hephai-pr-doctor

HEPHAI-PR-doctor

Overview

HEPHAI-PR-doctor is a GitHub Action that provides AI-powered PR reviews and automated test generation for Python & JavaScript. It leverages OpenAI to analyze pull requests, generate unit tests, and provide actionable feedback on code quality.

Features

• AI-generated PR review comments on code quality & best practices
• Automated test case generation for changed Python/JavaScript functions
• Runs tests and posts pass/fail results on the PR
• Works with Poetry for dependency management
• Seamlessly integrates into GitHub CI/CD pipelines

Installation

To use HEPHAI-PR-doctor, add the following workflow file to your repo:

name: HEPHAI-PR-doctor

on:
  pull_request:
    types: [opened, synchronize, reopened]

jobs:
  test-action:
    runs-on: ubuntu-latest

    steps:
      - name: Checkout repository
        uses: actions/checkout@v3

      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.10'

      - name: Install Poetry
        run: pip install poetry

      - name: Install dependencies
        run: poetry install --no-root

      - name: Run HEPHAI-PR-doctor
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: poetry run hephai-pr-doctor

Configuration

HEPHAI-PR-doctor requires:

• GITHUB_TOKEN (Automatically available in GitHub Actions)
• OPENAI_API_KEY (Set as a repository secret to use OpenAI for PR reviews and test generation)

How It Works

1. The action is triggered when a PR is opened or updated.
2. It fetches changed Python/JavaScript files in the PR.
3. The AI reviews code for readability, best practices, and potential issues.
4. It generates test cases for modified functions.
5. Runs tests and posts results as a comment on the PR.

Example Output

Example AI-generated PR comment:

### AI-Powered PR Review Feedback
#### File: my_script.py

• Code readability is good, but consider adding docstrings for better maintainability.
• Best practices followed. No major issues detected.
• Function process_data() lacks unit tests. Suggested test cases:

import pytest
from my_script import process_data

def test_process_data():
    assert process_data("input") == "expected_output"

## License
This project is licensed under the **Apache-2.0 License**.

## Contributing
Contributions are welcome! Feel free to submit PRs for enhancements or bug fixes.

## Contact
For any questions, open an issue or reach out on GitHub.

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**Improve your PR workflow with AI-powered reviews & test generation!**