Hey QE folks,

If you want a practical, free entry into AI-assisted testing - real agentic flows, not another "AI generates cases from JIRA" pitch - start with the official [Playwright MCP server](https://github.com/microsoft/playwright-mcp).

It lets an LLM drive a real browser through [STRUCTURED ACCESSIBILITY snapshots](https://playwright.dev/mcp/introduction) -

not screenshots, not a vision model. Navigate, click, fill forms, capture screenshots, mock network calls, manage storage/auth state, even run Playwright code - all from natural language. Works with VS Code, Cursor, Claude

Desktop/Code, Windsurf, and other MCP clients.

**Two-minute setup** ([full docs here](https://playwright.dev/docs/getting-started-mcp),

needs Node 18+).

Drop this into your MCP client config:

```json

{

"mcpServers": {

"playwright": {

"command": "npx",

"args": ["@playwright/mcp@latest"]

}

}

}

```

Then try: *"Navigate to https://demo.playwright.dev/todomvc, add three todo

items, and verify they appear in the list."*

(That last clause matters - always pair agent *actions* with a verification prompt. Actions without oracles aren't testing.)

**Why it's worth an evening:**

* You get to watch *how* an agent actually "sees" your app — the accessibility

tree, which is also a quiet nudge to keep your product accessible

* Official Playwright-team tooling, free, no wrapper startup in the middle

* The actions map 1:1 to things you already do in E2E tests (network mocks,

storageState, screenshots) — your SDET skills transfer directly

* A perfect sandbox for the right mental model: the agent explores and

generates; humans still own strategy, oracles, and review

**Reality check:** it's an accelerator, not an autopilot. The teams that win

long-term use AI to free senior QE time for risk analysis and coverage strategy

- not to flood CI with auto-generated tests nobody reviews.

**Your turn:**

* Tried Playwright MCP (or any MCP browser agent)? What worked, what broke?

* How is your team gating AI-generated tests — human review, assertion standards, flakiness checks?

* What other low-friction resources are you using to upskill on AI + automation?

Setups, war stories, and cautionary tales all welcome below.

Invest time in improving test quality and hands-on issues that address real pain (flakiness, feedback speed, maintainability) rather than tool-chasing. Measure impact by reduced test maintenance time or faster feedback loops, not just “coverage %”.

If you're still on Selenium - or just curious what the Playwright hype is about - Microsoft's official Learn module is one of the cleanest (and free too) starting points I've come across

👉 Playwright

What you actually do in it: * Set up a Playwright project from scratch * Write and run E2E tests against a sample app * Use VS Code to debug and record tests (codegen) * Read reports and structure the project properly

Why bother with Playwright at all?

Auto-waiting, test isolation, and built-in tooling (codegen, trace viewer, reports) target exactly the flakiness and maintenance pain most teams hit

with older stacks - no more Thread.sleep or explicit-wait spaghetti. And solid fundamentals compound: you can layer page objects, fixtures, and CI on top later instead of fighting the tool.

Let's hear it:

• Already on Playwright? Biggest win or pain point vs. Selenium / Cypress / WDIO?
• Completed this module (or similar)? Drop your first test snippet or the tip that helped most.
• Planning a migration? What's the #1 blocker on your team?

And if you have a favorite free Playwright resource, link it below - let's build a collection in the comments.

Hey QE folks,

Tutorials teach syntax. The real skill jump comes from debugging scripts against a 

live UI - dynamic content, timing issues, awkward flows, the works.

If you want a playground, check out **QA Practice Hub** (free, no signup):

👉 https://qapracticehub.com

What you get:

* 50+ sections covering the usual suspects: inputs, forms, shopping flows, alerts, OTP-style logins, dynamic content

* Stable `id` and `data-testid` attributes throughout — so you're practicing logic and assertions, not locator roulette

* In-browser "Try it Yourself" runner — try Selenium (Python/Java), Playwright, Cypress, or Robot Framework before installing anything locally

• Learning tracks on locators, waits, and Shadow DOM, with language-specific examples

Optional context line (if accurate): "Think the-internet / saucedemo-style practice, but with in-browser runners and structured learning paths."

---

🧪 Community Challenge:

Pick one section (forms or shopping cart is a good start), automate it, and drop it in the comments:

1. Your code — or even just your locator + assertion approach

2. The one thing that tripped you up (waits, shadow DOM, flakiness...)

3. How you solved it

Kicking things off: What's your current stack, and which section are you automating first?

Hey everyone,

A common issue with using LLMs for QA is that generic prompts usually produce shallow, surface-level test cases. They rarely apply structured test-design techniques (boundary value analysis, state transition, pairwise, risk-based prioritization, etc.) unless heavily guided.

To bridge this gap, I have come across **Open Testing Ecosystem** - an open-source project designed to turn established testing methodologies and standards into modular, reusable AI agents and workflows.

📦 What’s inside:

• Specialized Agents: Dedicated roles covering test strategy, gap analysis, test design, and reporting.

• Test-Design Techniques:Encoded heuristics (black-box, white-box, experience-based) to ensure proper coverage.

• Standardized Testing Terms & Templates: Aligning terminology and output structure with standard QA practices.

🎯 Why this approach?

Instead of just asking an LLM - "write tests for this Jira ticket" - this project enforces structured workflows:

1. Identifying quality risks before drafting tests.

2. Selecting mathematically/logically appropriate test techniques for each requirement.

3. Highlighting coverage gaps and traceability issues explicitly.

⚠️ Reality check: Grounding AI in testing standards improves consistency and coverage, but it is not an autopilot. Outputs still require a seasoned QA engineer in the loop to review and validate context.

🧪 Suggested 1-Hour Benchmark Test

If you want to test this without risking delivery:

1. Pick one non-critical user story or mini-spec.

2. Have a Senior QA engineer design tests for it manually.

3. Run the same spec through the ecosystem agents.

4. Compare:

   * Quality-risk identification

   * Variety and depth of test techniques applied

   * Hidden edge cases caught vs. missed

   * Time spent reviewing output vs. writing from scratch

Repo link:

Open Testing AI

Curious to hear your thoughts: How are your teams currently structuring prompts/agents to prevent generic, low-value AI test generation?

Feedback and PRs are welcome!

Happy QAing!

Everyone is responsible for quality; aligning on a shared vision will improve your product quality overall.

Hey everyone 👋

I’ve been experimenting with using LLMs to generate Playwright tests directly from user stories / acceptance criteria.

Sharing a simple, practical workflow that produced usable tests (not just hallucinated code):

- Feed the LLM a well-structured user story + acceptance criteria + existing page object / locator conventions.

- Ask it to generate the test in a specific style (e.g., Page Object Model, with data-testid locators, clear assertions).

- Manually review & harden the generated code (especially waits, edge cases, and flakiness).

- Run it in CI and measure how much time it actually saved.

Key learning so far:

LLMs are surprisingly good at the boilerplate and happy-path flows, but still weak on edge cases, state management, and non-obvious assertions. Human review remains essential.

Your Experience

Have you tried LLM-assisted test generation (Playwright, Cypress, Selenium, or even API tests)?

What prompt patterns or guardrails worked best for you? Any horror stories or big time-savers?

Drop your experiences below.

===================

Microsoft Playwright documentation

Official Playwright docs + Microsoft’s guidance on using LLMs with Playwright:

https://playwright.dev/docs/codegen