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Getting Started with AI-Powered E2E Testing

A beginner-friendly guide to Qualigate: write tests in natural language, let AI execute them in a real browser, and review video recordings of every run.

Qualigate TeamFebruary 10, 20267 min read

Why AI-Powered E2E Testing?

End-to-end testing has traditionally been one of the most time-consuming parts of software development. You need to learn a test framework, write brittle selectors, and maintain scripts that break every time someone renames a CSS class. AI-powered testing changes all of that by letting you describe tests in plain English while an AI agent handles the execution in a real browser.

Qualigate is built around a simple premise: if you can describe what a user does, you can test it. No code, no framework setup, no selector maintenance.

How Qualigate Works

The platform has three core layers that work together to turn your words into reliable test results.

1. Natural Language Test Definitions

Instead of writing Playwright or Selenium scripts, you write numbered steps in plain English:

text
1. Navigate to https://myapp.com/login
2. Enter "demo@example.com" in the email field
3. Enter "securePass123" in the password field
4. Click the "Sign In" button
5. Verify that the dashboard page loads successfully
6. Verify that a welcome message containing "Hello" is visible

Each step tells the AI what action to take and what outcome to expect. You can be as specific or as general as you like---the AI uses visual context and page structure to find the right elements.

2. AI Execution Engine

When you click "Run Test," Qualigate launches a real Chromium browser behind the scenes. An AI model (you can choose between Pulse for speed or Nova for complex workflows) reads the current page, identifies the element that matches your step, performs the action, and takes a screenshot. This loop repeats for every step until the test finishes or an assertion fails.

Because the AI understands intent rather than relying on fixed selectors, your tests survive UI redesigns, framework migrations, and copy changes without manual updates.

3. Video Recordings and Step Reports

Every test run produces a full video recording of the browser session plus a step-by-step timeline. Each step shows:

  • The action taken (click, type, navigate, assert)
  • The AI's reasoning for choosing a particular element
  • A screenshot at the moment the action executed
  • Pass or fail status for assertions
If a test fails, you can watch the video to see exactly where things went wrong, read the AI's reasoning, and provide feedback for the next attempt.

Your First Test in Five Minutes

Here is a practical walkthrough to go from zero to a passing test:

  1. Create an account at qualigate.app and complete the onboarding wizard
    • Add a project by entering your application's URL (e.g., https://staging.myapp.com)
    • Create a test case and write your steps in the natural language editor
    • Click Run and watch the AI execute your test in real time
    • Review results including the video recording, step screenshots, and pass/fail status
You get 15 free credits during your trial period, which is enough to run several tests and evaluate the platform.

Tips for Your First Tests

  • Start with a smoke test. Pick a critical user flow like login, search, or adding an item to a cart.
  • Be explicit about verifications. Always end with a "Verify that..." step so the AI knows what success looks like.
  • Use quoted values for exact text. Write Enter "user@test.com" rather than Enter an email when you need a specific value.
  • Check the AI Testing Notes. After the first run, the AI generates notes about selectors and timing that improve future runs automatically.

What Happens After Your First Run

Qualigate gets smarter with every execution. The AI Testing Notes feature captures patterns, workarounds, and selector preferences from each successful run. After three consecutive successes, the system compiles an optimized recipe that can replay the test with near-zero token usage---saving you credits and time.

If a test fails, you can provide human feedback (e.g., "Click the search icon, not the text input") and retry. Successful feedback gets folded back into the AI's knowledge for all future runs.


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