@conference {1217,
	title = {Trends That Affect Temporal Analysis Using SourceForge Data},
	booktitle = {5th Workshop on Public Data about Software Development (WoPDaSD 2010)},
	year = {2010},
	note = {"In this paper we examine some of the limitations of artifact data by specifically addressing the applicability of SourceForge data to the study of project evolution."

"For our analysis we examine 9,997 Production/Stable or Maintenance phase projects stored in CVS on SourceForge and extracted in October of 2006 [5]"},
	abstract = {SourceForge is a valuable source of software artifact data for researchers who study project evolution and developer behavior. However, the data exhibit patterns that may bias temporal analyses. Most notable are cliff walls in project source code repository timelines, which indicate large commits that are out of character for the given project. These cliff walls often hide significant periods of development and developer collaboration{\textemdash}a threat to studies that rely on SourceForge repository data. We demonstrate how to identify these cliff walls, discuss reasons for their appearance, and propose preliminary measures for mitigating their effects in evolution-oriented studies.},
	keywords = {cliff walls, committers, cvs, evolution, growth, source code, sourceforge, time, time series},
	attachments = {https://flosshub.org/sites/flosshub.org/files/wopdasd001.pdf},
	author = {MacLean, Alexander C. and Pratt, Landon J. and Krein, Jonathan L. and Knutson, Charles D.}
}
@conference {930,
	title = {Author entropy vs. file size in the GNOME suite of applications},
	booktitle = {2009 6th IEEE International Working Conference on Mining Software Repositories (MSR)2009 6th IEEE International Working Conference on Mining Software Repositories},
	year = {2009},
	pages = {91 - 94},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Vancouver, BC, Canada},
	abstract = {We present the results of a study in which author entropy was used to characterize author contributions per file. Our analysis reveals three patterns: banding in the data, uneven distribution of data across bands, and file size dependent distributions within bands. Our results suggest that when two authors contribute to a file, large files are more likely to have a dominant author than smaller files.},
	keywords = {author entropy, contributions, gnome, msr challenge},
	isbn = {978-1-4244-3493-0},
	doi = {10.1109/MSR.2009.5069484},
	author = {Casebolt, Jason R. and Krein, Jonathan L. and MacLean, Alexander C. and Knutson, Charles D. and Delorey, Daniel P.}
}
