@article {1240,
	title = {Impact of Programming Language Fragmentation on Developer Productivity},
	journal = {International Journal of Open Source Software and Processes},
	volume = {2},
	year = {2010},
	month = {32/2010},
	pages = {41 - 61},
	abstract = {Programmers often develop software in multiple languages. In an effort to study the effects of programming language fragmentation on productivity{\textemdash}and ultimately on a developer{\textquoteright}s problem-solving abilities{\textemdash}the authors present a metric, language entropy, for characterizing the distribution of a developer{\textquoteright}s programming efforts across multiple programming languages. This paper presents an observational study examining the project contributions of a random sample of 500 SourceForge developers. Using a random coefficients model, the authors find a statistically (alpha level of 0.001) and practically significant correlation between language entropy and the size of monthly project contributions. Results indicate that programming language fragmentation is negatively related to the total amount of code contributed by developers within SourceForge, an open source software (OSS) community.},
	keywords = {commits, entropy, language entropy, programming languages, sourceforge, srda},
	issn = {1942-3934},
	doi = {10.4018/jossp.2010040104},
	author = {Krein, Jonathan L. and MacLean, Alexander C. and Knutson, Charles D. and Delorey, Daniel P. and Eggett, Dennis L.}
}
@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 {1204,
	title = {Language entropy: A metric for characterization of author programming language distribution},
	booktitle = {4th Workshop on Public Data about Software Development (WoPDaSD 2009)},
	year = {2009},
	note = {The data set used in this study was previously collected for a separate, but related work. It was originally extracted from the SourceForge Research Archive (SFRA), August 2006. For a detailed discussion of the data source, collection tools and processes, and summary statistics, see [6]."

"From the initial data set we extracted a random sample of 500 developers3 along with descriptive details of all revisions that those developers made since the inception of the projects on which they worked. We then condensed this sample by totaling the lines of code added by each developer for each month in which that developer made at least one code submission."
[6] Daniel P. Delorey, Charles D. Knutson, and Alex MacLean. Studying production phase sourceforge projects: A case study using cvs2mysql and sfra+. In Second International Workshop on Public Data about Software Development (WoPDaSD {\textquoteright}07), June 2007.},
	month = {2009},
	abstract = {Programmers are often required to develop in multiple languages. In an effort to study the effects of programming language fragmentation on productivity{\textemdash}and ultimately on a programmer{\textquoteright}s problem solving abilities{\textemdash}we propose a metric, language entropy, for characterizing the distribution of an individual{\textquoteright}s development efforts across multiple programming languages. To evaluate this metric, we present an observational study examining all project contributions (through August 2006) of a random sample of 500 SourceForge developers. Using a random coefficients model, we found a statistically significant correlation (alpha level of 0.05) between language entropy and the size of monthly pro ject contributions (measured in lines of code added). Our results indicate that language entropy is a good candidate for characterizing author programing language distribution.},
	keywords = {contributions, developers, language entropy, lines of code, loc, multiple languages, programming languages, sourceforge},
	attachments = {https://flosshub.org/sites/flosshub.org/files/LanguageEntropy-JonathanKrein.pdf},
	author = {Krein, Jonathan L. and MacLean, Alexander C. and Delorey, Daniel P. and Knutson, Charles D. and Eggett, Dennis L.}
}
