Worldwide Gender Differences in Public Code Contributions - Replication Package

<strong>Worldwide Gender Differences in Public Code Contributions - Replication Package</strong> This document describes how to replicate the findings of the paper: Davide Rossi and Stefano Zacchiroli, 2022, <em>Worldwide Gender Differences in Public Code Contributions</em>. In Software Engineering in Society (ICSE-SEIS'22), May 21-29, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3510458.3513011 This document comes with the software needed to mine and analyze the data presented in the paper. <strong>Prerequisites</strong> These instructions assume the use of the bash shell, the Python programming language, the PosgreSQL DBMS (version 11 or later), the zstd compression utility and various usual *nix shell utilities (cat, pv, ...), all of which are available for multiple architectures and OSs.<br> It is advisable to create a Python virtual environment and install the following PyPI packages: <code>click==8.0.3 cycler==0.10.0 gender-guesser==0.4.0 kiwisolver==1.3.2 matplotlib==3.4.3 numpy==1.21.3 pandas==1.3.4 patsy==0.5.2 Pillow==8.4.0 pyparsing==2.4.7 python-dateutil==2.8.2 pytz==2021.3 scipy==1.7.1 six==1.16.0 statsmodels==0.13.0</code> <strong>Initial data</strong> <code>swh-replica</code>, a PostgreSQL database containing a copy of Software Heritage data. The schema for the database is available at https://forge.softwareheritage.org/source/swh-storage/browse/master/swh/storage/sql/.<br> We retrieved these data from Software 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, <em>The Software Heritage Graph Dataset: Public software development under one roof</em>. 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.<br> Once retrieved, the data can be loaded in PostgreSQL to populate <code>swh-replica</code>. <code>names.tab</code> - forenames and surnames per country with their frequency <code>zones.acc.tab</code> - countries/territories, timezones, population and world zones <code>c_c.tab</code> - ccTDL entities - world zones matches <strong>Data preparation</strong> Export data from the <code>swh-replica</code> database to create <code>commits.csv.zst</code> and <code>authors.csv.zst</code> <code>sh&gt; ./export.sh</code> Run the authors cleanup script to create <code>authors--clean.csv.zst</code> <code>sh&gt; ./cleanup.sh authors.csv.zst</code> Filter out implausible names and create <code>authors--plausible.csv.zst</code> <code>sh&gt; pv authors--clean.csv.zst | unzstd | ./filter_names.py 2&gt; authors--plausible.csv.log | zstdmt &gt; authors--plausible.csv.zst</code> <strong>Gender detection</strong> Run the gender guessing script to create <code>author-fullnames-gender.csv.zst</code> <code>sh&gt; pv authors--plausible.csv.zst | unzstd | ./guess_gender.py --fullname --field 2 | zstdmt &gt; author-fullnames-gender.csv.zst</code> <strong>Database creation and data ingestion</strong> Create the PostgreSQL DB <code>sh&gt; createdb gender-commit </code>Notice that from now on when prepending the <code>psql&gt;</code> prompt we assume the execution of psql on the <code>gender-commit</code> database. Import data into PostgreSQL DB <code>sh&gt; ./import_data.sh</code> <strong>Zone detection</strong> Extract commits data from the DB and create <code>commits.tab</code>, that is used as input for the gender detection script<br> <code>sh&gt; psql -f extract_commits.sql gender-commit</code> Run the world zone detection script to create <code>commit_zones.tab.zst</code> <code>sh&gt; pv commits.tab | ./assign_world_zone.py -a -n names.tab -p zones.acc.tab -x -w 8 | zstdmt &gt; commit_zones.tab.zst </code>Use <code>./assign_world_zone.py --help</code> if you are interested in changing the script parameters. Read zones assignment data from the file into the DB<br> <code>psql&gt; \copy commit_culture from program 'zstdcat commit_zones.tab.zst | cut -f1,6 | grep -Ev ''\s$'''</code> <strong>Extraction and graphs</strong> Run the script to execute the queries to extract the data to plot from the DB. This creates <code>commits_tz.tab</code>, <code>authors_tz.tab</code>, <code>commits_zones.tab</code>, <code>authors_zones.tab</code>, and <code>authors_zones_1620.tab</code>.<br> Edit <code>extract_data.sql</code> if you whish to modify extraction parameters (start/end year, sampling, ...). <code>sh&gt; ./extract_data.sh</code> Run the script to create the graphs from all the previously extracted tabfiles. This will generate <code>commits_tzs.pdf</code>, <code>authors_tzs.pdf</code>, <code>commits_zones.pdf</code>, <code>authors_zones.pdf</code>, and <code>authors_zones_1620.pdf</code>. <code>sh&gt; ./create_charts.sh</code> <strong>Additional graphs</strong> This package also includes some already-made graphs <code>authors_zones_1.pdf</code>: stacked graphs showing the ratio of female authors per world zone through the years, considering all authors with at least one commit per period <code>authors_zones_2.pdf</code>: ditto with at least two commits per period <code>authors_zones_10.pdf</code>: ditto with at least ten commits per period

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