After this training, you will:
– Embrace the “Quality as Code” mindset by structuring test plans and specifications in plain files (using formats like Markdown, Gherkin and Gauge).
– Use the essential features of Git to manage all quality assets as a version-controlled ‘single source of truth’, enabling collaboration and control.
– Understand why context is vital for AI and how to prepare a repository to serve as a “brain” for AI assistants.
– Write and understand fundamental browser automation scripts with Playwright.
– Generate and refine Playwright scripts using context-aware AI tools (like KiloCode in VSCode) based on your own Markdown documentation.
– Integrate these automated Playwright tests into a basic CI/CD pipeline (using GitHub Actions) to provide rapid feedback.
The future of quality assurance is not about choosing between manual testing and complex coding. It’s about a mindset: “Everything is a file.” This course bridges the gap between test plans and automation scripts by teaching an end-to-end process that is directly applicable in the real world.
This course teaches you how to apply AI in a practical way by giving it the right context from your own work. You will gain the skills to connect with DevOps teams and participate in a CI/CD process. You will learn not just the tools, but a workflow that enables effective collaboration, making you a valuable contributor to fast, high-quality software delivery.
This course is designed for quality professionals ready to lead the change. It’s the perfect, practical next step for those with a background in testing methodologies like ISTQB or TMap®.
No prior experience with Git or Playwright is necessary to participate.
– Software Testers and QA Engineers who want to learn a practical and modern automation workflow.
– QA Leads and Senior Testers aiming to implement a future-proof, efficient, and AI-enhanced quality strategy for their teams.
Day 1: The Foundation – “Everything is a File”
• Module 1: The New Mindset: From Documents to a Repository
o Why scattered documents fail in a fast-paced world.
o The ‘Quality as Code’ principle: treating tests and docs like source code.
o Hands-on Lab: Structuring your first test plan in Markdown.
• Module 2: Git for Quality Professionals
o The essentials for this workflow, no more, no less: repository, commit, branch, pull request. How this enables control and collaboration.
o Hands-on Lab: Creating a Git repository for your quality assets.
• Module 3: Introduction to Browser Automation with Playwright
o Core concepts: locators, actions, and assertions. The power of browser control.
o Hands-on Lab: Manually writing your first Playwright script based on your Markdown specification.
Day 2: The Acceleration – Intelligent, Context-Aware Automation
• Module 4: The Power of Context for AI
o Why AI needs your repository: moving beyond simple prompts.
o Preparing your repository to be the “brain” for your AI assistant.
• Module 5: AI-Assisted Script Generation in Practice
o Introduction to advanced concepts like the Model Context Protocol (MCP).
o Hands-on Lab in VSCode: Using tools like KiloCode to interact with your browser and generate advanced Playwright scripts based on the full context of your Git repository. This is where the magic happens!
• Module 6: Closing the Loop with CI/CD
o Integrating your Playwright test suite into a GitHub Actions pipeline.
o Analyzing results and understanding the fast feedback loop.
o The new workflow: Update the Markdown, commit to Git, and see the pipeline run.
– Duration: 2 Days
– Language: English
– Prerequisites: A solid understanding of software testing principles is required. No prior experience with AI, Playwright, or Git is needed to participate. We build all necessary skills from the ground up. Familiarity with the basic concepts of version control is a plus, but will be covered.
– What to bring: A laptop with VSCode installed and a personal GitHub account.
As a software engineer and entrepreneur, Simon Koudijs focuses on a practical goal: building quality software that is delivered reliably. His background in Electrical Engineering taught him to measure and solve problems in small steps; a skillset he has applied everywhere, from embedded programming and small startups to large companies like the pension fund PGGM.
In his daily work, he builds a software product. He uses modern AI tools to get the job done, but isn’t one to be fooled by the hype. This training is based on that hands-on experience. You’ll learn the practical skills and the “why” behind them, so you can confidently apply these methods to build better software, faster.
