Unit Testing Best Practices

 

There are several sorts of automatic testing on the market: front-end testing, smoke testing, load testing, end-to-end (E2E) testing, and that's to name just a couple. If you would like to design a sound testing strategy with the greatest possible ROI--and that does not want that? It is vital to understand each one of these kinds of testing, studying the advantages and disadvantages of each individual and which scenario they match best.

Unit testing is one of the most valuable kinds of automated testing. Getting started with it is not the easiest thing, though. In today's article , we discuss nine best practices to help you not fall into the same trap. When examples are applicable, those will be in JavaScript, however, the hints themselves are clear and applicable to the majority of programming languages.

What's a Unit Test?

Unit testing is one of many different kinds of automatic testing. Unit tests exercise very tiny parts of the program in complete isolation, comparing their actual behavior with the expected behavior. The"total isolation" part means that, when unit testing, you do not typically connect your program using external dependencies like databases, the filesystem, or even HTTP services. That allows unit checks to be fast and much more stable because they will not fail due to issues with these outside providers.

Unit evaluations, rather than being codeless evaluations, are created with code. It's possible to think of unit tests as little programs that work out your application, interacting with tiny portions of it. Each unit test is similar to a specification or illustration of how that very small portion--i.e. a device --behaves under a specific scenario. By executing the package of tests, programmers can get immediate feedback when they change some codebase.

The Advantages of Unit Tests

What follows is a non-comprehensive list of the advantages you get from adopting unit testing:

Unit tests help you find and fix bugs sooner. Organizations that integrate unit testing into their development process and start testing as soon as possible in the lifecycle are able to detect and fix issues sooner.

Your suite of unit tests becomes a safety net for programmers. An extensive suite of unit tests can act as a safety net for developers. By often running the tests, they could assure their recent modifications to the code have not broken anything. To put it differently, unit tests help prevent regressions.

Unit tests can contribute to greater code quality. This merchandise is a pure effect of the prior one. Since unit tests act as a safety net, programmers become more confident when altering the code. They could refactor the code without any fear of breaking things, driving the general quality of the codebase up.

Unit evaluations might bring about better application architecture. We are going to return to this later. For now, know that if you can add unit tests easily to a codebase, that's usually a fantastic sign concerning the caliber of the program's architecture. So, the push to write testable code may be an incentive to get greater architecture. This is why using TDD (test-driven development) can be so effective.

Unit tests can act as documentation. Unit tests are examples of how to use the code under test. So, you can also consider these as executable specifications or instruction.

Detect code scents in your codebase. If ease of adding unit tests to a codebase is a good indication, the reverse is also correct. With difficulty creating unit tests for any particular piece of code might be a indication of code smells in the code--e.g. functions that are too complex.

How to Attain Testable Code

We say that a specific piece of code is testable when it's easy to check with unit tests. On the other hand, a given piece of code is untreatable if it's difficult or impossible to include component tests to.

So, the way to guarantee code is testable? A complete reply to this question would be well worth a post of its own. A brief answer is simply: prevent the enemies of testability.

To resist side effects and no determinism, adopt pure functions. Far smaller and more concentrated modules over gigantic ones which do too much. Maintain cyclomatic complexity at bay.