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\section{Introduction} |
\label{sec:intro} |
\emph{Gender diversity}, or more often its lack thereof, among participants to |
software development activities has been thoroughly studied in recent years. In |
particular, the presence of, effects of, and countermeasures for \emph{gender |
bias} in Free/Open Source Software (FOSS) have received a lot of attention |
over the past decade~\cite{david2008fossdevs, qiu2010kdewomen, |
nafus2012patches, kuechler2012genderfoss, vasilescu2014gender, |
oneil2016debiansurvey, robles2016womeninfoss, terrell2017gender, |
zacchiroli2021gender}. \emph{Geographic diversity} is on the other hand the |
kind of diversity that stems from participants in some global activity coming |
from different world regions and cultures. |
Geographic diversity in FOSS has received relatively little attention in scholarly |
works. In particular, while seminal survey-based and |
point-in-time medium-scale studies of the geographic origins of FOSS |
contributors exist~\cite{ghosh2005understanding, david2008fossdevs, |
barahona2008geodiversity, takhteyev2010ossgeography, robles2014surveydataset, |
wachs2021ossgeography}, large-scale longitudinal studies of the geographic |
origin of FOSS contributors are still lacking. Such a quantitative |
characterization would be useful to inform decisions related to global |
development teams~\cite{herbsleb2007globalsweng} and hiring strategies in the |
information technology (IT) market, as well as contribute factual information |
to the debates on the economic impact and sociology of FOSS around the world. |
\paragraph{Contributions} |
With this work we contribute to close this gap by conducting \textbf{the first |
longitudinal study of the geographic origin of contributors to public code |
over 50 years.} Specifically, we provide a preliminary answer to the |
following research question: |
\begin{researchquestion} |
From which world regions do authors of publicly available commits come from |
and how has it changed over the past 50 years? |
\label{rq:geodiversity} |
\end{researchquestion} |
We use as dataset the \SWH/ archive~\cite{swhipres2017} and analyze from it |
2.2 billion\xspace commits archived from 160 million\xspace projects and authored by |
43 million\xspace authors during the 1971--2021 time period. |
We geolocate developers to |
\DATAWorldRegions/ world regions, using as signals email country code top-level domains (ccTLDs) and |
author (first/last) names compared with name distributions around the world, and UTC offsets |
mined from commit metadata. |
We find evidence of the early dominance of North America in open source |
software, later joined by Europe. After that period, the geographic diversity |
in public code has been constantly increasing. |
We also identify relevant historical shifts |
related to the end of the UNIX wars and the increase of coding literacy in |
Central and South Asia, as well as of broader phenomena like colonialism and |
people movement across countries (immigration/emigration). |
\paragraph{Data availability.} |
A replication package for this paper is available from Zenodo at |
\url{https://doi.org/10.5281/zenodo.6390355}~\cite{replication-package}. |
\section{Related Work} |
\label{sec:related} |
Both early and recent works~\cite{ghosh2005understanding, david2008fossdevs, |
robles2014surveydataset, oneil2016debiansurvey} have characterized the |
geography of Free/Open Source Software (FOSS) using \emph{developer surveys}, |
which provide high-quality answers but are limited in size (2-5\,K developers) |
and can be biased by participant sampling. |
In 2008 Barahona et al.~\cite{barahona2008geodiversity} conducted a seminal |
large-scale (for the time) study on FOSS \emph{geography using mining software |
repositories (MSR) techniques}. They analyzed the origin of 1\,M contributors |
using the SourceForge user database and mailing list archives over the |
1999--2005 period, using as signals information similar to ours: email domains |
and UTC offsets. |
The studied period (7 years) in~\cite{barahona2008geodiversity} is shorter than |
what is studied in the present paper (50 years) and the data sources are |
largely different; with that in mind, our results show a slightly larger quote of |
European v.~North American contributions. |
Another empirical work from 2010 by Takhteyev and |
Hilts~\cite{takhteyev2010ossgeography} harvested self-declared geographic |
locations of GitHub accounts recursively following their connections, |
collecting information for $\approx$\,70\,K GitHub users. A very recent |
work~\cite{wachs2021ossgeography} by Wachs et al.~has geolocated half a million |
GitHub users, having contributed at least 100 commits each, and who |
self-declare locations on their GitHub profiles. While the study is |
point-in-time as of 2021, the authors compare their findings |
against~\cite{barahona2008geodiversity, takhteyev2010ossgeography} to |
characterize the evolution of FOSS geography over the time snapshots taken by |
the three studies. |
Compared with previous empirical works, our study is much larger scale---having |
analyzed 43 million\xspace authors of 2.2 billion\xspace commits from 160 million\xspace |
projects---longitudinal over 50 years of public code contributions rather than |
point in time, and also more fine-grained (with year-by-year granularity over |
the observed period). Methodologically, our study relies on Version Control |
End of preview. Expand
in Dataset Viewer.
TokenMonster Datasets: English, Code, Fiction, Non-fiction
Included are datasets that were used to generate the TokenMonster pre-built vocabularies. All are raw text files.
The training data mostly came from Red Pajamas 1B Token Sample. However, to reduce formal English and emphasize other languages, informal writing and code, c4_sample & cc_sample were cropped to 100MB, and Reddit conversations data were added (also cropped to 100MB.)
Additionally, equally weighted code samples of 2MB per language (code_2mb) and 10MB per language (code_10mb) were added for 30 different programming languages to ensure all programming languages have representation. The source of the code samples was codeparrot/github-code. To ensure a range of coding styles, I allowed only 1 file per GitHub repository, and per file a maximum of 200 lines selected from the middle of the file.
Given the evolving nature of writing styles, I felt that book_sample.txt, which consists of out-of-copyright books, was not a good representation of contemporary fiction. To better represent a more modern style, I curated fiction.txt and fiction_100mb.txt by throwing together a few other datasets and cleaning it up.
| Filename | Filesize |
|---|---|
| arxiv_sample.txt | 88,925,569 |
| book_sample.txt | 108,069,616 |
| c4_sample.txt | 100,560,318 |
| cc_2023-06_sample.txt | 100,852,231 |
| code_2mb.txt | 62,895,904 |
| code_10mb.txt | 314,006,799 |
| fiction.txt | 357,119,086 |
| fiction_100mb.txt | 94,235,489 |
| github_sample.txt | 191,123,094 |
| stackexchange_sample.txt | 71,940,138 |
| wikipedia_sample.txt | 79,181,873 |
| reddit.txt | 100,027,565 |
Note: fiction_100mb.txt is a subset of fiction.txt, and code_2mb.txt is a subset of code_10mb.txt.
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