@proceedings {1451,
	title = {Exploring the Role of Outside Organizations in Free / Open Source Software Projects},
	volume = {378},
	year = {2012},
	month = {09/2012},
	pages = {201-215},
	publisher = {IFIP AICT},
	abstract = {Free/Open Source Software (FOSS) projects have a reputation for being grass-roots efforts driven by individual contributors volunteering their time and effort. While this may be true for a majority of smaller projects, it is not always the case for large projects. As projects grow in size, importance and complexity, many come to depend on corporations, universities, NGO{\textquoteright}s and governments, for support and contributions, either financially or through seconded staff. As outside organizations get involved in projects, how does this affect their governance, transparency and direction? To study this question we gathered bug reports and commit logs for GCC and the Linux Kernel. We found that outside organizations contribute a majority of code but rarely participate in bug triaging. Therefore their code does not necessarily address the needs of others and may distort governance and direction. We conclude that projects should examine their dependence on outside organizations},
	keywords = {bug reports, commit, Community sustainability, Contributor affiliation, gcc, governance, linux kernel, Participation metrics},
	url = {http://research.engr.oregonstate.edu/hci/sites/research.engr.oregonstate.edu.hci/files/papers/forrest2012exploring.pdf},
	author = {Forrest, Darren and Jensen, Carlos and Mohan, Nitin and Davidson, Jennifer}
}
@proceedings {1289,
	title = {Cliff Walls: An Analysis of Monolithic Commits Using Latent Dirichlet Allocation},
	year = {2011},
	note = {"Our data set consists of the version control logs of almost 10,000 projects from SourceForge, acquired in late 2006"},
	month = {10/2011},
	pages = {282-298},
	publisher = {Springer},
	abstract = {Artifact-based research provides a mechanism whereby researchers may study the creation of software yet avoid many of the difficulties of direct observation and experimentation. However, there are still many challenges that can affect the quality of artifact-based studies, especially those studies examining software evolution. Large commits, which we refer to as {\textquotedblleft}Cliff Walls,{\textquotedblright} are one significant threat to studies of software evolution because they do not appear to represent incremental development. We used Latent Dirichlet Allocation to extract topics from over 2 million commit log messages, taken from 10,000 SourceForge projects. The topics generated through this method were then analyzed to determine the causes of over 9,000 of the largest commits. We found that branch merges, code imports, and auto-generated documentation were significant causes of large commits. We also found that corrective maintenance tasks, such as bug fixes, did not play a significant role in the creation of large commits.},
	keywords = {artifacts, commit, cvs, LDA, lines of code, log files, scm, sloc, sourceforge, version control},
	author = {Pratt, Landon J. and MacLean, Alexander C. and Knutson, Charles D. and Ringger, Eric K.}
}
@conference {966,
	title = {Replaying IDE interactions to evaluate and improve change prediction approaches},
	booktitle = {2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)},
	year = {2010},
	pages = {161 - 170},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Cape Town, South Africa},
	abstract = {Change prediction helps developers by recommending program entities that will have to be changed alongside the entities currently being changed. To evaluate their accuracy, current change prediction approaches use data from versioning systems such as CVS or SVN. These data sources provide a coarse-grained view of the development history that flattens the sequence of changes in a single commit. They are thus not a valid basis for evaluation in the case of development-style prediction, where the order of the predictions has to match the order of the changes a developer makes. We propose a benchmark for the evaluation of change prediction approaches based on fine-grained change data recorded from IDE usage. Moreover, the change prediction approaches themselves can use the more accurate data to fine-tune their prediction. We present an evaluation procedure and use it on several change prediction approaches, both novel and from the literature, and report on the results.},
	keywords = {cbse, change based software evolution, change prediction, changes, commit, cvs, development history, eclipseeye, ide, mylyn, spyware, svn},
	isbn = {978-1-4244-6802-7},
	doi = {10.1109/MSR.2010.5463278},
	attachments = {https://flosshub.org/sites/flosshub.org/files/161Robbes2010changePrediction.pdf},
	author = {Robbes, Romain and Pollet, Damien and Lanza, Michele}
}
@conference {1226,
	title = {Understanding the KDE Social Structure through Mining of Email Archive},
	booktitle = {2nd Workshop on Public Data about Software Development (WoPDaSD 2007)},
	year = {2007},
	note = {"Our data source is constituted by e-mails sent to KDE mailing-lists and archived by MARC"

"Two problems quickly arise: neither the e-mails addresses nor the names can be considered unique. Consequently, we used an in-depth search algorithm to put together {\textquotedblleft}name-email{\textquotedblright} couples corresponding to a same contributor. Indeed, the algorithm suggests possible merges."

"There is a specific mailing list in our data set, kde-commit, which gathers automatic notifications from the revision control system (RCS)....We measure {\textquotedblleft}commit{\textquotedblright} by the number of messages sent to the {\textquotedblleft}kde-commit{\textquotedblright} mailing list. However, we did not count {\textquotedblleft}silent{\textquotedblright} commits, nor usual messages sent to this mailing list."

"We measured activities done in BTS in two ways: {\textquotedblleft}bug opener{\textquotedblright} and {\textquotedblleft}non bug opener{\textquotedblright}.	First, we counted the number of modifications done by the contributor who opened the concerned bug report. "
},
	abstract = {In order to achieve a better understanding of FLOSS social structure, we need a definition of social position. From a theoretical perspective, we propose to think the participation as a trajectory. Empirically, we use optimal matching to build a typology of participation trajectories based on KDE email archives. We show how these trajectories structure the community as a whole by combining these results with a social network analysis.},
	keywords = {bug tracking system, bugzilla, commit, email, email archive, kde, mailing list, participation, revision control, social network analysis},
	attachments = {https://flosshub.org/sites/flosshub.org/files/wopdasd_studer_et_all_full.pdf},
	author = {Studer, Matthias and M{\"u}ller, Beno{\^\i}t and Ritschard, Gilbert}
}
