Playwright Python Hybrid Framework 🚀

An R&D Repository for Asynchronous Quality Engineering.

🏗️ Modern Modular Architecture

This lab is built using a Domain-Driven Design (DDD) approach to ensure scalability and separation of concerns:

• core/: The engine room containing Custom Async API Clients and Browser configurations.
• pages/: Page Object Models (POM) representing the application state and UI interactions.
• infra/: Infrastructure as Code, including Docker-compose and environment utilities.
• configs/: Centralized environment-specific settings and secret management.
• data/: Structured test data and Pydantic models for API request/response validation.
• legacy/: Archived previous implementations for historical reference.

🛠️ Tech Stack

• Language: Python 3.12+ (Optimized for Async/Await patterns)
• Framework: Pytest + Playwright (Plugin-based architecture)
• API Engine: HTTPX (High-performance asynchronous HTTP client)
• Config Management: Pydantic-Settings (Environment variable validation)
• Build System: pyproject.toml (Following PEP 518/621 Standards)
• Linting/Formatting: Ruff (Ensuring high-quality, code standards)
• CI/CD: GitHub Actions using Node 24 runners for modern performance.

🚀 Quick Start (Local Setup)

Follow these steps to replicate the lab environment:

1. Clone & Create Virtual Env:

• python -m venv .venv
• Windows: .venv\Scripts\activate
• Mac/Linux: source .venv/bin/activate

2. Install Local Package & Dependencies:

• pip install .
• playwright install chromium

3. Configure Environment:

• Create a .env file in the root directory (referencing .env_example)

4. Run Tests:

• pytest

🚀 Latest Milestones

• Async API Engine: Implemented AsyncAPIClient with centralized logging.
• Type-Safe Configuration: Migrated to Pydantic-Settings for robust .env management.
• Multi-Target Support: Infrastructure ready for both SauceDemo (UI) and Restful-Booker (API/Hybrid).

📈 Engineering Objectives

• Hybrid Testing Flow: Executing API-first setups (e.g., Auth/Data seeding) followed by UI verification.
• Asynchronous Execution: Reducing CI/CD bottleneck by leveraging non-blocking I/O.
• Correlation Tracing: Implementing X-Correlation-ID in API clients for observability.
• Atomic Commits: Maintaining a clean, traceable Git history for collaborative growth.
• Modern Standards: Using pyproject.toml for unified, standardized configuration.