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 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==8.0.4
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 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 populate swh-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 the swh-replica database to create commits.csv.zst and authors.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

Zone detection by email

Run the email detection script to create author-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 the psql> prompt we assume the execution of psql on the zones-commit database.
Import data into PostgreSQL DB
```
sh> ./import_data.sh
```

Zone detection by name

Extract commits data from the DB and create commits.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 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.

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 creates commit_zones_7120.tab, author_zones_7120_t5.tab, commit_zones_7120.grid and author_zones_7120_t5.grid.
Edit extract_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.

sh> ./create_stackedbar_chart.py -w 20 -s 1971 -f commit_zones_7120.grid -f author_zones_7120_t5.grid -o chart.pdf