@conference {Gousios:2014:DPD:2597073.2597122,
	title = {A Dataset for Pull-based Development Research},
	booktitle = {Proceedings of the 11th Working Conference on Mining Software Repositories},
	series = {MSR 2014},
	year = {2014},
	pages = {368{\textendash}371},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Pull requests form a new method for collaborating in distributed software development. To study the pull request distributed development model, we constructed a dataset of almost 900 projects and 350,000 pull requests, including some of the largest users of pull requests on Github. In this paper, we describe how the project selection was done, we analyze the selected features and present a machine learning tool set for the R statistics environment.
},
	keywords = {Distributed software development, Empirical software engineering, msr data showcase, pull request, pull-based development},
	isbn = {978-1-4503-2863-0},
	doi = {10.1145/2597073.2597122},
	url = {http://doi.acm.org/10.1145/2597073.2597122},
	attachments = {https://flosshub.org/sites/flosshub.org/files/pullreqs-dataset.pdf},
	author = {Gousios, Georgios and Zaidman, Andy}
}
@conference {SGKL09,
	title = {Evaluating the Quality of Open Source Software},
	booktitle = {Electronic Notes in Theoretical Computer Science},
	volume = {233},
	year = {2008},
	note = {"the software source code and the associated data stored in the version control system, the bug tracking databases, the mailing lists, and the wikis allow us to evaluate quality in a transparent way"
"The data collection system collects the raw data from open source projects"
Mailing lists are measured in: Number of unique subscribers, Number of messages in user/support list per month, Number of messages in developers list per month, Average thread depth},
	month = {03/2009},
	pages = {5{\textendash}28},
	publisher = {The Reengineering Forum},
	organization = {The Reengineering Forum},
	abstract = {Traditionally, research on quality attributes was either kept under wraps within the organization that performed it, or carried out by outsiders using narrow, black-box techniques. The emergence of open source software has changed this picture allowing us to evaluate both software products and the processes that yield them. Thus, the software source code and the associated data stored in the version control system, the bug tracking databases, the mailing lists, and the wikis allow us to evaluate quality in a transparent way. Even better, the large number of (often competing) open source projects makes it possible to contrast the quality of comparable systems serving the same domain. Furthermore, by combining historical source code snapshots with significant events, such as bug discoveries and fixes, we can further dig into the causes and effects of problems. Here we present motivating examples, tools, and techniques that can be used to evaluate the quality of open source (and by extension also proprietary) software. },
	keywords = {bug tracking system, email, email archives, mailing list, metrics, open source, process quality attributes, product quality attributes, source code, SQO-OSS, wiki},
	doi = {10.1016/j.entcs.2009.02.058},
	url = {http://www.dmst.aueb.gr/dds/pubs/conf/2008-SQM-SQOOSS/html/SGKL09.html},
	attachments = {https://flosshub.org/sites/flosshub.org/files/entcs-sqooss.pdf},
	author = {Diomidis Spinellis and Gousios, Georgios and Vassilios Karakoidas and Panagiotis Louridas and Paul J. Adams and Samoladas, Ioannis and Ioannis Stamelos}
}
