11 unit testing best practices

Unit testing best practices support writing unit tests that operate independently in isolation and show deterministic properties of consistency.

Good unit tests reflect test-driven development (TDD) and use mock objects and stubs to aid isolation. Best practices also support continuous integration and automated tests.

Among different types of testing, unit testing provides a near-microscopic view of a unit of code, which is the smallest individual component evaluated through software testing. The key ingredient required for proper unit testing is isolation so that unit functions can be effectively evaluated.

Benefits of unit testing include acceleration of the software development process through automation and creation of labor-cost savings by incorporating debugging early within the software development lifecycle (SDLC). Such debugging efforts support the retention of any code changes made during development and enhance code quality throughout.

Unit testing frameworks help testers run tests on individual units and build a stronger overall codebase. Test passes occur when a test checks a particular piece of code and finds that the test executes properly, and any associated checks (also called assertions) have all been successfully realized. Test passes indicate that the unit is behaving as expected.

Unit testing is a multipronged topic with various aspects that require description. One of these areas concerns dependencies. Within the context of unit testing, dependencies refer to external services or components that a unit of code needs to operate properly.

It’s important to manage such dependencies effectively to write unit tests that are dependable and maintainable (which is to say, tests that remain valid, flexible and useful in a long-term context, during the full evolution of a codebase).

With effective management of dependencies, testers build a stronger and more reliable test suite that operates with expected behavior. Developers use dependency injection to insert (or “inject”) dependency-related lines of code into a codebase.

Each testing strategy outlined here supports best practices and reflects a hands-on style of test method.

Test environments depend upon the use of mocks and stubs to foster the deep isolation required for testing.

Mock objects are effectively duplications that help testers evaluate the probable behavior of actual objects by putting simulated objects in deep isolation.

Stubs provide analysts with data about probable interactions with external dependencies, such as components, file systems and databases.

Error detection is a core part of unit testing. Testers evaluate extreme use patterns occurring near a unit’s operating parameters or boundaries. These are called edge cases and might not be readily apparent, such as in an out-of-bounds array access. Here, the tester learns that the index for itemization transcends whatever maximum allowable value exists for that index.

In such cases, the tester is often going to be forced to then refactor the code, which means to restructure the code despite its ongoing functionalities.

Continuous integration/continuous delivery (CI/CD) pipelines are critically important to the testing process because they automate testing functions.

By running CI/CD pipelines, automated unit tests can take place at any time code changes are enacted. Automated tests can detect errors early in the development process and serve to safeguard code quality.

Numerous factors influence the maintainability of tests. To be considered maintainable, test code must exhibit optimal readability, clarity throughout, and sound identification methods. In short, tests should feature high-quality production code.

They should also be written as small and focused tests that deal with specific modules. Tests should also be created with speed in mind because faster tests can be conducted more quickly and often.

If testers don’t observe proper naming conventions, it's easy for otherwise good tests to get lost in the shuffle. Test names need to be concise but contain enough phrasing to fully describe the subject, so they can be found and recalled as needed. Labeling a test as “Test-1” simply doesn’t provide enough detail on what’s being tested or why.

Building a robust codebase requires testing that imagines both positive and negative scenarios. For positive scenarios, testers need to add tests for valid inputs. For negative scenarios, testers need to anticipate unexpected or invalid inputs.

It’s also important to maintain test coverage of edge cases and boundary conditions to ensure that your code is flexible enough to handle all types of situations.

Tests should follow standard test patterns, like the well established Arrange-Act-Assert (AAA) pattern.

The AAA pattern calls for organizing and preparing the code in a unit test, then conducting whatever step is needed to conduct the test. Finally, it involves assessing the test cases to see whether they have generated expected test results.

How much code is testable? That amount’s going to vary according to your organization’s specific circumstances. However, when testing’s the goal, it’s good to aim as high as realistically and feasibly possible.

Testers should attempt test coverage somewhere in the 70–80% range and ensure the regular frequency of tests.

Testing should be conducted in a clean testing environment. This means testers should follow teardown procedures related to restoring a system after testing has been concluded.

Typical teardown actions might require testers to delete temporary files, change global variables or shut down database connections. Otherwise, it’s easy for test fails to occur because of existing bits of stray code tripping up future tests.

When planning unit tests, keep in mind the type of usage your code is going to have. The public interface also requires testing, as do any public properties or methods within the code.

To retain focus, it’s better to limit testing implementation to those details that are part of the public application programming interface (API).

There’s a marked difference between the functionality of the code being tested and whatever underlying business rules might be in effect for that system. The testing being conducted should evaluate code functionality alone.

Developers have various tools available for use in unit testing. Here are the most popularly used:

• Jest: A preferred choice with both versed developers and novices (who appreciate its ease of use), Jest’s framework analyzes React and JavaScript components. It seeks to provide a zero-configuration testing experience with minimal setup time and fast test creation. Another plus is the way Jest reports test coverage and assesses the total amount of code that’s subject to validation.

• JUnit: When Java™components need evaluation, testers usually choose JUnit. JUnit provides optimal code organization, more versatile code repair and better error detection. For outfits that prize versatility, JUnit delivers in abundance. Not only does it streamline the testing process, but it can also be used during integration testing and functional testing of the whole system.

• Pytest: Pytest handily administers the writing and executing of tests built around the Python programming language. Pytest can also be used in unit testing, integration testing, functional testing and end-to-end testing. And it’s recognized for offering built-in support for test parameterization, so you can run the same test with different variables—without code duplication.

• xUnit: Developers working in the C# programing language usually use the popular open source unit-testing framework xUnit. Developers prize its testing environment as perfect for generating the type of deep isolation needed for testing components. It even works well with other testing tools to help foster seamless operating workflows. xUnit’s syntax aids in simplifying test creation.

It’s now universally understood that all computing is now in a state of transition, being revolutionized by the processing power of artificial intelligence (AI). Unit testing is realizing its own benefits because of AI:

• Comprehensive test coverage: The most important aspect of unit testing is error detection, and AI can find errors that human testers might miss. In addition, AI can create “self-healing” tests that learn over time. That is a major development.

• Ramped-up test writing: Testers base production environments on situations that are often fluid and whose needs are subject to rapid change. Fortunately, AI can achieve complicated things quickly, like developing whole suites of unit tests to keep development teams on schedule.

• Constant feedback: One of the perks of AI use is the way that it smooths and strengthens the use of development environments, not to mention DevOps and CI/CD pipelines. The immediate payoff for testers is the continuous feedback they’re able to get, which in turn enables faster development cycles.

• Specialized test analysis: With AI, testers have a lot more leeway in terms of what kinds of tests they can run. For example, AI can perform root cause analysis to assess root causes of test failures. Similarly, AI can conduct more complicated tests, like predictive test failure analysis, by using code patterns and historical data to forecast future test failures.