Quality Classifiers for Open Source Software Repositories

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dc.creatorTsatsaronis, George
dc.creatorHalkidi, Maria
dc.creatorGiakoumakis, Emmanouel A.
dc.date2009-04-29
dc.date.accessioned2026-07-07T13:10:05Z
dc.date.available2026-07-07T13:10:05Z
dc.descriptionOpen 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).
dc.description10 pages, 2 Tables, 7 equations, 13 references. Appeared in 2nd Artificial Intelligence Techniques in Software Engineering Workshop, AIAI 2009
dc.identifierhttps://arxiv.org/abs/0904.4708
dc.identifierhttp://arxiv.org/abs/0904.4708
dc.identifier2nd Artificial Intelligence Techniques in Software Engineering Workshop, 5th IFIP Conference on Artificial Intelligence Applications and Innovations, April 23-25, 2009, Thessaloniki, Greece
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/228945
dc.subjectSoftware Engineering
dc.subjectArtificial Intelligence
dc.subjectD.2.8
dc.titleQuality Classifiers for Open Source Software Repositories
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