Quality Classifiers for Open Source Software Repositories

Details Quality Classifiers for Open Source Software Repositories Quality Classifiers for Open Source Software Repositories

2009-04-29

arxiv.org/abs/0904.4708v1

2nd Artificial Intelligence Techniques in Software Engineering Workshop, 5th IFIP Conference on Artificial Intelligence Applic

Computer Science

Software Engineering

10 pages, 2 Tables, 7 equations, 13 references. Appeared in 2nd Artificial Intelligence Techniques in Software Engineering Wor

Scientific paper

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).

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