Geographic Diversity in Public Code Contributions — Replication Package
DOI10.5281/zenodo.6390355Zenodo6390355MaRDI QIDQ6716133
Dataset published at Zenodo repository.
Author name not available (Why is that?)
Publication date: 28 March 2022
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Geographic Diversity in Public Code Contributions - Replication Package This document describes how to replicate the findings of the paper: Davide Rossi and Stefano Zacchiroli, 2022,Geographic Diversity in Public Code Contributions - An Exploratory Large-Scale Study Over 50 Years. In 19th International Conference on Mining Software Repositories (MSR 22), May 23-24, Pittsburgh, PA, USA. ACM, New York, NY, USA, 5 pages.https://doi.org/10.1145/3524842.3528471 This document comes with the software needed to mine and analyze the data presented in the paper. Prerequisites These instructions assume the use of thebashshell, thePythonprogramming language, thePosgreSQLDBMS (version 11 or later), thezstdcompression utility and various usual *nix shell utilities (cat, pv, ), all of which are available for multiple architectures and OSs. It is advisable to create aPython virtual environmentand install the following PyPI packages: click==8.0.4 cycler==0.11.0 fonttools==4.31.2 kiwisolver==1.4.0 matplotlib==3.5.1 numpy==1.22.3 packaging==21.3 pandas==1.4.1 patsy==0.5.2 Pillow==9.0.1 pyparsing==3.0.7 python-dateutil==2.8.2 pytz==2022.1 scipy==1.8.0 six==1.16.0 statsmodels==0.13.2 Initial data swh-replica, a PostgreSQL database containing a copy of Software Heritage data. The schema for the database is available athttps://forge.softwareheritage.org/source/swh-storage/browse/master/swh/storage/sql/. We retrieved these data fromSoftware Heritage, in collaboration with the archive operators, taking an archive snapshot as of 2021-07-07. We cannot make these data available in full as part of the replication package due to both its volume and the presence in it of personal information such as user email addresses. However, equivalent data (stripped of email addresses) can be obtained from the Software Heritage archive dataset, as documented in the article: Antoine Pietri, Diomidis Spinellis, Stefano Zacchiroli,The Software Heritage Graph Dataset: Public software development under one roof. In proceedings of MSR 2019: The 16th International Conference on Mining Software Repositories, May 2019, Montreal, Canada. Pages 138-142, IEEE 2019.http://dx.doi.org/10.1109/MSR.2019.00030. Once retrieved, the data can be loaded in PostgreSQL to populateswh-replica. names.tab- forenames and surnames per country with their frequency zones.acc.tab- countries/territories, timezones, population and world zones c_c.tab- ccTDL entities - world zones matches Data preparation Export data from theswh-replicadatabase to createcommits.csv.zstandauthors.csv.zst sh ./export.sh Run the authors cleanup script to createauthors--clean.csv.zst sh ./cleanup.sh authors.csv.zst Filter out implausible names and createauthors--plausible.csv.zst sh pv authors--clean.csv.zst | unzstd | ./filter_names.py 2 authors--plausible.csv.log | zstdmt authors--plausible.csv.zst Zone detection by email Run the email detection script to createauthor-country-by-email.tab.zst sh pv authors--plausible.csv.zst | zstdcat | ./guess_country_by_email.py -f 3 2 author-country-by-email.csv.log | zstdmt author-country-by-email.tab.zst Database creation and initial data ingestion Create the PostgreSQL DB sh createdb zones-commit Notice that from now on when prepending thepsqlprompt we assume the execution of psql on thezones-commitdatabase. Import data into PostgreSQL DB sh ./import_data.sh Zone detection by name Extract commits data from the DB and createcommits.tab, that is used as input for the zone detection script sh psql -f extract_commits.sql zones-commit Run the world zone detection script to createcommit_zones.tab.zst sh pv commits.tab | ./assign_world_zone.py -a -n names.tab -p zones.acc.tab -x -w 8 | zstdmt commit_zones.tab.zst Use./assign_world_zone.py --helpif you are interested in changing the script parameters. Ingest zones assignment data into the DB psql \copy commit_zone from program 'zstdcat commit_zones.tab.zst | cut -f1,6 | grep -Ev \s$' Extraction and graphs Run the script to execute the queries to extract the data to plot from the DB. This createscommit_zones_7120.tab,author_zones_7120_t5.tab,commit_zones_7120.gridandauthor_zones_7120_t5.grid. Editextract_data.sqlif you whish to modify extraction parameters (start/end year, sampling, ). sh ./extract_data.sh Run the script to create the graphs from all the previously extracted tabfiles. sh ./create_stackedbar_chart.py -w 20 -s 1971 -f commit_zones_7120.grid -f author_zones_7120_t5.grid -o chart.pdf
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