repo-analysis

• Extract metadata of pull requests related to a GitHub repo (pointed at by the environment variable REPO_NAME or defined by the command-line --repo flag) in the form "<owner>/<repo>".
• If MAIN_BRANCH is set, it will be used as the main branch to compare against. Otherwise, the default is main.
• You will need to set the environment variable GITHUB_TOKEN to your GitHub Personal Access Token (PAT).
• This token should have access to the repository you are trying to analyze.
• The script will fetch all open pull requests, analyze them, and visualize the results.
• The results will be saved to a CSV file if the --csv <filename> flag is provided.

If you are in a corporate environment, the script is proxy-aware. Set the environment variables HTTP_PROXY and HTTPS_PROXY appropriately and the script will take them into account.

Instructions:

1. Install the required libraries:

   pip install -r requirements.txt
   

   Or using an explicit proxy setting like:

   pip install -r requirements.txt --proxy http://[user:passwd@]proxy.server:port
   

2. Update the .env file:

   Ensure your account/token has the repo scope and appropriate access to the repository (eg: it is fine for the repo to be private - as long as your account/token has access to it).

   GITHUB_TOKEN=your_github_token
   REPO_NAME=owner/repo_name
   MAIN_BRANCH=main  # Replace with the name of your main branch if different
   

   Proxy Servers: If you have problems with corporate proxy servers, you might add:

   HTTP_PROXY=http://username:password@proxy.company.com:port
   HTTPS_PROXY=http://username:password@proxy.company.com:port
   

3. Run the script:

   • For example: python main.py --repo x/y --branch z --csv pull_requests.csv
   • The script will save the pull request data from a repo called y under GitHub owner xto a CSV file named pull_requests.csv in the same directory. It will compare how far behind each pull-request is from branch z.

Additional Metrics:

• Lines of Code Changed: Sum of additions and deletions (additions + deletions). Larger changes may indicate higher complexity. Number of Changed Files:

• Number of Comments: Sum of comments and review_comments. More comments may indicate more discussion or contention. Time Open:

• Calculate the duration between created_at and closed_at (or the current date if still open). Longer durations may indicate complexity or delays.

• Merge Status: Whether the pull request is merged (merged_at is not None). Labels:

• Analyze labels (e.g., "bug", "enhancement", "critical") to prioritize based on importance.

• Commits Behind/Ahead Main: Already included in the previous script. Larger numbers may indicate more divergence from the main branch.

• Author Activity: Number of pull requests created by the author (to identify experienced contributors).

Notes:

The script fetches all pull requests (open, closed, and merged) and includes metadata like labels, comments, and file changes.

Proxy Servers:

If you have problems with corporate proxy servers, you might try (With set in Windows, export in Linux/Bash):

set HTTP_PROXY=http://username:password@proxy.company.com:port
set HTTPS_PROXY=http://username:password@proxy.company.com:port

Or use an explict proxy setting like:

pip install -r requirements.txt --proxy http://[user:passwd@]proxy.server:port