ecommerce-ui-automation

Overview

This repository contains a UI automation suite developed to validate the functionality of the e-commerce website SauceDemo. The project demonstrates modern test automation practices, including Behavior-Driven Development (BDD), Page Object Model (POM), and CI/CD pipelines.

The goal of this project is to showcase:

• Test automation capabilities.
• Framework setup, structure, and scalability.
• Integration with tools like reporting, CI/CD pipelines, and pre-commit hooks.

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Features Tested

This automation suite validates critical functionalities of the SauceDemo website:

1. Authentication

   • Login and logout functionalities.
   • Handling invalid credentials and missing input scenarios.
   • Addressing performance issues, such as long login times.

2. Products Page

   • Verification of product listings and navigation.
   • Sorting products by name and price.
   • Adding/removing products to/from the cart.
   • Viewing product details.

3. Shopping Cart

   • Validating the initial state of an empty cart.
   • Adding/removing products and continuing shopping.
   • Verifying cart updates and navigation functionality.

4. Checkout Process

   • Ensuring mandatory fields are required.
   • Validating initial and canceled states at each stage of checkout.
   • Providing an overview of selected products before purchase.

5. Burger Menu

   • Validating navigation options such as logout, "All Items," and "About."
   • Handling the menu in different contexts, e.g., cart and checkout pages.
   • Ensuring state reset functionality works as intended.

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Project Structure

The project is organized with a clear folder structure to ensure scalability and maintainability:

ecommerce-ui-automation/
├── .github/                   # GitHub Actions for CI/CD
│   └── workflows/
│       └── run_ui_tests.yml   # Workflow configuration for CI pipeline
├── .pytest_cache/             # Pytest's cache directory (auto-generated, git-ignored)
├── ui_automation_suite/       # Python package for the test automation suite
│   ├── bdd_tests/             # Test scripts written in BDD style
│   │   ├── features/          # Gherkin feature files
│   │   ├── step_defs/         # Step definitions for test steps
│   │   ├── pytest_fixtures/   # Custom fixtures for Playwright and other utilities
│   │   └── page_objects/      # Page Object Models for web pages
│   ├── settings/              # Test configuration files
│   ├── utils/                 # Utility functions and helpers
│   └── __init__.py            # Marks this as a Python package
├── reports/                   # Test execution reports (generated by Allure and Playwright traces)
│   ├── allure-report-bdd/     # Generated Allure HTML reports
│   ├── allure-results-bdd/    # Raw results for Allure
│   └── playwright_trace_data/ # Trace data generated by Playwright
├── .env                       # Environment variables (git-ignored)
├── .gitignore                 # Excludes unnecessary files from version control
├── .pre-commit-config.yaml    # Pre-commit hooks for code quality checks
├── conftest.py                # Pytest's configuration and shared fixtures
├── Makefile                   # Automation commands for setup and execution
├── pyproject.toml             # Poetry configuration for dependencies
├── pytest.ini                 # Pytest configuration
├── README.md                  # Project documentation
└── poetry.lock                # Poetry's lock file for dependencies

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Technology Stack

The suite is built using the following technologies:

• Test Automation Framework: Playwright
• BDD Framework: pytest-bdd
• Reporting: Allure
• Dependency Management: Poetry
• CI/CD: GitHub Actions
• Code Quality: pre-commit hooks (black, isort) for linting and formatting
• Logging: Python logging for debugging insights

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Setup and Execution

Option1: Running Tests in CI

The test suite is integrated with GitHub Actions for Continuous Integration (CI).

• Trigger CI Tests:
  • automatically on pull requests
  • manually via GitHub Actions Workflows
• View Results:
  • [Github Pages]:
    • an Allure report is automatically generated and hosted here.
    • ⚠️Note: Only the latest execution's report will be displayed️
  • [Github Artifacts]:
    • the Allure report is also stored as an artifact in the respective GitHub Actions run. You can download and view the report locally.

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Option2: Running Tests Locally

- Prerequisites

• Python 3.11 or later installed.
• Poetry installed (for dependency management).

- Steps to Run tests locally

1. Clone the repository:

   git clone https://github.com/yourusername/ecommerce-ui-automation.git
   cd ecommerce-ui-automation

2. Install dependencies:

   poetry install

3. Run the complete test suite:

    make test

4. View the report:

   make generate-and-view-report

5. Click on the link to view the report in the browser

- Ways to Run Tests

The test suite offers various execution modes for flexibility.

1. Run the complete test regression suite

   1. Headless Mode

      make test

   2. Headed Mode

      make test-headed

2. Run a specific test:

   1. Headless Mode

      make test "MARKER=login_success"

   2. Headed Mode

      make test-headed "MARKER=login_success"

3. Change Browser (default: chromium)

   1. Headless Mode

      make test "BROWSER=firefox"

   2. Headed Mode

      make test-headed "BROWSER=firefox"

4. Debug Mode (Runs only in headed mode)

   1. Run step-by-step:

      make test-debug

   2. Combine with specific marker and browser:

      make test-debug "MARKER=login_success" "BROWSER=firefox"

Additional Notes

Key Features

• Comprehensive Coverage: Validates authentication, product navigation, checkout, and more.
• Clean Architecture: Follows BDD principles, Page Object Models, and modular design.
• Continuous Integration: Automates test execution via GitHub Actions.
• Enhanced Reporting: Uses Allure for detailed test result visualizations.
• Pre-commit Hooks: Ensures code quality with tools like black and isort.

Future Improvements

• Integrate Visual Testing: Automate visual regression testing to detect UI inconsistencies and layout shifts effectively.
• Extend Test Coverage: Add more test scenarios for edge cases and negative paths.
• Parallelize Test Execution Across Browsers: Optimize test runtime by running tests simultaneously in multiple browsers (e.g., Chrome, Firefox, Safari).
• Automate Local Report Management: Implement a local mechanism to keep only the latest test report for easy access while automatically deleting older reports to reduce clutter.
• Centralize Report Storage for Historical Analysis: Implement a system to upload and store test results in a centralized location (e.g., S3 bucket, database, or CI tool dashboard) to maintain a history of test executions, allowing trend analysis and debugging over time.
• Cross-Device Testing: Extend test suite to simulate and verify functionality across different device types (e.g. mobile, tablet)
• Standardized Assertion Library: Create a dedicated and reusable library for assertion comparisons to promote consistency, maintainability, and readability across tests.