Quality Classifiers for Open Source Software Repositories

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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).
10 pages, 2 Tables, 7 equations, 13 references. Appeared in 2nd Artificial Intelligence Techniques in Software Engineering Workshop, AIAI 2009

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Software Engineering, Artificial Intelligence, D.2.8

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http://salesiana.dossiersoluciones.com/handle/123456789/228945

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0904.4708
 http://arxiv.org/abs/0904.4708
 2nd Artificial Intelligence Techniques in Software Engineering Workshop, 5th IFIP Conference on Artificial Intelligence Applications and Innovations, April 23-25, 2009, Thessaloniki, Greece

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arXiv