Identifying Files and Workflows Contributing to Technical Debt in GitHub Repositories Using Data Mining and Natural Language Processing Techniques

Features

• Data Fetching from GitHub Actions Workflows
  Efficiently fetch data from GitHub Actions workflows for further analysis.

• Data Cleaning
  Clean and preprocess the fetched data to ensure consistency and accuracy for downstream tasks.

• Automatic Text Classification with NLP
  Leverage a pre-trained NLP model to automatically classify text,as TD and Not_TD instances

• Technical Debt (TD) Visualization
  To visualize technical debt, we have generated insightful plots using popular Python libraries such as Matplotlib, Pandas, and Seaborn. These visualizations help to better understand the distribution and impact of technical debt across different aspects of the project.

Installation

Prerequisites

Ensure that you have the following installed:

• Python 3.8+

• pip

Clone the repository

First, clone the project to your local machine:

git clone https://github.com/Aqila-Farahmand/MasterThesis
cd your-repository

Create a virtual environment (optional but recommended)

python -m venv venv
source venv/bin/activate    # For Linux/macOS
venv\Scripts\activate       # For Windows

Install the dependencies

Use pip to install all the required packages from requirements.txt:

pip install -r requirements.txt

Usage

Running the Project

After installing the dependencies, you can run the project with:

• Fetching Data
  To fetch the required data, run the following command:

python -m data_fetching.__main__

• Data Cleaning For data cleaning, you can use Google Colab or Jupyter Notebook. Import the file from data_cleaning/clean_data.ipynb and run the code there.

• Text Classification The text classification process uses an NLP model trained on a large dataset from GitHub issues. Due to GitHub's large file size limitations, only the inference code is provided in this repository.

• Technical Debt (TD) Visualization
  You can generate simple plots to visualize your data using the script located at data_visualization/workflow_analysis.py. Simply run the file to create visualizations based on your dataset.

Configuration

To fetch data, you'll need to configure authentication for making GitHub API requests.

1. Generate a Personal Access Token (GITHUB_TOKEN) for authenticated API requests.

2. Set the GITHUB_TOKEN as an environment variable:

   env:
     GITHUB_TOKEN: ${{your_github_token}}
   
   

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Steps to contribute:

• Fork the repository
• Create a new branch (git checkout -b feature-branch)
• Commit your changes (git commit -m 'Add new feature')
• Push to the branch (git push origin feature-branch)
• Create a new pull request

License

Apache 2.0