samwalkow / diversity_and_open_source_software_sustainability Goto Github PK

• List keywords to use for search

• Find articles to review (5-10)

• Form 1-2 general hypothesis

• Working hypothesis: long term projects will have the same or little change over in to project maintainers/contributors. Minority contributors will experience a steeper attrition rate.

• Using Pandas

• Using Dask

• Pull out usernames

• Find a way to pull them apart into actual names

• Filter out anonymous usernames

• record steps for data and environment set up
• add links to all materials (readme?)

Project Road Map

This issue is the project roadmap.

This project is an investigation into open source software development activities and sustainability. The project will examine software project development over time though the lens of diversity by looking at contributor attrition rates in conjunction with their demographics.

The project will have two phases: investigating the lifecycle of open source software and the number of diverse contributors to the software. Exploring the intersection of these two elements will offer insight into what makes open source software sustainable (or not) in the long run, and the nature of the role an inclusive community plays in that sustainability. This will be done using github archive data. Operating in a way that facilitates openness is also a goal and will take a different form at every milestone.

See the milestones below:

Milestone 1: Step Up

This milestone includes setting up a computing environment, retrieving the data, and formulating a hypothesis.

Actions include:

• a literature review
• set up a virtual environment with necessary software
• downloading from data source

Open Practices:

• record steps taken to access the data set and environment needed to analyze data
• have links to all necessary materials and tools
• post all to github project

Milestone 2: Data Exploration

This milestone includes reading in data, processing the data into the necessary format, data cleaning, and understanding the data well as possible.

Actions include:

• Reading in the data
• Reformatting and cleaning the data
• Data visualization

Open Practices:

• Push notebook to this project
• record code, comments, and other details to notebook

Milestone 3: Data Analysis

This milestone includes creating a data analysis plan, running analysis on the data, and summarizing results.

Actions include:

• deciding on analysis techniques
• executing data analysis on data
• writing summary report

Open Practices:

• Push notebook to this project
• record code, comments, and other details to notebook

Project board:
https://github.com/samwalkow/Diversity_and_Open_Source_Software_Sustainability/projects/1

• Get personal github data

• Submitted pull request

• Follow-up with email

• Reformat individual tables

• Parse strings into tables

• Find missing headers

• Link tables together

• Download data onto node

• Set up computing environment

• will use a jupyter notebook on virtual environment

• use Pandas?