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AI Test Generation

AI Test Generation

Overview

AI test generation helps you draft structured test cases from natural language, repo context, or pull request diffs. The generator outputs steps, expected results, and metadata suitable for AI execution.

Inputs

  • Target URL (for E2E tests).
  • Prompt or requirement text.
  • Optional GitHub repository context.
  • Optional PR diff context for change-based testing.
  • Provide the target URL.
  • Mention critical UI elements by role or visible text.
  • List expected outcomes as clear assertions.

Example:

Validate checkout success on https://example.com.
Steps: open cart, proceed to checkout, submit order.
Assertions: confirmation banner visible, order number shown, cart empty.

Outputs

  • Structured test case with steps.
  • Suggested tags, severity, and status.
  • AI clarity warnings (when ambiguous).

Best practices

  • Keep steps atomic and explicit.
  • Prefer stable selectors or accessible roles when available.
  • Include at least one final assertion.
AI Test ExecutionAPI Reference