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A Python script for analyzing pull request comments, by pulling data from GHTorrent and GitHub, and identifying code comprehension related features.

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Code Comprehension Classifier for Pull Request Comments (CCC4PRC)

A machine learning classifier that identifies code comprehension related comments in a pull request.

Set up

  1. Install git-lfs: https://github.com/git-lfs/git-lfs/wiki/Installation
  2. Initialize Git LFS and its respective hooks in this repo:
git lfs install

On macOS and Linux:

python3 -m venv .venv
source .venv/bin/activate

On Windows:

python -m venv .venv
.venv\Scripts\Activate.ps1

Install dependent packages

pip install -r requirements.txt

Download NLTK corpora

In the Python console, launch the NLTK Downloader:

import nltk
nltk.download()

Download the following corpora:

  • nps_chat

Configure Confuse

By default, Visual Studio Code will use .env automatically when starting the Python environment, so add the following to the end of .env (create the file if it does not exist):

# Confuse Config Search Directory
CCC4PRCDIR="./"

Alternatively, if not using Visual Studio Code, please see the instructions below.

On macOS and Linux

  1. Add the following to the end of venv activate script:
# Confuse Config Search Directory
CCC4PRCDIR="./"
export CCC4PRCDIR
  1. Then reactivate the venv to take effect:
source .venv/bin/activate

On Windows

  1. Add the following to the end of Activate.ps1:
# Confuse Config Search Directory
$env:CCC4PRCDIR="./"
  1. Then reactivate the venv to take effect:
.venv\Scripts\Activate.ps1

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A Python script for analyzing pull request comments, by pulling data from GHTorrent and GitHub, and identifying code comprehension related features.

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