 10 |
| Journal | CEUR Workshop Proceedings |
| Volume | 475 |
| State | Published - 2009 |
| Externally published | Yes |
| Event | 5th IFIP Conference on Artificial Intelligence Applications and Innovations, AIAI 2009 - Thessaloniki, Greece Duration: Apr 23 2009 → Apr 25 2009 |