Modern E2E Test Architecture: Patterns and Anti-Patterns for a Maintainable Test Suite

The Challenge of E2E Testing

End-to-end tests verify that an application functions correctly from the user’s perspective, testing all components from frontend to backend. While invaluable, these tests often become the most troublesome part of a CI/CD pipeline for several reasons:

1. Complexity: E2E tests interact with multiple systems and dependencies
2. Flakiness: Tests fail intermittently due to timing, network, or environment issues
3. Maintenance burden: Changes to the UI often break tests, requiring constant updates
4. Slow execution: Running a comprehensive E2E suite can take hours

A well-designed test architecture can mitigate these challenges while maximizing the value of your E2E tests.

Architectural Patterns for Maintainable E2E Tests

1. The Page Object Pattern

The Page Object pattern remains one of the most effective approaches for organizing E2E test code. It encapsulates UI elements and interactions within dedicated classes, creating an abstraction layer between test logic and UI implementation.

// Instead of this:
test('user login', async () => {
  await page.fill('#username', 'testuser');
  await page.fill('#password', 'password123');
  await page.click('button[type="submit"]');
  await expect(page.locator('.welcome-message')).toBeVisible();
});

// Do this:
class LoginPage {
  constructor(page) {
    this.page = page;
    this.usernameInput = page.locator('#username');
    this.passwordInput = page.locator('#password');
    this.submitButton = page.locator('button[type="submit"]');
  }

  async login(username, password) {
    await this.usernameInput.fill(username);
    await this.passwordInput.fill(password);
    await this.submitButton.click();
  }
}

test('user login', async () => {
  const loginPage = new LoginPage(page);
  await loginPage.login('testuser', 'password123');
  await expect(page.locator('.welcome-message')).toBeVisible();
});

Key benefits:

• Centralizes selector maintenance
• Improves test readability
• Makes tests resilient to UI changes

2. Component-Based Test Structure

Modern applications are built with components. Your test structure should mirror this architecture:

tests/
  ├── components/
  │   ├── header/
  │   │   ├── navigation.spec.js
  │   │   └── search.spec.js
  │   ├── cart/
  │   │   ├── add-item.spec.js
  │   │   └── checkout.spec.js
  ├── flows/
  │   ├── authentication.spec.js
  │   └── purchase.spec.js
  └── e2e/
      └── complete-purchase.spec.js

This approach:

• Keeps tests focused on specific functionalities
• Makes it easier to locate and update affected tests when components change
• Enables more granular test runs based on code changes

3. Data Management Strategies

Tests need data, but hardcoding test data leads to brittle tests. Instead:

• Use factories: Create test data generators that produce consistent, customizable data
• Implement test isolation: Each test should create and clean up its own data
• Consider data seeding: Pre-populate databases with known states for specific test scenarios

// Data factory example
const createUser = (overrides = {}) => ({
  username: `user-${Date.now()}`,
  email: `test-${Date.now()}@example.com`,
  role: 'customer',
  ...overrides
});

test('admin can view user details', async () => {
  const testUser = createUser({ role: 'standard' });
  await api.users.create(testUser);

  // Test logic here

  await api.users.delete(testUser.id);
});

4. Service Virtualization

Reduce dependencies on external services by implementing service virtualization:

• API mocking: Intercept and mock API responses for predictable behavior
• Controlled environments: Create isolated test environments with simulated dependencies
• Contract testing: Ensure your mocks accurately represent real services

Anti-patterns to Avoid

1. Selector Fragility

Using brittle selectors like XPaths or positional selectors makes tests highly susceptible to UI changes.

❌ Avoid:

await page.click('div:nth-child(3) > button');

✅ Better:

await page.click('[data-testid="submit-button"]');

Collaborate with your development team to add testability attributes like data-testid to important elements.

2. Test Interdependence

Tests that depend on each other create cascading failures and make debugging difficult.

❌ Avoid:

// Test A creates a user
test('create user', async () => {
  await createUser('testuser');
});

// Test B depends on Test A
test('user can log in', async () => {
  await loginAs('testuser');
});

✅ Better: Each test should set up its own state and clean up afterward.

3. Excessive Waiting

Hard-coded waits lead to either brittle tests or unnecessarily slow execution.

❌ Avoid:

await page.click('#submit');
await page.waitForTimeout(2000); // Arbitrary wait
await expect(page.locator('#result')).toBeVisible();

✅ Better:

await page.click('#submit');
await page.waitForSelector('#result', { state: 'visible', timeout: 5000 });

4. Overlooking Test Isolation

Tests that modify shared state without proper cleanup create unpredictable environments.

❌ Avoid: Leaving test data in the system or modifying global configurations without restoring them.

✅ Better: Implement proper setup and teardown routines for each test.

Strategies for Reducing Flaky Tests

Flaky tests—those that pass sometimes and fail others—undermine confidence in your test suite. To combat flakiness:

1. Implement intelligent retries: Automatically retry failed tests to distinguish between actual failures and environmental issues
2. Log extensively: Capture detailed logs, screenshots, and videos of test failures
3. Monitor test metrics: Track flakiness rates and prioritize fixing the most unstable tests
4. Use visual regression tools: Detect unexpected visual changes that functional tests might miss

Building a Scalable CI/CD Pipeline

As your test suite grows, your CI/CD pipeline must evolve:

1. Parallelize test execution: Distribute tests across multiple agents
2. Implement test splitting: Group tests by execution time for balanced distribution
3. Use caching strategies: Cache dependencies and test environments between runs
4. Prioritize critical tests: Run the most important tests first for faster feedback

The Future of E2E Testing: AI-Powered No-Code Solutions

While architectural patterns and best practices significantly improve test suite maintainability, the future of E2E testing lies in intelligent automation and no-code solutions. As applications grow more complex, maintaining even well-structured test code becomes increasingly challenging.

AI-Driven Test Generation and Maintenance

The next frontier in testing involves leveraging artificial intelligence to:

1. Automatically generate test scenarios based on application analysis and user behavior patterns
2. Self-heal broken tests by intelligently adapting to UI changes
3. Predict potential failure points before code reaches production
4. Optimize test coverage by identifying gaps and redundancies

Platforms like Thunder Code are pioneering this approach, using AI agents to automate traditional manual QA work. These solutions can:

• Create comprehensive test suites without writing a single line of code
• Maintain tests automatically as applications evolve
• Generate test data that covers edge cases human testers might miss
• Provide intelligent insights into application quality and risk areas

Benefits of AI-Powered Testing Platforms

Organizations adopting these next-generation testing approaches experience:

• Reduced maintenance overhead: Tests automatically adapt to application changes
• Faster time-to-market: Test creation and execution requires significantly less time
• Higher test coverage: AI can generate more comprehensive test scenarios
• Lower skill barriers: Team members without coding expertise can create and manage tests
• More strategic QA focus: QA professionals can focus on test strategy rather than implementation details

As these platforms mature, we’re moving toward a future where test code becomes increasingly self-maintaining, with AI agents handling the complex task of keeping tests aligned with rapidly evolving applications.

Conclusion

A well-designed E2E test architecture is an investment that pays dividends through reduced maintenance costs and higher confidence in your application’s quality. By adopting architectural patterns that promote maintainability and avoiding common anti-patterns, you can build a test suite that evolves alongside your product rather than holding it back.

Looking ahead, AI-powered no-code testing solutions represent the next evolution in test architecture—reducing the burden of maintenance while increasing test effectiveness. These platforms don’t replace good architectural decisions but rather build upon them, using artificial intelligence to handle the complexity that makes traditional E2E testing challenging.

The most successful testing strategies will increasingly leverage these intelligent tools, allowing teams to focus on delivering value rather than maintaining brittle test suites. With the right combination of architectural principles and AI-powered automation, E2E tests can truly become a valuable asset that accelerates rather than impedes your development process.

About the Author :
Co-founder and CTO of Thunders with over 20 years of expertise in Cloud Architecture and AI. Co-founder and former CTO at Expensya, previously Senior Developer at Microsoft, Jihed Othmani combines strategic vision with deep technical expertise. He leads high-performing engineering teams focused on delivering scalable, high-impact technology solutions.