What you'll learn:
- Build a Playwright test automation framework in Java that remains maintainable as it grows
- Write UI and API tests that are stable enough to trust in CI, not just locally
- Structure test code so changes don’t ripple unpredictably across the suite
- Use AI to support test design and implementation without introducing inconsistency
- Handle authentication, network behaviour, and dynamic UI scenarios in real applications
- Run tests in parallel without introducing subtle, hard-to-debug failures
- Produce reports that clearly explain what failed and why
Most Playwright tests work at first. Then they start to fall apart.
AI can generate Playwright tests in seconds.
Keeping them consistent over time is where it gets hard.
A few weeks in, the same flows start appearing in slightly different forms. Assertions don’t quite line up anymore.
Someone fixes a selector in one place but not another.
Then a small UI change lands, and suddenly half the suite is red.
At that point, it’s not obvious what’s wrong. The code looks reasonable. It runs. But it doesn’t behave the same way twice.
That’s usually a design problem, not a tooling problem.
And it’s something I’ve seen repeatedly in large teams and long-lived systems.
That’s what this course is about.
Playwright itself isn’t the hard part. Writing a test that passes isn’t the hard part either.
What’s difficult is ending up with a test suite that still makes sense as it grows.
One where changes don’t ripple unpredictably, and where you don’t have to stop and think “why was this written this way?” every time you open a file.
That’s what this course focuses on.
You’ll build out a Playwright framework in Java step by step, but the emphasis isn’t on the mechanics.
It’s on the decisions behind it.
Why one structure holds up better than another. Why some tests become fragile even when they look clean.
We’ll work through real scenarios — authentication, API interactions, dynamic UI behaviour — the kinds of things that tend to break naive test suites.
By the end, you should have something that feels like a system, not just a collection of tests.
AI is part of that system now, whether we like it or not.
Used carefully, it’s genuinely useful. It helps explore flows, sketch out tests, and fill in the obvious gaps.
But left unchecked, it introduces a kind of quiet inconsistency.
Not dramatic failures. Just small differences that accumulate.
Nothing is obviously wrong, but nothing quite lines up either.
That’s where most teams start losing control.
So instead of treating AI as a shortcut, we treat it as something that needs boundaries.
You’ll see how to guide it so the output fits into a consistent structure.
How to keep naming, intent, and patterns aligned.
The aim isn’t to write tests faster.
It’s to make sure what you generate today still makes sense a month from now.
It’s about 15 hours of content, and it assumes you already know your way around Java and basic testing.
This isn’t about getting started. It’s about getting past the point where things start to get messy.
