AI-Powered UI Debugging: Faster Problem Solving with Copilot and Playwright

Debugging user interfaces (UIs) can be a real headache. Tracking down that one elusive bug that only appears under specific conditions often feels like searching for a needle in a haystack. But what if you had a smart assistant to help you sift through the code, identify the problem, and even suggest solutions?

That's the promise of AI-powered debugging. Tools like GitHub Copilot, especially when used in "agent mode," combined with powerful testing frameworks like Playwright and its Machine-Checkable Properties (MCP) server, are revolutionizing how we tackle UI issues. This article will explore how these technologies can speed up your debugging process and make your life as a developer a whole lot easier.

The Challenge of UI Debugging

Before diving into the solutions, let's acknowledge the problem. UI bugs are notoriously difficult to fix because:

• They're often context-dependent: A bug might only appear when a specific browser version is used, on a particular screen size, or after a user interacts with the UI in a certain way.

• They can be visually subtle: A misaligned element, a slightly off color, or a text overflow might be hard to spot manually, especially in complex UIs.

• They involve multiple layers: UI bugs can stem from issues in the HTML, CSS, JavaScript, or even the backend data feeding the UI.

Traditional debugging methods, like stepping through code line by line or adding console.log statements everywhere, can be time-consuming and frustrating.

Enter the AI Assistant: GitHub Copilot Agent Mode

GitHub Copilot is an AI-powered code completion tool that can suggest entire lines of code or even whole functions based on your comments and the context of your code. In "agent mode," Copilot takes things a step further. It can act as an intelligent assistant, helping you to:

• Understand the code: Copilot can explain complex code blocks in plain English, helping you quickly grasp what's going on.

• Identify potential issues: By analyzing your code, Copilot can point out potential bugs or performance bottlenecks.

• Suggest fixes: Copilot can provide code snippets or even complete solutions to address the issues it identifies.

Think of it as having a senior developer sitting next to you, constantly offering helpful advice and guidance.

Playwright MCP Server: Formalizing UI Testing

Playwright is a powerful testing framework for modern web applications. It allows you to write automated tests that simulate user interactions with your UI. The Machine-Checkable Properties (MCP) server is an extension that brings formal verification techniques to Playwright tests.

Technical Deep Dive: What are Machine-Checkable Properties?

MCPs are essentially formal specifications of how your UI should behave. Instead of just checking if a button click triggers a specific function, you can define properties like "clicking this button should always lead to a successful form submission," or "this element should never overlap with that element."

By formalizing your UI requirements in this way, you can catch subtle bugs that might otherwise slip through the cracks.

Combining Copilot and Playwright MCP Server: A Powerful Debugging Workflow

The real magic happens when you combine Copilot and Playwright MCP server. Here's how you can use them together to debug UI issues more effectively:

1. Write Playwright tests with MCPs: Use Playwright to create automated tests that interact with your UI. Define MCPs to specify the expected behavior of your UI elements.

    // Example Playwright test with MCPs
    const { test, expect } = require('@playwright/test');
   
    test('Form submission success', async ({ page }) => {
      await page.goto('/my-form');
      await page.fill('#name', 'John Doe');
      await page.fill('#email', 'john.doe@example.com');
      await page.click('#submit');
   
      // MCP: Check that the success message is displayed
      await expect(page.locator('#success-message')).toBeVisible();
   
      // MCP: Check that the form is cleared after submission
      await expect(page.locator('#name')).toBeEmpty();
      await expect(page.locator('#email')).toBeEmpty();
    });
   

2. Run the tests and identify failures: Run your Playwright tests and see which ones fail. The MCP server will provide detailed information about why the tests failed, pinpointing the specific properties that were violated.

3. Use Copilot to understand the code and the error: Paste the relevant code and the error message into Copilot. Ask Copilot to explain the code, identify potential causes of the error, and suggest fixes.

4. Implement Copilot's suggestions and re-run the tests: Try out the fixes suggested by Copilot and re-run the Playwright tests. Iterate on this process until all the tests pass.

Step-by-Step Example: Debugging a Broken Button

Let's say you have a button that's supposed to submit a form, but it's not working.

1. Playwright Test: You write a Playwright test that clicks the button and checks if the form is submitted successfully (e.g., by verifying the presence of a success message).

2. Test Failure: The test fails because the success message isn't displayed.

3. Copilot Analysis: You paste the button's click handler code and the test failure message into Copilot. You ask Copilot, "Why isn't this button submitting the form?"

4. Copilot Suggestion: Copilot analyzes the code and identifies a potential issue: a missing preventDefault() call in the click handler, which is preventing the form from being submitted.

5. Fix and Re-test: You add event.preventDefault() to the click handler and re-run the Playwright test. This time, the test passes!

Practical Implications

Using Copilot and Playwright MCP server together can have a significant impact on your development workflow:

• Faster Debugging: Quickly identify and fix UI bugs, saving you time and frustration.

• Improved Code Quality: Write more robust and reliable UI code by formalizing your requirements with MCPs.

• Reduced Manual Testing: Automate UI testing and reduce the need for manual testing, freeing up your time for other tasks.

• Better Collaboration: Use Copilot to explain complex code to other developers, improving collaboration and knowledge sharing.

Conclusion

AI-powered debugging is no longer a futuristic fantasy; it's a reality that's available to developers today. By combining tools like GitHub Copilot and Playwright MCP server, you can significantly accelerate your debugging process, improve the quality of your UI code, and ultimately deliver better user experiences. Embrace these technologies and unlock the power of AI to become a more efficient and effective developer.

Inspired by an article from https://github.blog/ai-and-ml/github-copilot/debugging-ui-with-ai-github-copilot-agent-mode-meets-mcp-servers/