@conference {Bloemen:2014:GPD:2597073.2597131,
	title = {Gentoo Package Dependencies over Time},
	booktitle = {Proceedings of the 11th Working Conference on Mining Software Repositories},
	series = {MSR 2014},
	year = {2014},
	pages = {404{\textendash}407},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Open source distributions such as Gentoo need to accurately track dependency relations between software packages in order to install working systems. To do this, Gentoo has a carefully authored database containing those relations. In this paper, we extract the Gentoo package dependency graph and its changes over time. The final dependency graph spans 15 thousand open source projects and 80 thousand dependency relations. Furthermore, the development of this graph is tracked over time from the beginning of the Gentoo project in 2000 to the first quarter of 2012, with monthly resolution. The resulting dataset provides many opportunities for research. In this paper we explore cluster analysis to reveals meaningful relations between packages and in a separate paper we analyze changes in the dependencies over time to get insights in the innovation dynamics of open source software.
},
	keywords = {dependencies, gentoo, graph, INNOVATION},
	isbn = {978-1-4503-2863-0},
	doi = {10.1145/2597073.2597131},
	url = {http://doi.acm.org/10.1145/2597073.2597131},
	author = {Bloemen, Remco and Amrit, Chintan and Kuhlmann, Stefan and Ord{\'o}{\~n}ez{\textendash}Matamoros, Gonzalo}
}
@conference {Bloemen:2014:IDO:2597073.2597079,
	title = {Innovation Diffusion in Open Source Software: Preliminary Analysis of Dependency Changes in the Gentoo Portage Package Database},
	booktitle = {Proceedings of the 11th Working Conference on Mining Software Repositories},
	series = {MSR 2014},
	year = {2014},
	pages = {316{\textendash}319},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {In this paper we make the case that software dependencies are a form of innovation adoption. We then test this on the time-evolution of the Gentoo package dependency graph. We find that the Bass model of innovation diffusion fits the growth of the number of packages depending on a given library. Interestingly, we also find that low-level packages have a primarily imitation driven adoption and multimedia libraries have primarily innovation driven growth.
},
	keywords = {dependencies, gentoo, graph, INNOVATION},
	isbn = {978-1-4503-2863-0},
	doi = {10.1145/2597073.2597079},
	url = {http://doi.acm.org/10.1145/2597073.2597079},
	attachments = {https://flosshub.org/sites/flosshub.org/files/bloeman.pdf},
	author = {Bloemen, Remco and Amrit, Chintan and Kuhlmann, Stefan and Ord{\'o}{\~n}ez{\textendash}Matamoros, Gonzalo}
}
@book {1532,
	title = {How Healthy Is My Project? Open Source Project Attributes as Indicators of Success},
	series = {IFIP Advances in Information and Communication Technology Open Source Software: Quality Verification},
	volume = {404},
	year = {2013},
	note = {"The dataset used has thus been obtained through a third source which has made
the data publicly available [18]. FlossMole.org contains data collected for the period
2006 to December 2009 from which a dataset was compiled of 125,700 projects. "},
	pages = {30 - 44},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {Determining what factors can influence the successful outcome of a software project has been labeled by many scholars and software engineers as a difficult problem. In this paper we use machine learning to create a model that can determine the stage a software project has obtained with some accuracy. Our model uses 8 Open Source project metrics to determine the stage a project is in. We validate our model using two performance measures; the exact success rate of classifying an Open Source Software project and the success rate over an interval of one stage of its actual performance using different scales of our dependent variable. In all cases we obtain an accuracy of above 70\% with one away classification (a classification which is away by one) and about 40\% accuracy with an exact classification. We also determine the factors (according to one classifier) that uses only eight variables among all the variables available in SourceForge, that determine the health of an OSS project.
},
	keywords = {flossmole, sourceforge},
	isbn = {978-3-642-38928-3},
	issn = {1868-422X},
	doi = {10.1007/978-3-642-38928-3_3},
	url = {http://link.springer.com/chapter/10.1007/978-3-642-38928-3_3},
	attachments = {https://flosshub.org/sites/flosshub.org/files/OSSHealth_1.0.pdf},
	author = {Piggot, James and Amrit, Chintan},
	editor = {Petrinja, Etiel and Succi, Giancarlo and Ioini, Nabil and Sillitti, Alberto}
}
