What is unit testing?

Unit testing involves isolating units so that functionality can be confirmed before units are integrated with other parts of the application. 

A unit testing framework offers both immediate and long-range benefits. In the short term, unit tests facilitate a quicker development process by allowing for automated testing. Over the long haul, unit testing yields savings in labor costs because less debugging is needed later in the software development lifecycle (SDLC), when those costs are apt to be considerably higher.

The reason less debugging is required is due to the enhanced code quality that unit testing supports. Unit testing encourages preemptive and vigilant error detection, all of which occurs much earlier in the development process. By concentrating on individual units, testers can focus on “run units,” which are the individual pieces of code or lines of code being evaluated.

The ultimate effect is building a stronger codebase where code changes are defined and made securely and earlier during software testing, thus replacing early and outdated legacy code that might remain.

Of all types of testing, unit testing can be considered the purest example of a “shift-left” discipline. The goal of shift-left test methods is to relocate certain parts of software testing to earlier within the SDLC, based on an envisioned project timeline that moves sequentially from left to right.

So, if a tester tinkers with the smallest parts of the source code, that’s working at the project’s most basic level, placing it in the project timeline far left. In fact, unit testing can be so far shift-left it begins before any actual software engineering is conducted. One aspect of unit testing is that it pushes software developers to contemplate potential unit problems and address them mentally while in early design stages.

Within the software testing arena, there are several types of testing that seem to share certain properties and functionality.

For example, it’s easy to see why there can be some occasional confusion between unit testing and simple tests. By their wording, it sounds as though the two terms share similar meanings, and we know that unit tests are focused on simple pieces of code. But while unit testing is relegated to testing fundamental pieces of code, simple tests—despite their name—can be considerably broader and more complex.

Simple tests can also be used for various purposes, such as integration testing (to see how well components function together). Simple tests can even be used for conducting end-to-end testing (to gauge total system performance). The key difference involves the testing environment for each. Unit testing strives to test code in isolation, while simple tests might or might not do so.

Fortunately, there’s considerably less ambiguity with other testing types. For example, acceptance testing, which analyzes an entire software system and how effectively it appears to meet business expectations and satisfy user requirements. Acceptance testing occurs late in the SDLC, right after regression testing (which ensures code changes are not inducing errors to functionality) and before system deployment.

Usually, the most significant difference between unit testing and other testing types is their location within the SDLC. Unit testing needs to occur early in that lifecycle. The other key difference involves whether code is being checked in isolation.

There are five broadly acknowledged steps to unit testing, which must be handled sequentially.

Here, the tester is choosing the unit test code to be analyzed, which might be a function, class or method.

The next choice involves the type of testing to be implemented, whether that’s manual testing or automated unit testing through one of many possible frameworks.

In preparation for the actual unit testing, the tester needs to ensure that the test environment meets all requirements to execute tests, including test data, dependencies and mock objects. It’s essential to use an integrated development environment (IDE) at this point.

The IDE is a software app that can be thought of as a kind of multipurpose Swiss army knife, containing all the tools necessary for writing, building, testing and debugging code. IDEs foster the creation and execution of unit tests.

The tester selects a unit testing framework and writes the test cases to be used. During the development and execution of the tests, a compiler converts tests written in programming languages into executable code. After conducting the test cases, the tester confirms the test results.

Finally, one after-step remains. Should any of the test cases fail, the tester needs to debug the code and confirm its root cause. Then, the issue should be repaired. After that, the tester needs to run unit tests again to make sure any bugs in the code have been remediated.

When developers are writing tests and running tests, they have various testing tools available to them, depending on their specific needs:

• Jest: JavaScript’s framework for testing JavaScript and React components. One of Jest’s attributes is the useful way that it reports code coverage, including the percentage of total code that’s being evaluated. Another is its focus on delivering a “zero-configuration” testing experience in which setup time is minimized and developers are free to begin writing tests as quickly as they want. Jest is considered simple to use and welcoming to developers.

• JUnit: JUnit is the Java™ framework for testing Java components. The advantages of using JUnit include better code organization, more thorough error detection and stronger code repair. Beyond that, JUnit is known to help improve software quality and streamline the testing process. And although JUnit is primarily used for unit testing, it can also be used for integration testing and functional testing (of the entire system).

• Mocha: Mocha is an open source framework for testing JavaScript code. Mocha enables test automation and test execution based on a structure of “tests” and “test suites”—its organizational tools for establishing and grouping tests. Mocha’s framework is considered versatile and can be adapted for various testing needs. Another Mocha advantage is thorough reporting of test results, so developers can detect test fails and start debugging efforts.

• NUnit: NUnit is another open source testing framework, designed to work with the .NET platform and its associated languages (like C#, VB.NET and F#). It offers unit testing based on test attributes that establish test methods and work with setup code before tests and cleanup code after tests. NUnit supplies various assertion methods to help verify anticipated code behaviors and uses the NUnit console runner for batch execution of tests.

• Pytest: Pytest is a framework for writing and executing Python tests. Its versatility is shown in its usage in unit testing, integration testing, end-to-end testing and functional testing. Its prime benefit is the built-in support that it provides for test parameterization, allowing you to run the same test but with different configurations or inputs, without having to duplicate test code. It also supports easy mocking and patching (temporarily substituting a mock object for a real object, function or method), including creating mock objects for testing purposes.

• xUnit: Another popular open source unit testing framework, xUnit, is typically used for development related to the C# programming language. xUnit was designed expressly for unit testing purposes, so it excels at providing an isolated code-running environment for testing components. xUnit’s also prized for its intuitive and easy-to-grasp syntax, which simplifies test creation. Further, it integrates well with other testing tools for a seamless operating workflow.

Unit testing represents a deeply engaged and hands-on approach to testing, as these testing strategies illustrate.

As with all computing, artificial intelligence (AI) is bringing powerful new velocity and other benefits to unit testing. Here are some examples of how AI is now revolutionizing unit testing: 

• Faster test writing: AI can create entire suites of unit tests faster than its human counterparts, which can help keep development teams on track to complete needed testing without negatively impacting their product release cycles.

• Better test coverage: AI does a masterful job of detecting edge cases that human testers might overlook. However, AI’s most stunning trick might be its capacity to generate “self-healing” tests that can learn from code-change patterns, so tests remain relevant over time.

• Advanced test analysis: AI unlocks the ability to run complicated tests, like predictive test failure analysis, which uses historical data and code patterns to pinpoint looming test failures. Similarly, AI enables root cause analysis to identify the root causes of test failures.

• Continuous feedback: With AI pushing unit testing forward, it’s easier to achieve strong integration with development environments as well as DevOps and CI/CD pipelines. With that integration, testers can receive continuous feedback and achieve faster development cycles.