Quality Code: Testing Legacy and Scalable Systems

From zero to mastering testing strategies, from legacy code tests to ensuring scalability software.

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Shipping High quality software that stays that way is a hard task. It’s easy to fall into the trap of writing code that works now but becomes a burden as your application grows.

To maintain consistent, high-quality code, you need to build the foundation that prioritizes testing, and code quality.

This guide offers practical steps to write high-quality tests by focusing on testing strategies, design principles, techniques to make your software scalable and maintainable, and tips for working with legacy code.

The Testing Pyramid: A Balanced Approach

Before diving into specific testing practices, let me start by introducing the Testing Pyramid.

This model helps us understand the importance of different types of tests at different levels:

1. Unit Tests: These test individual functions or components in isolation. They should be precise, small and cover the smallest units of code.

2. Integration Tests: These test how different modules or components interact with each other.

3. End-to-End (E2E) Tests: These simulate real user workflows to test the full system from start to finish. While they are comprehensive, they should be limited to critical paths.

The goal is to have a strong base of unit tests, a solid middle layer of integration tests, and a minimal set of E2E tests to cover critical features. Each level of the pyramid handles prevent a different set of issues that could happen. It helps in ensuring that your application is covered without being over-tested.The next step is to ensure your code is designed to be testable

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Writing Testable Code: Principles and Examples

Good testing start with writing testable code. The easier it is to isolate and test your code, the more confident you’ll be in its correctness.

Here are some principles to follow:

1. Separation of Concerns: Each module should have a single responsibility.

2. Dependency Injection: Pass dependencies into functions rather than creating them inside.

3. Avoid Side Effects: Write pure functions wherever possible.

4. Modular Design: Break large functions into smaller, reusable ones.

Designing our code for tests

Let’s take a look at a real-world example of how refactoring code for testability can lead to more maintainable and testable components.

Before Refactoring:
In this example, the code directly handles business logic, sends emails, and calculates totals in one function.

function processOrder(order) {
  const discount = order.amount > 100 ? 0.1 : 0;
  const total = order.amount - order.amount * discount;
  sendEmail(order.email, `Total: $${total}`);
}

This function is difficult to test because it directly calls sendEmail. Also, the logic for calculating the discount and total is mixed into one function.

After Refactoring:
We can separate the concerns, making each function independently testable.

function calculateDiscount(amount) {
  return amount > 100 ? 0.1 : 0;
}

function calculateTotal(order) {
  const discount = calculateDiscount(order.amount);
  return order.amount - order.amount * discount;
}

function processOrder(order, emailSender) {
  const total = calculateTotal(order);
  emailSender(order.email, `Total: $${total}`);
}

Now, calculateDiscount, calculateTotal, and processOrder are all independent and can be tested individually. This makes the code easier to maintain and scale.

Now let’s explore tools to implement your tests effectively.

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Jest : A Quick intro

Now that we have the testing pyramid in mind, and are able to write testable code, that is easy to refactor. let’s look at Jest, one of the most popular testing libraries.

Jest makes it easy to start testing your JavaScript code with powerful features such as mocking, snapshot testing, and more.

Core Concepts in Jest

Test Suites and Test Cases:

• A test suite groups related test cases in a single file.

• A test case verifies a specific piece of functionality.

Matchers: Methods used to validate the results of your functions.

• toBe(): Strict equality check.

• toEqual(): Deep equality check.

• toHaveBeenCalled(): Ensures a mock function was called.

Mocking with Jest

Mocks allow us to simulate external dependencies and isolate the functionality we want to test. This is especially useful when testing code that interacts with APIs, databases, or external services.

const mockApiClient = jest.fn().mockResolvedValue({ data: { temp: 25 } });

async function fetchWeatherData(apiClient) {
  return apiClient();
}

// Test
test('fetchWeatherData calls API and returns temperature', async () => {
  const data = await fetchWeatherData(mockApiClient);
  expect(mockApiClient).toHaveBeenCalled();
  expect(data.temp).toBe(25);
});

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Handling legacy code

Testing legacy code can be intimidating, but it’s a critical step in maintaining and improving it. Here’s how to approach it effectively:

1. Identify Risky Areas

Start by identifying parts of the codebase that are most likely to fail or have the greatest business impact. Focus your testing efforts there first.

2. Characterization Tests

Characterization tests capture the current behavior of the code. They serve as a baseline to ensure that changes don’t introduce regressions.

// Legacy function
function calculateTotalLegacy(amount) {
  return amount * 1.2; // Includes tax
}

// Characterization test
test('calculateTotalLegacy returns correct total with tax', () => {
  expect(calculateTotalLegacy(100)).toBe(120);
});

3. Refactor for Testability

Refactor small, isolated pieces of the code to make them testable. For example, extract complex logic into separate functions.

4. Handling Legacy Code Isn’t always Possible
When direct testing is not feasible (for example, if the legacy code is too tightly coupled or involves external dependencies that are hard to mock), here are a few approaches:

• Integration Tests: Start by writing integration tests that cover critical paths. These tests can help capture the behavior of interconnected systems, even when individual units are hard to isolate.

• Refactoring in Stages: If direct testing isn’t possible due to complex legacy code, consider refactoring the code in stages. Break down large functions or components into smaller, testable parts. Test each small part incrementally as you refactor, ensuring that each change doesn’t introduce new issues. The proxy and strangler pattern can be great here.

• Don’t Aim for high testing coverage, rather focus on critical areas

5. Use Tools for Safety

Tools like code coverage analysis and static analysis tools can help identify gaps in your tests and risky dependencies.

6. Balance Tests and Development

While testing is crucial, balance it with your project deadlines. Focus on high-risk areas when time is limited

7. Utilize The Proxy Pattern

The Proxy Pattern can be useful in testing legacy code by isolating external dependencies and making components more testable.

Here’s how it helps:

1. Isolate Dependencies for Testing Legacy systems often rely on tightly coupled external services (e.g., databases, APIs). Using a proxy, you can simulate these dependencies, making it easier to test the logic in isolation.

Example:

class PaymentGatewayProxy {
  process(order) {
    return order.amount > 1000 ? { success: true } : { success: false };
  }
}

In tests, you can replace the real payment gateway with this proxy, avoiding external calls while testing.

2. Mock Behavior for Complex Operations: Proxies allow you to mock complex, slow, or resource-intensive operations (e.g., network requests, file I/O), speeding up tests.

Example:

class FileSystemProxy {
  constructor(realFileSystem) {
    this.realFileSystem = realFileSystem;
    this.cache = {};  // Cache results
  }

readFile(filePath) {
    if (this.cache[filePath]) return this.cache[filePath];
    const content = this.realFileSystem.readFile(filePath);
    this.cache[filePath] = content;
    return content;
  }
}

This proxy helps avoid hitting the actual file system during tests.

3. Incremental Refactoring: The Proxy Pattern allows you to refactor legacy code incrementally. You can introduce proxies to abstract complex or tightly coupled components, making them easier to test while refactoring in stages.

Example:

class LegacyServiceProxy {
  method() {
    // Proxy delegates to refactored, testable components
    return new RefactoredService().method();
  }
}

This makes testing easier and reduces the risk of breaking functionality during refactoring.

Recap:

• Isolate Dependencies: Simulate external services for isolated testing.

• Mock Expensive Operations: Avoid costly operations (e.g., file I/O) during tests.

• Incremental Refactoring: Introduce proxies to refactor legacy code without disrupting tests.

By using the Proxy Pattern, you make legacy systems more testable, ensuring that tests are focused, efficient, and easier to maintain.

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Testing for Scalability

High-quality code isn’t just about correctness — it’s also about ensuring your code can handle increased complexity and growth over time, without introducing regressions or performance issues. Here’s how to integrate scalability with testing practices:

1. Test for Growth
   As your codebase grows, so do the number of edge cases and interactions between components. Write tests that anticipate how your system will handle increased load and complexity.
   Example: Write tests that simulate large datasets or increased user activity. This helps ensure that performance remains optimal under growing demand, especially when integrating new features.

2. Optimize and Test for Performance
   Scalability also involves ensuring your code performs well as it expands. Performance testing helps identify bottlenecks before they become issues in production.
   Example: Use load testing tools like Jest or other performance profiling tools to test how your application handles large volumes of data, such as stress-testing database queries or large API responses.

3. Test Caching and Data Access Patterns
   Caching is essential for scaling, but you need to ensure that your caching strategy doesn’t introduce bugs or inconsistencies. Write tests to validate that your caching mechanism works as expected.
   Example: Write unit and integration tests that check if cached data is updated correctly, ensuring no stale data is returned, and validate that caching doesn’t cause conflicts in concurrent requests.

4. Database Queries and Test Coverage
   With the increase in data, the performance of your database queries becomes critical. Ensure you’re writing tests that validate query optimization and indexing.
   Example: Write integration tests that simulate large query responses and measure their performance, ensuring that the indexes and database optimizations handle large-scale operations efficiently.

5. Modular Testing with Scalable Architectures
   In a microservices or serverless architecture, each service must be independently tested to ensure they can scale individually. Create modular test suites that can scale with your application’s growth.
   Example: Develop end-to-end tests that cover each microservice and ensure they function in isolation, as well as in combination with other services, without creating performance bottlenecks.

By incorporating these scalability-focused testing practices, you ensure that your codebase remains maintainable, high-performing, and robust as it grows. Always test for scalability, not just functionality, to deliver quality code that stands the test of time.

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Final Thoughts

Testing is a critical practice for building high-quality software that lasts. writing tests improves your development process by:

Building Confidence in Code:
Tests act as a safety net, enabling you to make changes without fear of introducing bugs. For example, when adding a feature to apply discounts based on order amounts, writing tests for edge cases ensures the code behaves as expected under various conditions.

Encouraging Better Design:
Writing tests forces you to simplify and modularize your code, leading to better overall architecture and maintainability.

Accelerating Debugging:
Tests help identify and fix bugs early. For instance, if a negative amount is accidentally passed into the applyDiscount function, a test will catch it immediately, saving you hours of debugging later.

Supporting Refactoring:
Comprehensive tests give you the confidence to refactor. With tests in place, you can safely change or restructure parts of your code without worrying about breaking existing functionality.