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Modern E2E Testing with Playwright and AI

September 23, 2025 10:17
C
Checkly
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Modern End-to-End Testing with Playwright and AI

This webinar explores the integration of AI, specifically Large Language Models (LLMs), into the end-to-end testing workflow using Playwright. The presenter, Stefan from Checkly, demonstrates how AI can assist in generating, refactoring, and improving Playwright tests, while also highlighting crucial considerations for effective AI adoption.

Main Points

  • The Rise of AI in Development: The session acknowledges the significant impact of AI and LLMs on software development, discussing various perspectives on its role in code generation and developer productivity. [1:32-2:35]
  • Playwright and Synthetic Monitoring with Checkly: The webinar emphasizes Playwright's capabilities for end-to-end testing and how Checkly leverages Playwright for synthetic monitoring, enabling continuous testing and early detection of issues in production. [3:05-4:11]
  • Leveraging AI for Playwright Testing:
    • Context is Key: It's crucial to provide LLMs with relevant context. Using tools like "Context 7" to pull in up-to-date documentation ensures accuracy, preventing reliance on outdated LLM knowledge cutoffs. [8:19-9:54]
    • Generating Test Plans: AI can be used to analyze codebases (e.g., Next.js applications) and generate comprehensive test plans by identifying pages, API routes, and core functionalities. The output can be saved to markdown files (setup.md, testplan.md) for future reference. [10:54-17:42]
    • Playwright MCP for Control: Playwright MCP (Model Context Protocol) allows LLMs to interact with and control Playwright within conversations, enabling navigation, element interaction, and data retrieval. [13:31-15:35]
    • AI-Assisted Test Generation: By combining Playwright MCP with well-defined prompts and pre-generated context files, LLMs can be guided to generate Playwright test code, including handling actions and assertions. [21:16-23:27]
    • Refactoring and Improvement: AI can refactor existing Playwright code, such as introducing Page Object Models (POMs), to improve readability and maintainability. [32:26-36:10]
    • API Testing: Playwright can also be used for API testing, and AI can assist in generating test cases for API endpoints, including validating responses and status codes. [39:23-43:03]
  • Important Considerations and Best Practices:
    • Don't Trust Blindly: AI-generated code should always be reviewed and validated. LLMs can hallucinate or produce suboptimal solutions. [28:14-28:49, 43:01-43:34]
    • Iterative Approach: Fixing failing AI-generated tests often requires an iterative process, potentially involving multiple attempts by the AI or manual adjustments. [25:30-27:38]
    • Context Management: Keeping LLM conversations lean by clearing context or saving information to files (like setup.md) is vital to prevent the AI from going off track. [38:20-39:27]
    • Explicit Prompting: Be very specific and explicit in your prompts to guide the LLM effectively. [17:40-18:12]
    • Use AI for its Strengths: AI excels at tasks like refactoring, reformatting, and generating boilerplate code, saving developers significant time. [30:20-30:53, 50:19-50:52]

Key Takeaways

  • AI is a powerful co-pilot, not a replacement: LLMs can significantly boost developer productivity by assisting with test generation, refactoring, and debugging.
  • Context is paramount: Providing up-to-date documentation and codebase information to LLMs is essential for accurate and reliable results.
  • Playwright MCP unlocks interactive AI-driven testing: This protocol enables LLMs to control Playwright and gather necessary information for test creation.
  • Continuous review is non-negotiable: Always review and validate AI-generated code to ensure it meets requirements and doesn't introduce subtle bugs.
  • Checkly integrates AI-generated tests for monitoring: The platform allows users to deploy and run Playwright tests, including those generated with AI, for synthetic monitoring.

Actionable Insights

  • Experiment with Playwright MCP: Integrate Playwright MCP into your workflow to allow LLMs to interact with your applications.
  • Create context files: Develop setup.md or similar files to provide LLMs with essential information about your project.
  • Refine your prompts: Invest time in crafting clear and explicit prompts to guide AI effectively.
  • Leverage AI for repetitive tasks: Utilize AI for code refactoring, pattern enforcement, and generating repetitive test structures.
  • Utilize Checkly for AI-powered monitoring: Explore how Checkly can host and run your AI-generated Playwright tests for continuous monitoring.