Worldwide Gender Differences in Public Code Contributions - Replication Package
Creators
- 1. University of Bologna, Italy
- 2. LTCI, Télécom Paris, Institut Polytechnique de Paris, France
Description
Worldwide Gender Differences in Public Code Contributions - Replication Package
This document describes how to replicate the findings of the paper: Davide Rossi and Stefano Zacchiroli, 2022, Worldwide Gender Differences in Public Code Contributions. 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.
Prerequisites
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.
It is advisable to create a Python virtual environment and install the following PyPI packages: 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
Initial data
swh-replica
, 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/.
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, 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 frequencyzones.acc.tab
- countries/territories, timezones, population and world zonesc_c.tab
- ccTDL entities - world zones matches
Data preparation
- Export data from the
swh-replica
database to createcommits.csv.zst
andauthors.csv.zst
sh> ./export.sh
- Run the authors cleanup script to create
authors--clean.csv.zst
sh> ./cleanup.sh authors.csv.zst
- Filter out implausible names and create
authors--plausible.csv.zst
sh> pv authors--clean.csv.zst | unzstd | ./filter_names.py 2> authors--plausible.csv.log | zstdmt > authors--plausible.csv.zst
Gender detection
- Run the gender guessing script to create
author-fullnames-gender.csv.zst
sh> pv authors--plausible.csv.zst | unzstd | ./guess_gender.py --fullname --field 2 | zstdmt > author-fullnames-gender.csv.zst
Database creation and data ingestion
-
Create the PostgreSQL DB
sh> createdb gender-commit
Notice that from now on when prepending thepsql>
prompt we assume the execution of psql on thegender-commit
database. -
Import data into PostgreSQL DB
sh> ./import_data.sh
Zone detection
- Extract commits data from the DB and create
commits.tab
, that is used as input for the gender detection script
sh> psql -f extract_commits.sql gender-commit
- Run the world zone detection script to create
commit_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 --help
if you are interested in changing the script parameters. - Read zones assignment data from the file into the DB
psql> \copy commit_culture 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 creates
commits_tz.tab
,authors_tz.tab
,commits_zones.tab
,authors_zones.tab
, andauthors_zones_1620.tab
.
Editextract_data.sql
if 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. This will generate
commits_tzs.pdf
,authors_tzs.pdf
,commits_zones.pdf
,authors_zones.pdf
, andauthors_zones_1620.pdf
.sh> ./create_charts.sh
Additional graphs
This package also includes some already-made graphs
authors_zones_1.pdf
: stacked graphs showing the ratio of female authors per world zone through the years, considering all authors with at least one commit per periodauthors_zones_2.pdf
: ditto with at least two commits per periodauthors_zones_10.pdf
: ditto with at least ten commits per period
Files
README.md
Additional details
Related works
- Is supplement to
- Conference paper: 10.1145/3510458.3513011 (DOI)