mahyarsamani / ecs260-final-project

SPACE Project

Installation

virtualenv -p python3 venv
source venv/bin/activate

Intro

In order for us to create a model for productivity we need to gather the following information for each aspect of SPACE.

• Performance: Number of bugs (number of issues)
• Activity: Number of pull requests (open and close)
• Communication: Number of pull requests

As a baseline we will use the number of commits to measure productivity:

• We can easily get this with pydriller.

The page limit for the progress report.

Plan

We aim to develop a linear model to predict productivity. This model has five preditctors (at this stage). These predictors are listed as follows:

• Number of bugs reported
• Number of pull requests opened per month
• Number of pull requests closed per month
• Number of merged pull requests
• Number of unmerged pull requests

These predictors will predict an outcome which is the number of commits.

Division of labor:

• Agnieszka and Mahyar will be responsible for extracting information.

1. Agnieszka will gather information about pull requests
2. Mahyar will gather information about issues (bugs).
3. Deliverables: python module that does these

• Alicia and Veronica will work on developing a model:

1. Developing a linear model using data analysis modules in python
2. Create dummy data for your model and develop that model
3. Deliverable: Jupyter Notebook to create model and plot graphs

Note

Never push your code to master branch, rather create a branch with your name (just a suggestion) and then create a PR.

Datasets

For each project we gather the follwoing data:

• developer_id
• time_window
• issue_opened
• issue_closed
• pr_opened
• pr_closed
• pr_merged
• commits

Plots list & Tables

1. Statistics for the selected GitHub projects. (Language, issues, PRs, commits)
2. Statistics on the metrics related to productivity over a period of 12 time windows: mean, st. dev, min, median, max for: issue_opened, issue_closed, pr_opened, pr_closed, pr_merged, commits.
3. Multilinear regression summary table for all repos
4. Maybe include linear regression summaries and plots (?)
5. Results