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Incremental refactoring of automated tests

Incremental refactoring of automated tests is crucial in maintaining the health and effectiveness of the test suite. As the codebase evolves, tests can become outdated or brittle, leading to false positives or negatives. By regularly refactoring tests, we can ensure they remain aligned with the current code structure and business logic, ultimately improving their reliability and reducing maintenance overhead.

Before Doing Refactoring

While it may seem efficient to simultaneously add new tests and refactor existing ones, it's important to separate refactoring from new feature implementation. Before beginning any refactoring work, ensure these activities remain distinct. This separation provides several advantages:

  • Isolating changes. When you combine refactoring with new features, it becomes difficult to determine if issues are caused by the refactoring or the new functionality.

  • Risk management. Refactoring carries its own risks, as does adding new features. Combining them multiplies the potential points of failure.

  • Focused code reviews. When pull requests contain both refactoring and new features, reviewers struggle to effectively evaluate either aspect properly, thus increasing the cognitive load for the reviewer.

  • Ensure simpler rollbacks. If you need to revert changes, having separate commits for refactoring versus new features allows for more targeted rollbacks.

  • Have clearer commit history. Separating refactoring from feature work makes your version control history more meaningful and easier to understand.

Guidelines for Refactoring

Focus on Most Critical Refactoring

  • Identify pain points first. Start with either flaky tests (tests that keep on retrying), slow tests, or tests that generate the most maintenance work.

  • Prioritize by impact. Focus on tests covering critical functionality or areas with high change frequency. Tests of this kind tend to bring out the most value.

  • Use metrics to guide decisions. Code coverage, test execution time, and failure frequency can reveal where refactoring will provide the most benefit.

Test Early, Test Often

  • Refactor in small, manageable steps. Make atomic changes, follow a consistent pattern, and document your changes.

  • Run tests after each small change. This allows to identify issues early and fix them before they compound.

  • Common test refactoring techniques. Extract common setup and teardown logic, improve test naming conventions, remove duplication, enhance assertion clarity, and separate test data from test logic.

Apply Test Design Patterns

  • Page Object Model. For UI tests, implement page objects to encapsulate UI elements and interactions. An example of a Page Object Model implementation is a simple login page:
// Simple Page Object for a login page using Playwright with TypeScript
class LoginPage {
  private page: Page;

  // Element selectors
  private readonly usernameField = '#username';
  private readonly passwordField = '#password';
  private readonly loginButton = '#login-btn';

  constructor(page: Page) {
    this.page = page;
  }

  // Page actions
  async enterUsername(username: string): Promise<void> {
    await this.page.fill(this.usernameField, username);
  }

  async enterPassword(password: string): Promise<void> {
    await this.page.fill(this.passwordField, password);
  }

  async clickLogin(): Promise<void> {
    await this.page.click(this.loginButton);
  }

  // Combined actions
  async login(username: string, password: string): Promise<void> {
    await this.enterUsername(username);
    await this.enterPassword(password);
    await this.clickLogin();
  }
}

// Usage in a test
test('user can login with valid credentials', async ({ page }) => {
  const loginPage = new LoginPage(page);
  await page.goto('/login');
  await loginPage.login('testuser', 'password123');

  // Assert successful login
  await expect(page).toHaveURL('/dashboard');
});
  • Builder Pattern. Use for complex test data creation to improve readability. The example below showcases a very simple UserBuilder.ts class to create user objects for tests.
// UserBuilder.ts - Example of Builder Pattern for test data
class UserBuilder {
  private firstName: string = 'John';
  private lastName: string = 'Doe';
  private email: string = 'john.doe@example.com';
  private password: string = 'Password123!';
  private isAdmin: boolean = false;

  withFirstName(firstName: string): UserBuilder {
    this.firstName = firstName;
    return this;
  }

  withLastName(lastName: string): UserBuilder {
    this.lastName = lastName;
    return this;
  }

  withEmail(email: string): UserBuilder {
    this.email = email;
    return this;
  }

  withPassword(password: string): UserBuilder {
    this.password = password;
    return this;
  }

  asAdmin(): UserBuilder {
    this.isAdmin = true;
    return this;
  }

  build(): User {
    return {
      firstName: this.firstName,
      lastName: this.lastName,
      email: this.email,
      password: this.password,
      isAdmin: this.isAdmin,
    };
  }
}

// User interface
interface User {
  firstName: string;
  lastName: string;
  email: string;
  password: string;
  isAdmin: boolean;
}

// Usage in a test
test('admin user can access settings page', async ({ page }) => {
  // Create an admin user with custom email using the builder
  const adminUser = new UserBuilder()
    .withEmail('admin@example.com')
    .asAdmin()
    .build();

  // Use the built user in your test
  await loginAs(page, adminUser);
  await page.click('#settings-link');

  // Assert admin-specific elements are visible
  await expect(page.locator('#user-management')).toBeVisible();
});

// Regular user test with different data
test('regular user views profile page', async ({ page }) => {
  // Create a regular user with default values except name
  const regularUser = new UserBuilder()
    .withFirstName('Jane')
    .withLastName('Smith')
    .build();

  await loginAs(page, regularUser);
  // Continue with test...
});
  • Test Data Factories. Create reusable methods that generate test data with sensible defaults. An example of an implementation of Test Data Factory pattern is shown below.
// TestDataFactory.ts - Example of Test Data Factory pattern
class TestDataFactory {
  // Create a user with default values
  static createDefaultUser(): User {
    return {
      firstName: 'John',
      lastName: 'Doe',
      email: 'john.doe@example.com',
      password: 'Password123!',
      isAdmin: false,
    };
  }

  // Create an admin user
  static createAdminUser(): User {
    return {
      firstName: 'Admin',
      lastName: 'User',
      email: 'admin@example.com',
      password: 'SecurePass456!',
      isAdmin: true,
    };
  }

  // Create a user with specific email
  static createUserWithEmail(email: string): User {
    const user = this.createDefaultUser();
    return { ...user, email };
  }

  // Create a product with default values
  static createDefaultProduct(): Product {
    return {
      id: 'prod-1',
      name: 'Test Product',
      price: 99.99,
      inStock: true,
    };
  }
}

// Usage in a test
test('admin can update product prices', async ({ page }) => {
  const admin = TestDataFactory.createAdminUser();
  const product = TestDataFactory.createDefaultProduct();

  await loginAs(page, admin);
  await updateProductPrice(page, product.id, 149.99);

  // Assert price was updated
  await expect(page.locator(`#product-${product.id} .price`)).toHaveText(
    '$149.99',
  );
});

Improve Test Independence

  • Avoid test ordering dependencies. Each test should be able to run in isolation, unless the test scenario requires a specific order such as the change password feature.

  • Clean up test data. Ensure proper teardown to prevent test pollution. Proper teardown prevents one test from affecting others and maintains a clean test environment, which is essential for reliable test results.

  • Use fresh fixtures. Reset or recreate test environments between test runs. The goal is always to ensure each test runs in a clean, predictable environment.

Enhance Maintainability

  • Parameterize tests. Convert duplicate tests with slight variations into parameterized tests. This reduces code duplication and improves test readability.

  • Establish clear test boundaries. Each test should verify one specific behavior. This makes it easier to identify the source of failures and simplifies test maintenance.

  • Implement test tagging. Add metadata to tests (slow, integration, smoke) for better organization. This helps in selectively running tests based on their tags.

Address Technical Debt Systematically

  • Create a test debt backlog. Track known issues with tests that need refactoring.

  • Allocate dedicated time. Set aside regular intervals for test maintenance. Remember that consistent, smaller investments in test maintenance are generally more effective than infrequent larger refactoring efforts.

  • Apply the Boy Scout Rule. Leave test code better than you found it by making small improvements each time you touch it. This practice naturally integrates refactoring into your regular workflow, steadily improving the test codebase with minimal risk while eliminating the need for dedicated refactoring sessions.

Use Tooling to Your Advantage

  • Leverage static analysis. Use tools to identify test code smells. Static analysis serves as an automated "first pass" to identify where your test refactoring efforts should focus, making the process more systematic and less dependent on individual developer intuition. Tools like SonarQube or ESLint can help identify issues such as duplicate code, unused variables, and other code smells.

  • Implement mutation testing. Mutation testing improves test automation quality by deliberately introducing small code defects ("mutations") and verifying your tests detect them, revealing weak assertions and blind spots that traditional coverage metrics miss and helping prioritize meaningful test improvements. Tools such as Stryker Mutator can help automate this process by generating mutations and running your tests against them.

However, mutation testing can be resource-intensive and may require careful configuration to avoid false positives.

  • Track test metrics. Monitor test execution times and failure rates to identify refactoring targets. This data-driven approach helps prioritize improvements and allocate resources effectively.

After completing your refactoring work, thoroughly validate all changes through comprehensive testing before merging them into the main codebase. Only then should you implement new features on this improved foundation, resulting in more maintainable code, clearer troubleshooting paths, and a more stable development process.

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