Good news! You’ve finished coding your API project. But are you truly done? As a developer, you know other people will work on the project at some point, whether to add new features or to fix bugs. How can you ensure those future changes won’t inadvertently break your code? Simple answer – tests, or more specifically test code coverage.

Test coverage is a metric that measures how much of your codebase is exercised by tests, providing insight into the effectiveness of your testing efforts. In this blog, we’ll focus on API projects, exploring the types of tests suited for code coverage, realistic goals (and tools to help achieve them), and the minimum coverage needed to reap the benefits of your tests.

Testing your code—down to individual lines—is a critical practice in modern software development. It helps ensure applications remain reliable, stable, and maintainable, all while balancing practical constraints.

Types of Tests for Code Coverage

There are various types of tests you can use to achieve code coverage. These tests are applicable in any coding environment and work together to help you reach your coverage goals. The key is to ensure that your code is not only covered but also covered with the right kinds of tests to maximize their effectiveness. Keep in mind that some lines of code may require multiple tests to achieve complete coverage and accurately reflect the robustness of your application.

Condition Coverage

Also known as predicate coverage, condition coverage tests evaluate all the conditional expressions in your code to ensure they produce the expected outcomes for every possible result (true and false). These tests can be designed as separate tests for each condition or combined into a single test that asserts each condition behaves correctly under different scenarios.

Condition coverage is particularly useful for testing complex logic within conditional statements, such as if, while, or for clauses. It’s most effective when applied to critical decision-making sections of your code where bugs could significantly impact functionality.

Tip: Focus on conditions that drive key application behavior and consider using tools that visualize conditional branches to help identify untested paths.

Integration Testing

Integration tests combine multiple components of your code to verify that they work together as expected. Unlike end-to-end testing, which focuses on the full workflow of an application, integration testing examines the interactions between specific modules or functions. These tests often involve testing parent methods that call other methods, helping to cover not only the parent methods but also the dependent code they invoke.

Integration testing is particularly effective when applied to the entry points of your application, such as public methods. From there, tests can verify functionality cascading down to private methods, provided your test setup allows access to private methods. If private methods are inaccessible, focus on covering their functionality indirectly through public interfaces.

Tip: Integration tests are most valuable when applied to high-priority or high-risk areas of your code, especially those with complex dependencies. These tests can also help identify issues early, such as incorrect data handling or mismatched expectations between modules.

Functional Coverage

Functional coverage tests ensure that your code behaves as intended by verifying its functionality against design specifications, requirements, or user stories. These tests focus on whether the implemented features meet the expected outcomes, rather than on the internal workings of the code.

Functional tests are especially useful for validating core business logic, ensuring the software delivers value to end users as described in the user stories. They also help catch discrepancies between what was requested and what was built, acting as a bridge between development and product requirements.

Tip: When writing functional tests, collaborate with stakeholders to prioritize user-centric scenarios. These tests are most impactful when they focus on the features or workflows that users rely on most.

End-to-End

End-to-end (E2E) tests simulate real-world usage of your application, ensuring that it functions correctly from start to finish. These tests typically begin at a main entry point, such as an API controller, and proceed through all the layers of your application—including method calls, business logic, and data storage—before returning results to the client.

The primary goal of E2E tests is to validate that the entire system works together as intended, covering the full client-to-server-to-client workflow. These tests are especially valuable for identifying issues that arise from integration points, such as data inconsistencies, misconfigurations, or unexpected interactions between components.

Tip: Apply E2E tests to critical workflows or user-facing features. Automate where possible to improve efficiency.

Loop Coverage

Loop coverage tests ensure the loops in your program behave correctly under all scenarios, such as executing zero, one, or multiple iterations. These tests validate whether loops handle their entry, exit, and conditions as intended.

Similar to condition coverage, loop tests can be a single comprehensive test or multiple focused tests targeting different scenarios.

Tip: Focus on edge cases and boundary values to catch potential issues in loop logic.

Regression Tests

Regression tests ensure that new code or refactored code hasn’t broken existing functionality. These tests are crucial for maintaining stability as your application evolves, helping catch unintended side effects introduced by changes.

Tip: Automate regression tests for key features to quickly identify issues during updates or releases.

Unit Test

Unit tests focus on verifying individual parts of the code, such as functions, methods, or classes, in isolation. They ensure each component performs as expected without interference from other parts of the system. APIs often have multiple units to test, and some code—like business logic or utility functions—may require extra attention to cover edge cases and critical functionality.

These tests are fast, easy to automate, and provide quick feedback during development, forming the foundation of a reliable testing strategy. By isolating and thoroughly testing key areas, unit tests help catch bugs early and improve the maintainability of your codebase.

Tip: Write clear, descriptive test names to make it easy to understand what behavior is being validated.

Bonus: Labeling Tests

When writing your tests, make sure to label them. Clear labeling will make them easier to run, allowing you to see what’s covered at a glance. Some examples are below.

This example is of just the Integration Tests on one API project. You can see the numbers of lines covered, partially covered, and not covered code and the total percent of covered code.

If you use Visual Studio, it will color-coordinate the lines of code covered. Different colors mean different things, like full coverage or partial coverage. These colors can be changed in the settings if you’d like.

Realistic Test Code Coverage Goals

Achieving 100% test code coverage may not be practical or necessary for efficient testing—don’t hold that against me! This doesn’t mean you shouldn’t aim high. Striving for 100% coverage is admirable, but practical constraints like time, resources, and project priorities often make it unrealistic.

At the client I’m currently working with, we set a minimum goal of 80–90% test coverage for API projects. This range strikes a balance, ensuring critical methods are thoroughly tested while leaving room to adjust and expand tests as the development lifecycle progresses. Hitting this benchmark means we cover the most essential parts of the code without getting bogged down in diminishing returns.

Let’s think back to the example above (which was just Integration Tests). Analyzing the coverage results often shows we meet or exceed the 80–90% criteria. This approach allows us to maintain high reliability while managing our resources effectively.

Before setting a target for code coverage, it’s essential to plan which parts of the code need the most thorough testing. Test coverage for an API project is a dynamic process that evolves. Code updates—whether adding, removing, or refactoring—will cause the coverage percentage to fluctuate.

Keep in mind that adding tests doesn’t always increase the overall coverage percentage. New tests should focus on covering the lines or conditions introduced by recent code changes. This ensures that test coverage remains meaningful and adapts to the evolving needs of the project.

Tools for Test Coverage

To help keep API code coverage within the 80 to 90% rule, there are many tools and automations to use. Many factors go into keeping code covered – such as the resources used and the amount of time allocated for the project to be done. Here are some tools to use to help keep code coverage up.

Testing frameworks like Jest, TestNG, JUnit, and NUnit provide a great foundation for writing tests against API code. They help organize tests in a structured way and allow for asserting that the code behaves as expected. Using these frameworks ensures that tests are easy to maintain and scale as the project grows.

API testing tools like Postman and SoapUI are essential for testing and validating API functionality. Postman allows you to create and execute API requests with ease, providing a user-friendly interface for testing various endpoints.

SoapUI, on the other hand, is designed to test both SOAP and RESTful APIs, making it ideal for more complex testing scenarios. Both of these tools streamline the testing process and help ensure your APIs function as intended.

Mocking libraries are crucial in API projects as they allow developers to simulate code or external systems that are outside the scope of the current test. By using mocks, developers can isolate the code being tested and control its behavior. Popular mocking libraries include Mockito, Sinon, and Moq, each offering different capabilities for simulating objects and functions. For example, Moq can be used to mock method calls and specify what should be returned when they are invoked, helping ensure that tests focus solely on the functionality being tested.

Using these tools and plugins will make teams more effective at measuring, tracking, and improving API code test coverage. This approach not only enhances test coverage but also leads to increased efficiency, smoother workflows, and overall improved code quality.

Bare Minimum Test Coverage

​​While the ideal test coverage range is 80-90%, it’s important to consider the minimum level that still provides useful benefits. A common minimum benchmark is 70% code coverage, which can still detect bugs, ensure code maintainability, and maintain developer confidence. Although not all paths may be covered, key entry points, critical code, and major functionality should be included in this coverage.

However, it’s crucial to understand that code coverage alone doesn’t guarantee a bug-free API project. Even when adhering to the 70% minimum, the quality of the tests matters. Well-written tests help ensure that new or refactored code performs as expected and that client requirements are met. By focusing on quality tests and prioritizing essential areas, teams can maintain the effectiveness of their test coverage.

• Setting Clear Goals
  • Define what the test code coverage should be at
  • Discuss with the team the minimum line of coverage that is expected
  • Inform the team of what types of tests need to be used when writing code
• Test Driven Development or Unit Tests
  • TDD is used for the type of development where the tests are written before the code is written, ensuring that the test coverage is there
  • Unit tests are done before, during, or after the code is written
  • Both these types of tests can be automated and run each time the code changes
• Test Code Coverage Tools
  • Utilizing tools that inform the team of code coverage will help maintain the code coverage for the project
  • These tools can be used as check-in gates for code or give alerts to the developers if coverage is not sufficient

The 70% minimum is not set in stone by any means—it will vary depending on the project’s nature and the company’s standards. By setting clear coverage goals and teaching the team to follow them, you ensure that both old and new functionality remain intact, unaffected by code or data changes.

Teams that prioritize testing often find that their code experiences fewer bugs and less frequent breaks over time. While 100% coverage isn’t necessary, covering critical and high-traffic code paths is essential to maintain the benefits of test coverage.

It’s important to note that there is a baseline for effective testing—having thousands of lines of code but only testing a small fraction won’t provide much value, except to claim that you have “tests.”

Wrapping Up

In conclusion, code test coverage is essential for ensuring the quality and reliability of an API project. While it’s ideal to aim for high coverage, time constraints often make focused, well-defined tests critical. The different types of coverage help pinpoint areas that may need additional testing or refactoring.

Having a solid testing framework gives developers confidence, knowing their changes won’t unintentionally break existing functionality. With various tests at your disposal, achieving the practical minimum of 70% code coverage is achievable. Keeping coverage at this level will help with bug detection, maintainability, and developer confidence. Once your coverage is solid, you can confidently check in your code to the repository—mission accomplished!

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