First
In software development, ensuring the quality of code is crucial for delivering a reliable product. Automated testing and continuous integration (CI) processes help catch issues early in the development cycle. This blog outlines the steps taken to set up a CI pipeline for a Python project using GitHub Actions. It covers the configuration of automated testing, linting, and handling common challenges faced during implementation.
Setting Up CI/CD for a Chatminal
In this case, our project involves building a Python application with the following goals:
- Run automated tests for every push and pull request to the main branch.
- Perform code linting and enforce formatting standards.
- Ensure all changes pass CI checks before merging.
1. Creating the GitHub Actions Workflow
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.11
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Run Linter
run: |
pip install flake8 black
flake8 ./app/*.py
black --check .
- name: Run Tests
run: |
python -m unittest discover -s test
2. Handling Linting with flake8 and black
Linting ensures consistent code style and catches potential bugs early. We used flake8 for code linting and black for enforcing code formatting. To ignore specific warnings (e.g., unused imports), .flake8 configuration was added:
[flake8]
ignore = F401
3. Writing and Running Unit Tests
Tests were written using the unittest module. Each test file followed a naming convention of *_test.py and was placed in the test directory. An example of a tests for the Chatminal Object wide, Utilities.
4. Enforcing CI Checks for Pull Requests
To ensure code quality, a rule was set to block merging if CI checks fail. This prevents code that does not meet quality standards from entering the main branch.
Conclusion
Setting up a robust CI/CD pipeline with automated testing and linting helps maintain high code quality. GitHub Actions simplifies this process, making it easy to automate testing, enforce code standards, and catch issues early. By adopting these practices, developers can focus on building features while reducing the likelihood of introducing regressions.
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