Overview

This research artifact accompanies our ICSE 2019 paper "Going Farther Together: The Impact of Social Capital on Sustained Participation in Open Source". If you use the artifact, please consider citing:

@inproceedings{QiuNBSV19,
  author = {Qiu, Huilian Sophie and 
  		Nolte, Alexander and 
  		Brown, Anita and 
  		Serebrenik, Alexander and 
  		Vasilescu, Bogdan},
  title = {Going Farther Together: The Impact of Social Capital on Sustained Participation in Open Source},
  booktitle = {Proceedings of the 41st International Conference on Software Engineering (ICSE) 2019, Montreal, Canada},
  note = {to appear},
  organization = {IEEE},
  year = {2019},
}

The artifact consists of three main parts:

1. Data collection scripts, written in Python.

   The code can be used to select open source contributors, collect their GitHub projects, gather data such as contributors’ years of experience on GitHub, and projects’ age and size. The code also calculates social capital measures, including team familiarity, recurring cohesion, and heterogeneity of programming language expertise.

   The final output is a csv file, each row of which is a data point used in our survival analysis. Each row consists of information per person per project per quarter (three-month time window), including all the social capital measures.

   The code was implemented in Python 2 and tested on a Linux machine. Required dependencies: pymysql, sqlalchemy, numpy, scipy, sklearn, and pandas. You also need to have (access to) a MySQL dump of GHTorrent.

2. The survey instrument used in the paper.

3. Survey analysis scripts, written in R.

We give more details on data collection scripts next.

Data Collection

Tables in the MySQL database:

In addition to the standard GHTorrent tables, we created a table ght_namsor_s, containing inferred gender data kindly provided by NamSor (thanks, Elian Carsenat!)

How to run the code:

1. Use MySQL_queries/filter_valid_users to find valid users.

2. Run sample_user.py to construct a balanced sample of male and female contributors. The result is saved in data/uid.list. In order to obtain a sample with equal number of men and women, sample_user.py calls our gender classifier to determine users' genders. The code for the gender classifier is stored in the gender/ folder. Details about these files are in the following section.

3. Run setup.py, which reads the files dict/alias_map_b.dict, dict/reverse_alias_map_b.dict, and data/uid.list, and generates files data/pid.list, data/all_contributors.list, data/watchers_monthly_counts_win.csv, dict/contr_projs.dict, data/all_projs.list, and dict/proj_contrs_count.dict.

4. Run get_user_info.py, get_proj_info.py, and get_user_proj_info.py. They write to data/results_users.csv, data/results_proj.csv, and data/results_user_proj.csv repectively.

5. Run merge_result.py to combine these tables. The result will be saved in data/proj_user_proj.csv, which will be used for data analysis.

To determine gender:

Our gender classifier uses names' n-grams as well as results from two other existing gender classifiers, NamSor and genderComputer as features.

In the gender/ folder are two Python files that demostrate how our gender classifer works. First, get_feature.py reads users' information from the MySQL ght_namsor_s table, which contains users' combined data from GHTorrent and origin and gender information obtained from NamSor. Then it gets classification results from genderComputer. To get a better result from genderComputer, we need to know the user's country. For this, we use the data provided by Namsor on one's origin, computed based on their names.

There are other gender classifiers one can use, e.g., genderize.io. To use them, simply make the result a new feature in the model.

In determine_gender.py, our classifier divides the name into n-grams and uses them as additional features. The result will be written to data/gender.csv, which will later be used in sample_user.py for balance sampling as described above.

Data analysis

Survey

The survey analysis script is survey.R. It contains code to calculate reliability measures, correlations, plots, and conduct logistic regression analysis on the collected survey data.

Survival analysis

The models as reported in the paper are created by the survival_analysis.R R script. We have also included an annonymous version of data we used for this paper here. Each row in the csv file is one data point in our model. It represents one user's activity in one project during one three-month window. The csv file consists of 34 columns. Those with prefix "u_" are information about users and those start with "p_" are about projects:

• u_age is the number of three-month windows since the user's first activity.
• u_commits_to_date is the number of commits made by this user across all projects up to that three-month window.
• u_email is the md5 hash of the user's email address.
• u_follower is the number of followers the user had up to that three-month window.
• u_gender is the user's gender.
• u_id is the id of the user in the GHTorrent dataset users table.
• u_login is the md5 has of the user's login.
• u_nichewidth is the number of programming languages that the user had used up to that three-month window.
• u_projects_to_date is the number of projects to which the user had submitted commits up to that three-month window.
• u_temp_failure is a binary indicator of whether the user had been inactive for half a year (2 three-month windows).
• u_temp_failure_1_year is a binary indicator of whether the user had been inactive for a year (4 three-month windows).
• u_window_active_to_date is the number of three-month windows during which the user had submitted commits.
• window_num represents the current three-month window. 2008 Jan to 2008 Mar will be window_num = 1.
• owner_company is a binary indicator of whether the owner of the repository displays their company in their profile.
• owner_gender is the repository owners' genders, with -1 representing male, 1 female, and 0 unknown.
• p_id is the id of the project in the GHTorrent dataset projects table.
• u_is_major is a binary indicator of whether the user is a major contributor (more than 5% commits) to that project.
• u_is_owner is a binary indicator of whether the user is the owner of that project.
• u_pr_merge is a binary indicator of whether the user can merge pull request in that project.
• p_age is the number of three-month windows since the creation of that project.
• p_div_langdenom is the value of the programming language diversity of that project p_id in that window window_num.
• p_fam_no_decay is the value of the team familiarity of that project in that window.
• p_lang is the major programming language of that project.
• p_num_commits is the number of commits of that project in that window.
• p_num_commits_to_date is the total number of commits of that project since is creation.
• p_num_stars is the project's number of stars in that window.
• p_num_users_to_date is the number of users who had sent commits to the project up to that window.
• p_owner is the project owner's id in GHTorrent dataset users table.
• p_recurring_co is the value of the recurring cohesion of that project in that window.
• p_sharenewcomers is the percentage of new GitHub users out of the users who had sent commits to that project.
• p_sharenewcomers is the percentage of new users to that project out of the users who had sent commits to that project.
• p_team_size
• p_windows_active_to_date is the number of three-month windows that the project received commits.
• window is the date format of three-month window.

License

CC0 1.0