write a test method using the unit of work

About 45 mins

Learning Objectives

Apex unit tests, code coverage requirement for deployment, test method syntax, unit test example: test the temperatureconverter class, increase your code coverage, create and execute a test suite, create test data, tell me more, get ready for the hands-on challenge.

• Challenge +500 points

Get Started with Apex Unit Tests

After completing this unit, you’ll be able to:

• Describe the key benefits of Apex unit tests.
• Define a class with test methods.
• Execute all test methods in a class and inspect failures.
• Create and execute a suite of test classes.

The Apex testing framework enables you to write and execute tests for your Apex classes and triggers on the Lightning Platform. Apex unit tests ensure high quality for your Apex code and let you meet requirements for deploying Apex.

Testing is the key to successful long-term development and is a critical component of the development process. The Apex testing framework makes it easy to test your Apex code. Apex code can only be written in a sandbox environment or a Developer org, not in production. Apex code can be deployed to a production org from a sandbox. Also, app developers can distribute Apex code to customers from their Developer orgs by uploading packages to the Lightning Platform AppExchange. In addition to being critical for quality assurance, Apex unit tests are also requirements for deploying and distributing Apex. 

These are the benefits of Apex unit tests.

• Ensuring that your Apex classes and triggers work as expected
• Having a suite of regression tests that can be rerun every time classes and triggers are updated to ensure that future updates you make to your app don’t break existing functionality
• Meeting the code coverage requirements for deploying Apex to production or distributing Apex to customers via packages
• High-quality apps delivered to the production org, which makes production users more productive
• High-quality apps delivered to package subscribers, which increase your customers trust

Before each major service upgrade, Salesforce runs all Apex tests on your behalf through a process called Apex Hammer. The Hammer process runs in the current version and next release and compares the test results. This process ensures that the behavior in your custom code hasn’t been altered as a result of service upgrades. The Hammer process picks orgs selectively and doesn’t run in all orgs. Issues found are triaged based on certain criteria. Salesforce strives to fix all issues found before each new release.

Maintaining the security of your data is our highest priority. We don't view or modify any data in your org, and all testing is done in a copy that runs in a secure data center.

Before you can deploy your code or package it for the Lightning Platform AppExchange, at least 75% of Apex code must be covered by tests, and all those tests must pass. In addition, each trigger must have some coverage. Even though code coverage is a requirement for deployment, don’t write tests only to meet this requirement. Make sure to test the common use cases in your app, including positive and negative test cases, and bulk and single-record processing.

Test methods are defined using the @isTest annotation and have the following syntax:

The @isTest annotation takes multiple modifiers within parentheses and separated by blanks. We’ll cover one such parameter later.

The visibility of a test method doesn’t matter, so declaring a test method as public or private doesn’t make a difference as the testing framework is always able to access test methods. For this reason, the access modifiers are omitted in the syntax.

Test methods must be defined in test classes, which are classes annotated with @isTest . This sample class shows a definition of a test class with one test method.

Test classes can be either private or public. If you’re using a test class for unit testing only, declare it as private. Public test classes are typically used for test data factory classes, which are covered later.

The following simple example is of a test class with three test methods. The class method that’s being tested takes a temperature in Fahrenheit as an input. It converts this temperature to Celsius and returns the converted result. Let’s add the custom class and its test class.

• In the Developer Console, click File | New | Apex Class , and enter TemperatureConverter for the class name, and then click OK .
• Replace the default class body with the following. public class TemperatureConverter { // Takes a Fahrenheit temperature and returns the Celsius equivalent. public static Decimal FahrenheitToCelsius(Decimal fh) { Decimal cs = (fh - 32) * 5/9; return cs.setScale(2); } }
• Press Ctrl+S to save your class.
• Repeat the previous steps to create the TemperatureConverterTest class. Add the following for this class. @isTest private class TemperatureConverterTest { @isTest static void testWarmTemp() { Decimal celsius = TemperatureConverter.FahrenheitToCelsius(70); System.assertEquals(21.11,celsius); } @isTest static void testFreezingPoint() { Decimal celsius = TemperatureConverter.FahrenheitToCelsius(32); System.assertEquals(0,celsius); } @isTest static void testBoilingPoint() { Decimal celsius = TemperatureConverter.FahrenheitToCelsius(212); System.assertEquals(100,celsius,'Boiling point temperature is not expected.'); } @isTest static void testNegativeTemp() { Decimal celsius = TemperatureConverter.FahrenheitToCelsius(-10); System.assertEquals(-23.33,celsius); } }

The TemperatureConverterTest test class verifies that the method works as expected by calling it with different inputs for the temperature in Fahrenheit. Each test method verifies one type of input: a warm temperature, the freezing point temperature, the boiling point temperature, and a negative temperature. The verifications are done by calling the System.assertEquals() method, which takes two parameters: the first is the expected value, and the second is the actual value. There is another version of this method that takes a third parameter—a string that describes the comparison being done, which is used in testBoilingPoint() . This optional string is logged if the assertion fails.

Let’s run the methods in this class.

• In the Developer Console, click Test | New Run .
• Under Test Classes , click TemperatureConverterTest .
• To add all the test methods in the TemperatureConverterTest class to the test run, click Add Selected .
• Click Run .

After you run tests, code coverage is automatically generated for the Apex classes and triggers in the org. You can check the code coverage percentage in the Tests tab of the Developer Console. In this example, the class you’ve tested, the TemperatureConverter class, has 100% coverage, as shown in this image.

Whenever you modify your Apex code, rerun your tests to refresh code coverage results.

A known issue with the Developer Console prevents it from updating code coverage correctly when running a subset of tests. To update your code coverage results, use Test | Run All rather than Test | New Run .

While one test method would have resulted in full coverage of the TemperatureConverter class, it’s still important to test for different inputs to ensure the quality of your code. Obviously, it isn’t possible to verify every data point, but you can test for common data points and different ranges of input. For example, you can verify passing positive and negative numbers, boundary values, and invalid parameter values to verify negative behavior. The tests for the TemperatureConverter class verify common data points, like the boiling temperature, and negative temperatures.

The TemperatureConverterTest test class doesn’t cover invalid inputs or boundary conditions. Boundary conditions are about minimum and maximum values. In this case, the temperature conversion method accepts a Decimal , which can accept large numbers, higher than Double values. For invalid inputs, there is no invalid temperature but the only invalid input is null. How does the conversion method handle this value? In this case, when the Apex runtime dereferences the parameter variable to evaluate the formula, it throws a System.NullPointerException . You can modify the FahrenheitToCelsius() method to check for an invalid input and return null in that case, and then add a test to verify the invalid input behavior.

Up to this point, all tests pass because the conversion formula used in the class method is correct. But that’s boring! Let’s try to simulate a failure just to see what happens when an assertion fails. For example, let’s modify the boiling point temperature test and pass in a false expected value for the boiling point Celsius temperature (0 instead of 100). This causes the corresponding test method to fail.

• Change the testBoilingPoint() test method to the following. @isTest static void testBoilingPoint() { Decimal celsius = TemperatureConverter.FahrenheitToCelsius(212); // Simulate failure System.assertEquals(0,celsius,'Boiling point temperature is not expected.'); }
• To execute the same test run, click the latest run in the Tests tab, and then click Test | Rerun .The assertion in testBoilingPoint() fails and throws a fatal error (an AssertException that can’t be caught).
• Check the results in the Tests tab by expanding the latest test run. The test run reports one out of four tests failed. To get more details about the failure, double-click the test run. Detailed results appear in a separate tab as shown in this image.  [Alt text: Inspect results of a failed test in the Developer Console]
• To get the error message for the test failure, double-click inside the Errors column for the failed test. You’ll see the following; descriptive text next to Assertion Failed: is the text we provided in the System.assertEquals()  statement. System.AssertException: Assertion Failed: Boiling point temperature is not expected.: Expected: 0, Actual: 100.00

The test data in these test methods are numbers and not Salesforce records. You’ll find out more about how to test Salesforce records and how to set up your data in the next unit.

When writing tests, try to achieve the highest code coverage possible. Don’t just aim for 75% coverage, which is the lowest coverage that the Lightning Platform requires for deployments and packages. The more test cases that your tests cover, the higher the likelihood that your code is robust. Sometimes, even after you write test methods for all your class methods, code coverage is not at 100%. One common cause is not covering all data values for conditional code execution. For example, some data values tend to be ignored when your class method has if statements that cause different branches to be executed based on whether the evaluated condition is met. Ensure that your test methods account for these different values.

This example includes the class method, getTaskPriority() , which contains two if statements. The main task of this method is to return a priority string value based on the given lead state. The method validates the state first and returns null if the state is invalid. If the state is CA, the method returns 'High'; otherwise, it returns 'Normal' for any other state value.

The equality operator ( == ) performs case-insensitive string comparisons, so there is no need to convert the string to lower case first. This means that passing in 'ca' or 'Ca' will satisfy the equality condition with the string literal 'CA'.

This is the test class for the getTaskPriority() method. The test method simply calls getTaskPriority() with one state ('NY').

Let’s run this test class ( TaskUtilTest ) in the Developer Console and check code coverage for the corresponding TaskUtil class that this test covers. After the test run finishes, the code coverage for TaskUtil is shown as 75%. If you open this class in the Developer Console, you see six blue (covered) lines and two red (uncovered) lines, as shown in this image.

The reason why line 5 wasn’t covered is because our test class didn’t contain a test to pass an invalid state parameter. Similarly, line 11 wasn’t covered because the test method didn’t pass 'CA' as the state. Let’s add two more test methods to cover those scenarios. The following shows the full test class after adding the testTaskHighPriority() and testTaskPriorityInvalid() test methods. If you rerun this test class using Run All or New Run , the code coverage for TaskUtil is now at 100%!

A test suite is a collection of Apex test classes that you run together. For example, create a suite of tests that you run every time you prepare for a deployment or Salesforce releases a new version. Set up a test suite in the Developer Console to define a set of test classes that you execute together regularly.

You now have two test classes in your org. These two classes aren’t related, but let’s pretend for the moment that they are. Assume that there are situations when you want to run these two test classes but don’t want to run all the tests in your org. Create a test suite that contains both classes, and then execute the tests in the suite.

• In the Developer Console, select Test | New Suite .
• Enter TempConverterTaskUtilSuite for the suite name, and then click OK .
• Select TaskUtilTest , hold down the Ctrl key, and then select TemperatureConverterTest .

• Click Save .
• Select Test | New Suite Run .
• Select TempConverterTaskUtilSuite , and then click > to move TempConverterTaskUtilSuite to the Selected Test Suites column.
• Click Run Suites .
• On the Tests tab, monitor the status of your tests as they’re running. Expand the test run, and expand again until you see the list of individual tests that were run. Like in a run of individual test methods, you can double-click method names to see detailed test results.

Salesforce records that are created in test methods aren’t committed to the database. They’re rolled back when the test finishes execution. This rollback behavior is handy for testing because you don’t have to clean up your test data after the test executes.

By default, Apex tests don’t have access to pre-existing data in the org, except for access to setup and metadata objects, such as the User or Profile objects. Set up test data for your tests. Creating test data makes your tests more robust and prevents failures that are caused by missing or changed data in the org. You can create test data directly in your test method, or by using a utility test class as you’ll find out later.

Even though it is not a best practice to do so, there are times when a test method needs access to pre-existing data. To access org data, annotate the test method with @isTest(SeeAllData=true) . The test method examples in this unit don’t access org data and therefore don’t use the SeeAllData parameter.

• You can use Salesforce Apex Extension for Visual Studio Code to run Apex tests and verify the functionality of your code.
• You can save up to 6 MB of Apex code in each org. Test classes annotated with @isTest don’t count toward this limit.
• Even though test data rolls back, no separate database is used for testing. As a result, for some sObjects that have fields with unique constraints, inserting duplicate sObject records results in an error.
• Test methods don’t send emails.
• Test methods can’t make callouts to external services. You can use mock callouts in tests.
• SOSL searches performed in a test return empty results. To ensure predictable results, use Test.setFixedSearchResults() to define the records to be returned by the search.
• Apex Developer Guide : Testing Best Practices
• Apex Developer Guide : What Are Apex Unit Tests?
• Apex Developer Guide : Isolation of Test Data from Organization Data in Unit Tests
• Salesforce Help : Checking Code Coverage

In order to complete the hands-on challenge for this unit, you’ll need to create a new Apex class named VerifyDate with the code copied from below:

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Unit testing best practices with .NET Core and .NET Standard

• 18 contributors

There are numerous benefits of writing unit tests; they help with regression, provide documentation, and facilitate good design. However, hard to read and brittle unit tests can wreak havoc on your code base. This article describes some best practices regarding unit test design for your .NET Core and .NET Standard projects.

In this guide, you learn some best practices when writing unit tests to keep your tests resilient and easy to understand.

By John Reese with special thanks to Roy Osherove

Why unit test?

There are several reasons to use unit tests.

Less time performing functional tests

Functional tests are expensive. They typically involve opening up the application and performing a series of steps that you (or someone else) must follow in order to validate the expected behavior. These steps might not always be known to the tester. They'll have to reach out to someone more knowledgeable in the area in order to carry out the test. Testing itself could take seconds for trivial changes, or minutes for larger changes. Lastly, this process must be repeated for every change that you make in the system.

Unit tests, on the other hand, take milliseconds, can be run at the press of a button, and don't necessarily require any knowledge of the system at large. Whether or not the test passes or fails is up to the test runner, not the individual.

Protection against regression

Regression defects are defects that are introduced when a change is made to the application. It's common for testers to not only test their new feature but also test features that existed beforehand in order to verify that previously implemented features still function as expected.

With unit testing, it's possible to rerun your entire suite of tests after every build or even after you change a line of code. Giving you confidence that your new code doesn't break existing functionality.

Executable documentation

It might not always be obvious what a particular method does or how it behaves given a certain input. You might ask yourself: How does this method behave if I pass it a blank string? Null?

When you have a suite of well-named unit tests, each test should be able to clearly explain the expected output for a given input. In addition, it should be able to verify that it actually works.

Less coupled code

When code is tightly coupled, it can be difficult to unit test. Without creating unit tests for the code that you're writing, coupling might be less apparent.

Writing tests for your code will naturally decouple your code, because it would be more difficult to test otherwise.

Characteristics of a good unit test

• Fast : It isn't uncommon for mature projects to have thousands of unit tests. Unit tests should take little time to run. Milliseconds.
• Isolated : Unit tests are standalone, can be run in isolation, and have no dependencies on any outside factors such as a file system or database.
• Repeatable : Running a unit test should be consistent with its results, that is, it always returns the same result if you don't change anything in between runs.
• Self-Checking : The test should be able to automatically detect if it passed or failed without any human interaction.
• Timely : A unit test shouldn't take a disproportionately long time to write compared to the code being tested. If you find testing the code taking a large amount of time compared to writing the code, consider a design that is more testable.

Code coverage

A high code coverage percentage is often associated with a higher quality of code. However, the measurement itself can't determine the quality of code. Setting an overly ambitious code coverage percentage goal can be counterproductive. Imagine a complex project with thousands of conditional branches, and imagine that you set a goal of 95% code coverage. Currently the project maintains 90% code coverage. The amount of time it takes to account for all of the edge cases in the remaining 5% could be a massive undertaking, and the value proposition quickly diminishes.

A high code coverage percentage isn't an indicator of success, nor does it imply high code quality. It just represents the amount of code that is covered by unit tests. For more information, see unit testing code coverage .

Let's speak the same language

The term mock is unfortunately often misused when talking about testing. The following points define the most common types of fakes when writing unit tests:

Fake - A fake is a generic term that can be used to describe either a stub or a mock object. Whether it's a stub or a mock depends on the context in which it's used. So in other words, a fake can be a stub or a mock.

Mock - A mock object is a fake object in the system that decides whether or not a unit test has passed or failed. A mock starts out as a Fake until it's asserted against.

Stub - A stub is a controllable replacement for an existing dependency (or collaborator) in the system. By using a stub, you can test your code without dealing with the dependency directly. By default, a stub starts out as a fake.

Consider the following code snippet:

The preceding example would be of a stub being referred to as a mock. In this case, it's a stub. You're just passing in the Order as a means to be able to instantiate Purchase (the system under test). The name MockOrder is also misleading because again, the order isn't a mock.

A better approach would be:

By renaming the class to FakeOrder , you've made the class a lot more generic. The class can be used as a mock or a stub, whichever is better for the test case. In the preceding example, FakeOrder is used as a stub. You're not using FakeOrder in any shape or form during the assert. FakeOrder was passed into the Purchase class to satisfy the requirements of the constructor.

To use it as a Mock, you could do something like the following code:

In this case, you're checking a property on the Fake (asserting against it), so in the preceding code snippet, the mockOrder is a Mock.

It's important to get this terminology correct. If you call your stubs "mocks," other developers are going to make false assumptions about your intent.

The main thing to remember about mocks versus stubs is that mocks are just like stubs, but you assert against the mock object, whereas you don't assert against a stub.

Best practices

Here are some of the most important best practices for writing unit tests.

Avoid infrastructure dependencies

Try not to introduce dependencies on infrastructure when writing unit tests. The dependencies make the tests slow and brittle and should be reserved for integration tests. You can avoid these dependencies in your application by following the Explicit Dependencies Principle and using Dependency Injection . You can also keep your unit tests in a separate project from your integration tests. This approach ensures your unit test project doesn't have references to or dependencies on infrastructure packages.

Naming your tests

The name of your test should consist of three parts:

• The name of the method being tested.
• The scenario under which it's being tested.
• The expected behavior when the scenario is invoked.

Naming standards are important because they explicitly express the intent of the test. Tests are more than just making sure your code works, they also provide documentation. Just by looking at the suite of unit tests, you should be able to infer the behavior of your code without even looking at the code itself. Additionally, when tests fail, you can see exactly which scenarios don't meet your expectations.

Arranging your tests

Arrange, Act, Assert is a common pattern when unit testing. As the name implies, it consists of three main actions:

• Arrange your objects, create and set them up as necessary.
• Act on an object.
• Assert that something is as expected.
• Clearly separates what is being tested from the arrange and assert steps.
• Less chance to intermix assertions with "Act" code.

Readability is one of the most important aspects when writing a test. Separating each of these actions within the test clearly highlight the dependencies required to call your code, how your code is being called, and what you're trying to assert. While it might be possible to combine some steps and reduce the size of your test, the primary goal is to make the test as readable as possible.

Write minimally passing tests

The input to be used in a unit test should be the simplest possible in order to verify the behavior that you're currently testing.

• Tests become more resilient to future changes in the codebase.
• Closer to testing behavior over implementation.

Tests that include more information than required to pass the test have a higher chance of introducing errors into the test and can make the intent of the test less clear. When writing tests, you want to focus on the behavior. Setting extra properties on models or using non-zero values when not required, only detracts from what you are trying to prove.

Avoid magic strings

Naming variables in unit tests is important, if not more important, than naming variables in production code. Unit tests shouldn't contain magic strings.

• Prevents the need for the reader of the test to inspect the production code in order to figure out what makes the value special.
• Explicitly shows what you're trying to prove rather than trying to accomplish .

Magic strings can cause confusion to the reader of your tests. If a string looks out of the ordinary, they might wonder why a certain value was chosen for a parameter or return value. This type of string value might lead them to take a closer look at the implementation details, rather than focus on the test.

When writing tests, you should aim to express as much intent as possible. In the case of magic strings, a good approach is to assign these values to constants.

Avoid logic in tests

When writing your unit tests, avoid manual string concatenation, logical conditions, such as if , while , for , and switch , and other conditions.

• Less chance to introduce a bug inside of your tests.
• Focus on the end result, rather than implementation details.

When you introduce logic into your test suite, the chance of introducing a bug into it increases dramatically. The last place that you want to find a bug is within your test suite. You should have a high level of confidence that your tests work, otherwise, you won't trust them. Tests that you don't trust, don't provide any value. When a test fails, you want to have a sense that something is wrong with your code and that it can't be ignored.

If logic in your test seems unavoidable, consider splitting the test up into two or more different tests.

Prefer helper methods to setup and teardown

If you require a similar object or state for your tests, prefer a helper method than using Setup and Teardown attributes if they exist.

• Less confusion when reading the tests since all of the code is visible from within each test.
• Less chance of setting up too much or too little for the given test.
• Less chance of sharing state between tests, which creates unwanted dependencies between them.

In unit testing frameworks, Setup is called before each and every unit test within your test suite. While some might see this as a useful tool, it generally ends up leading to bloated and hard to read tests. Each test will generally have different requirements in order to get the test up and running. Unfortunately, Setup forces you to use the exact same requirements for each test.

xUnit has removed both SetUp and TearDown as of version 2.x

Avoid multiple acts

When writing your tests, try to only include one act per test. Common approaches to using only one act include:

• Create a separate test for each act.
• Use parameterized tests.
• When the test fails, it is clear which act is failing.
• Ensures that the test is focused on just a single case.
• Gives you the entire picture as to why your tests are failing.

Multiple acts need to be individually Asserted and it isn't guaranteed that all of the Asserts will be executed. In most unit testing frameworks, once an Assert fails in a unit test, the proceeding tests are automatically considered to be failing. This kind of process can be confusing as functionality that is actually working, will be shown as failing.

Validate private methods by unit testing public methods

In most cases, there shouldn't be a need to test a private method. Private methods are an implementation detail and never exist in isolation. At some point, there's going to be a public facing method that calls the private method as part of its implementation. What you should care about is the end result of the public method that calls into the private one.

Consider the following case:

Your first reaction might be to start writing a test for TrimInput because you want to ensure that the method is working as expected. However, it's entirely possible that ParseLogLine manipulates sanitizedInput in such a way that you don't expect, rendering a test against TrimInput useless.

The real test should be done against the public facing method ParseLogLine because that is what you should ultimately care about.

With this viewpoint, if you see a private method, find the public method and write your tests against that method. Just because a private method returns the expected result, doesn't mean the system that eventually calls the private method uses the result correctly.

Stub static references

One of the principles of a unit test is that it must have full control of the system under test. This principle can be problematic when production code includes calls to static references (for example, DateTime.Now ). Consider the following code:

How can this code possibly be unit tested? You might try an approach such as:

Unfortunately, you'll quickly realize that there are a couple of problems with your tests.

• If the test suite is run on a Tuesday, the second test will pass, but the first test will fail.
• If the test suite is run on any other day, the first test will pass, but the second test will fail.

To solve these problems, you'll need to introduce a seam into your production code. One approach is to wrap the code that you need to control in an interface and have the production code depend on that interface.

Your test suite now becomes as follows:

Now the test suite has full control over DateTime.Now and can stub any value when calling into the method.

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📙 A guide to unit testing in Javascript

mawrkus/js-unit-testing-guide

Folders and files, repository files navigation, a guide to unit testing in javascript.

This is a living document. New ideas are always welcome. Contribute: fork, clone, branch, commit, push, pull request.

All the information provided has been compiled & adapted from many references, some of them cited at the end of the document. The guidelines are illustrated by my own examples, fruit of my personal experience writing and reviewing unit tests. Many thanks to all of the sources of information & contributors.

📅 Last edit: September 2023.

📖 Table of contents

• General principles

Design principles

• Whenever possible, use TDD
• When applying TDD, always start by writing the simplest failing test
• When applying TDD, always make baby steps in each cycle
• Structure your tests properly
• Name your tests properly
• Use the Arrange-Act-Assert pattern
• Avoid logic in your tests
• Don't write unnecessary expectations
• Test the behaviour, not the internal implementation
• Consider using factory functions
• Don't test multiple concerns in the same test
• Cover the general case and the edge cases
• Use dependency injection
• Don't mock everything
• Don't write unit tests for complex user interactions
• Test simple user actions
• Create new tests for every defect
• Don't comment out tests
• Know your testing framework API
• Review test code first
• Practice code katas, learn with pair programming
• Translations
• Contributors

⛩️ General principles

Unit = Unit of work

The work can involve multiple methods and classes , invoked by some public API that can:

• Return a value or throw an exception
• Change the state of the system
• Make 3rd party calls (API, database, ...)

A unit test should test the behaviour of a unit of work: for a given input, it expects an end result that can be any of the above.

Unit tests are isolated and independent of each other

• Any given behaviour should be specified in one and only one test
• The execution/order of execution of one test cannot affect the others

The code is designed to support this independence (see "Design principles" below).

Unit tests are lightweight tests

• Easy to write and read

Unit tests are code too

They should be easily readable and maintainable .

Don't hesitate to refactor them to help your future self. For instance, it should be trivial to understand why a test is failing just by looking at its own code, without having to search in many places in the test file (variables declared in the top-level scope, closures, test setup & teardown hooks, etc.).

• Back to ToC •

The key to good unit testing is to write testable code . Applying simple design principles can help, in particular:

• Use a good, consistent naming convention .
• D on't R epeat Y ourself: avoid code duplication.
• Single responsibility : each software component (function, class, component, module) should focus on a single task.
• Keep a single level of abstraction in the same component. For example, do not mix business logic with lower-level technical details in the same method.
• Minimize dependencies between components: encapsulate, interchange less information between components.
• Support configurability rather than hard-coding: to prevent having to replicate the exact same environment when testing.
• Apply adequate design patterns : especially dependency injection , to be able to easily substitue the component's dependencies when testing.
• Avoid global mutable state : to keep things easy to understand and predictable.

🧭 Guidelines

The goal of these guidelines is to make your tests:

• Maintainable
• Trustworthy

These are the 3 pillars of good unit testing.

All the following examples assume the usage of the Jest testing framework .

✨ Whenever possible, use TDD

Test-Driven Development is a design process, not a testing process. It's a highly-iterative process in which you design, test, and code more or less at the same time. It goes like this:

• Think: Figure out what test will best move your code towards completion. (Take as much time as you need. This is the hardest step for beginners.)
• Red: Write a very small amount of test code. Only a few lines... Run the tests and watch the new test fail: the test bar should turn red.
• Green: Write a very small amount of production code. Again, usually no more than a few lines of code. Don't worry about design purity or conceptual elegance. Sometimes you can just hardcode the answer. This is okay because you'll be refactoring in a moment. Run the tests and watch them pass: the test bar will turn green.
• Refactor: Now that your tests are passing, you can make changes without worrying about breaking anything. Pause for a moment, look at the code you've written, and ask yourself if you can improve it. Look for duplication and other "code smells." If you see something that doesn't look right, but you're not sure how to fix it, that's okay. Take a look at it again after you've gone through the cycle a few more times. (Take as much time as you need on this step.) After each little refactoring, run the tests and make sure they still pass.
• Repeat: Do it again. You'll repeat this cycle dozens of times in an hour. Typically, you'll run through several cycles (three to five) very quickly, then find yourself slowing down and spending more time on refactoring. Then you'll speed up again.

This process works well for two reasons:

• You're working in baby steps, constantly forming hypotheses and checking them. Whenever you make a mistake, you catch it right away. It's only been a few lines of code since you made the mistake, which makes the mistake very easy to find and fix. We all know that finding mistakes, not fixing them, is the most expensive part of programming.
• You're always thinking about design. Either you're deciding which test you're going to write next, which is an interface design process, or you're deciding how to refactor, which is a code design process. All of this thought on design is immediately tested by turning it into code, which very quickly shows you if the design is good or bad.

Notice also how code written without a test-first approach is often very hard to test!

✨ When applying TDD, always start by writing the simplest failing test

From there, start building the functionalities incrementally.

✨ When applying TDD, always make baby steps in each cycle

Build your tests suite from the simple case to the more complex ones. Keep in mind the incremental design. Deliver new code fast, incrementally, and in short iterations:

✨ Structure your tests properly

Don't hesitate to nest your suites to structure logically your tests in subsets:

✨ Name your tests properly

Tests names should be concise, explicit, descriptive and in correct English. Read the output of the test runner and verify that it is understandable!

Keep in mind that someone else will read it too and that tests can be the live documentation of the code:

In order to help you write test names properly, you can use the "unit of work - scenario/context - expected behaviour" pattern:

Or if there are many tests that follow the same scenario or are related to the same context:

For example:

You might also want to use this pattern to describe a class and its methods:

Also, tests "should not begin with should" .

✨ Use the Arrange-Act-Assert pattern

This pattern is a good support to help you read and understand tests more easily:

• The arrange part is where you set up the objects to be tested: initializing input variables, setting up spies, etc.
• The act part is where you act upon the code under test: calling a function or a class method, storing the result, ...
• The assert part is where you test your expectations.

This pattern is also named "Given-When-Then" or "Setup-Exercise-Verify".

✨ Avoid logic in your tests

Always use simple statements. Don't use loops and/or conditionals. If you do, you add a possible entry point for bugs in the test itself:

• Conditionals: you don't know which path the test will take.
• Loops: you could be sharing state between tests.

✨ Don't write unnecessary expectations

Remember, unit tests are a design specification of how a certain behaviour should work, not a list of observations of everything the code happens to do:

✨ Test the behaviour, not the internal implementation

A better approach is to test at the same level of the API:

• Pro: changing the internal implementation will not necessarily force you to refactor the tests.
• Con: when a test is failing, you might have to debug to know which part of the code needs to be fixed.

Here, a balance has to be found, unit-testing some key parts can be beneficial.

✨ Consider using factory functions

Factories can:

• help reduce the setup code, especially if you use dependency injection,
• make each test more readable by favoring cohesion, since the creation is a single function call in the test itself instead of the setup,
• provide flexibility when creating new instances (setting an initial state, for example).

Factories can be particularly useful when dealing with the DOM:

Here also, there's a trade-off to find between applying the DRY principle and readability.

✨ Don't test multiple concerns in the same test

If a method has several end results, each one should be tested separately so that whenever a bug occurs, it will help you locate the source of the problem directly:

Pay attention when writing "and" or "or" in your test names ;)

✨ Cover the general case and the edge cases

Having edge cases covered will:

• clarify what the code does in a wide range of situations,
• capture regressions early when the code is refactored,
• help the future reader fully understand what the code fully does, as tests can be the live documentation of the code.

✨ Use dependency injection

We created a permanent storage of data. What happens if we do not properly clean it between tests? We might affect the result of other tests. By using dependency injection, we can prevent this behaviour:

✨ Don't mock everything

The idea to keep in mind is that dependencies can still be real objects . Don't mock everything because you can. Consider using the real version if:

• it does not create a shared state between the tests, causing unexpected side effects,
• the code being tested does not make HTTP requests or browser page reloads,
• the speed of execution of the tests stays within the limits you fixed,
• it leads to a simple, nice and easy tests setup.

✨ Don't write unit tests for complex user interactions

Examples of complex user interactions:

• Filling a form, drag and dropping some items then submitting the form.
• Clicking a tab, clicking an image thumbnail then navigating through a gallery of images loaded on-demand from an API.

These interactions involve many units of work and should be handled at a higher level by end-to-end tests . They will usually take more time to execute, they could be flaky (false negatives) and they will require debugging whenever a failure is reported.

For these complex user scenarios, consider using tools like Playwright or Cypress , or manual QA testing.

✨ Test simple user actions

Example of simple user actions:

• Clicking on a link that toggles the visibility of a DOM element
• Clicking on a button that performs an API call (like sending a tracking event).

These actions can be easily tested by simulating DOM events, for example:

Note how simple the tests are because the UI (DOM) layer does not mix with the business logic layer:

• a "click" event occurs
• a public method is called

The next step could be to test the logic implemented in showPreview() or hidePreview() .

✨ Create new tests for every defect

Whenever a bug is found, create a test that replicates the problem before touching any code . Then fix it.

✨ Don't comment out tests

Never. Ever. Tests have a reason to be or not.

Don't comment them out because they are too slow, too complex or produce false negatives. Instead, make them fast, simple and trustworthy. If not, remove them completely.

✨ Know your testing framework API

Take time to read the API documentation of the testing framework that you have chosen to work with.

Having a good knowledge of the framework will help you in reducing the size and complexity of your test code and, in general, will help you during development.

✨ Review test code first

When reviewing code , always start by reading the code of the tests. Tests are mini use cases of the code that you can drill into.

It will help you understand the intent of the developer very quickly (could be just by looking at the names of the tests).

✨ Practice code katas, learn with pair programming

Because experience is the only teacher . Ultimately, greatness comes from practicing; applying the theory over and over again, using feedback to get better every time.

📙 Resources

There's a ton of resources available out there, here are just a few I've found useful...

• Yoni Goldberg - "Writing clean JavaScript tests with the BASIC principles": https://yonigoldberg.medium.com/fighting-javascript-tests-complexity-with-the-basic-principles-87b7622eac9a
• Testim - "Unit Testing Best Practices: 9 to Ensure You Do It Right": https://www.testim.io/blog/unit-testing-best-practices/
• Vladimir Khorikov - "DRY vs DAMP in Unit Tests": https://enterprisecraftsmanship.com/posts/dry-damp-unit-tests/
• Georgina McFadyen - "TDD - From the Inside Out or the Outside In?": https://8thlight.com/blog/georgina-mcfadyen/2016/06/27/inside-out-tdd-vs-outside-in.html
• Sandi Metz - "Make Everything The Same": https://sandimetz.com/blog/2016/6/9/make-everything-the-same
• Varun Vachhar - "How to actually test UIs": https://storybook.js.org/blog/how-to-actually-test-uis/
• Rebecca Murphy - "Writing Testable JavaScript": http://alistapart.com/article/writing-testable-javascript
• James Shore - "Red-Green-Refactor ": https://www.jamesshore.com/v2/blog/2005/red-green-refactor
• Martin Fowler - "Refactoring": https://refactoring.com/
• Making Tech Better Podcast - "When is a test not a test? with Daniel Terhorst-North": https://www.madetech.com/resources/podcasts/episode-18-daniel-terhorst-north/
• Assert(js) Conference (2019): https://www.youtube.com/watch?v=UOOuW5tqT8M&list=PLZ66c9_z3umMtAboEKsHXQWB0YMJje7Tl
• Assert(js) Conference (2018): https://www.youtube.com/watch?v=zqdCM8zR6Mc&list=PLZ66c9_z3umNSrKSb5cmpxdXZcIPNvKGw
• James Shore - "TDD Lunch & Learn ": https://www.jamesshore.com/v2/projects/lunch-and-learn
• Roy Osherove - "JS Unit Testing Good Practices and Horrible Mistakes": https://www.youtube.com/watch?v=iP0Vl-vU3XM
• José Armesto - "Unit Testing sucks (and it’s our fault)": https://www.youtube.com/watch?v=GZ9iZsMAZFQ

Unit testing libraries

• Jest: https://jestjs.io/
• Mocha: https://mochajs.org/
• Node TAP: https://github.com/tapjs/node-tap
• Tape: https://github.com/substack/tape

End-to-end testing tools

• Cypress: https://www.cypress.io/
• Playwright: https://playwright.dev/
• https://kata-log.rocks/index.html
• http://codekata.com/
• https://github.com/gamontal/awesome-katas
• https://github.com/cesalberca/katas
• https://github.com/emilybache

🌐 Translations

This style guide is also available in other languages:

• 🇨🇳 Chinese (Simplified) - Thanks to GabrielchenCN !

🫶🏾 Contributors

Contributors 5

• Work Energy Power
• Unit Of Work

Unit of Work

Work is defined as the measure of the displacement of an object or a point. Some common examples of force include riding a bike uphill or carrying something in the presence of the earth’s gravity. In essence, work is nothing but a mechanical manifestation of energy. It is represented as W .

Mathematically, it is represented as follows;

• F is the force applied
• d is the displacement

Let’s look at the work units below.

SI Unit of Work

The SI unit of work is joule (J). Joule is defined as the work done by a force of one newton causing a displacement of one meter. Sometimes, newton-metre (N-m) is also used for measuring work. However, as this unit is also used for torque it can get quite confusing. Thus, SI authority does not encourage anyone to use this unit.

Following is the table of units and dimensional formula:

Watch the video to find out what are base measurements

Other Units of Work

Some commonly used work units also include erg in the CGS system, the horsepower-hour, the newton-metre, the foot-pound, the kilowatt-hour, the foot-poundal, and the litre-atmosphere. Notably, work has a similar physical dimension as heat; therefore, measurement units such as BTU, therm and Calorie, are also used for measuring work done.

Converting The Units Of Work

To learn more units and other related topics download BYJU’S- The Learning App.

Related Links

The below video helps to revise the concepts in the chapter Work and Energy Class 9

Frequently Asked Questions – FAQs

Define work., what is the formula to represent work, what is the si unit of work, what is a joule, list three units of work in cgs system..

• Horsepower-hour
• Newton-metre
• foot-poundal

Watch the video and solve NCERT exercise questions in the chapter Work and Energy Class 9

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Put your understanding of this concept to test by answering a few MCQs. Click ‘Start Quiz’ to begin!

Select the correct answer and click on the “Finish” button Check your score and answers at the end of the quiz

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JUnit 5 tutorial - Learn how to write unit tests

1.1. configuration for using junit 5, 1.2. how to define a test in junit, 1.3. example for developing a junit 5 test for another class, 1.4. junit test class naming conventions, 1.5. where should the test be located, 1.6. static imports and unit testing, 2.1. testing for exceptions, 2.2. testing multiple assertions (grouped assertions) with assertall, 2.3. defining timeouts in your tests, 2.4. how to disable tests, 3.1. using dynamic tests, 3.2. using parameterized tests, 4.1. nested tests, 4.2. test execution order, 4.3. using the @tempdir annotation to create temporary files and paths, 4.4. test suites, 5.1. project creation, 5.2. configure maven dependencies for junit 5, 5.3. package creation, 5.4. create a java class, 5.5. create a junit test, 5.6. run your test in eclipse, 5.7. fix the bug and re-run your tests, 5.8. review, 5.9. simplify your test code with @before each, 5.10. define a group check with assertall, 6.1. project creation, 6.2. update build.gradle file to use junit5, 6.3. create a java class, 6.4. create a junit test, 6.5. run your test in eclipse, 6.6. fix the bug and re-run your tests, 6.7. review, 6.8. simplify your test code with @before each, 6.9. define a group check with assertall, 7.1. create project and add junit5 dependencies, 7.2. create the data model used for testing, 7.3. write tests for the model and the services, 7.4. verify on command line, 7.5. add a long running method to your data service, 7.6. develop a test to constrain the execution time of the long running method, 8.1. create the data model used for testing, 8.2. write tests for the model and the services, 8.3. verify, 8.4. solution, 9.1. write tests checking for exceptions, 9.2. verify, 9.3. solution, 9.4. enable test only on certain platforms, 10.1. write tests, 10.2. solution, 10.3. run tests, 11.1. create class for testing, 11.2. write a dynamic test, 11.3. verify, 12.1. add dependency, 12.2. write a parameterized test, 12.3. verify, 12.4. add more options, 13.1. create class under test, 13.2. write tests, 14.1. add dependency to @inject, 14.2. create class under test, 14.3. solution, 15. exercise: create test reports, 16. exercise: clone the junit5 github repo and review tests, 17. overview of junit5 annotations, 18. conclusion, 19. junit resources.

This tutorial explains unit testing with JUnit with the JUnit 5 framework (JUnit Jupiter). It explains the creation of JUnit 5 tests with the Maven and Gradle build system. It demonstrates the usage of the Eclipse IDE for developing software tests with JUnit 5 but this tutorial is also valid for tools like Visual Code or IntelliJ.

Check out our Java Testing training including Instructional Videos

1. overview.

JUnit is a popular unit-testing framework in the Java ecosystem. JUnit 5 added many new features based on the Java 8 version of the language.

This guide gives an introduction into unit testing with the JUnit framework using JUnit 5. It focus on the usage of the framework.

For further information about testing with Java see:

What is software testing

Using Maven in the Eclipse IDE

Using the Eclipse IDE for creating and running JUnit test

Using Gradle in the Eclipse IDE

Mockito tutorial

JUnit 5 extensions

Hamcrest Tutorial

AssertJ Tutorial

JUnit 4 tutorial

To use JUnit 5 you have to make the libraries available for your test code. Jump to the section which is relevant to you, for example read the Maven part, if you are using Maven as build system.

== Configure Maven dependencies for JUnit 5

=== Steps required to configure Maven to use JUnit5

To use JUnit5 in an Maven project, you need to:

Configure to use Java 11 or higher, as this is required by JUnit5

Configure the maven-surefire-plugin and maven-failsafe-plugin to be at version 2.22.2 so that they can run JUnit5

Add dependencies to the JUnit5 API and engine for your test code

=== Configure Maven

Therefore you need to adjust your pom file, similar to the following:

Once you have done this, you can start using JUnit5 in your Maven project for writing unit tests.

=== Update Maven settings (in case you are using the Eclipse IDE)

Right-click your pom file, select Maven   Update Project and select your project. This triggers an update of your project settings and dependencies.

== Update build.gradle file to use JUnit5

To use JUnit 5 with the Gradle build system, ensure you use at least Gradle 6.0 to avoid already fixed issues.

Modify your build.gradle file to contain at least the following entries. Your build file may contain more dependencies.

If you are not using a build system and the JUnit library is not part of the classpath of your project during the creation of a new test, Eclipse prompts you to add it.

A JUnit test is a method contained in a class which is only used for testing. This is called a Test class . To mark a method as a test method, annotate it with the @Test annotation. This method executes the code under test.

The following code defines a minimal test class with one minimal test method.

You can use assert methods, provided by JUnit or another assert framework, to check an expected result versus the actual result. Such statement are called asserts or assert statements .

Assert statements typically allow to define messages which are shown if the test fails. You should provide here meaningful messages to make it easier for the user to identify and fix the problem. This is especially true if someone looks at the problem, who did not write the code under test or the test code.

The following example defines a Java class and defines software tests for it.

Assume you have the following class which you want to test.

A test class for the above class could look like the following.

Build tools like Maven use a pattern to decide if a class is a test classes or not. The following is the list of classes Maven considers automatically during its build:

Therefore, it is common practice to use the Test or Tests suffix at the end of test classes names.

Typical, unit tests are created in a separate source folder to keep the test code separate from the real code. The standard convention from the Maven and Gradle build tools is to use:

src/main/java - for Java classes

src/test/java - for test classes

JUnit 5 allows to use static imports for its assertStatements to make the test code short and easy to read. Static imports are a Java feature that allows fields and methods defined in a class as public static to be used without specifying the class in which the field is defined.

JUnit assert statements are typically defined as public static to allow the developer to write short test statements. The following snippet demonstrates an assert statement with and without static imports.

2. Assertions and assumptions

JUnit 5 comes with multiple assert statements, which allows you to test your code under test. Simple assert statements like the following allow to check for true, false or equality. All of them are static methods from the org.junit.jupiter.api.Assertions.* package.

Messages can be created via lambda expressions, to avoid the overhead in case the construction of the message is expensive.

Testing that certain exceptions are thrown are be done with the org.junit.jupiter.api.Assertions.expectThrows() assert statement. You define the expected Exception class and provide code that should throw the exception.

This lets you define which part of the test should throw the exception. The test will still fail if an exception is thrown outside of this scope.

If an assert fails in a test, JUnit will stop executing the test and additional asserts are not checked. In case you want to ensure that all asserts are checked you can assertAll .

In this grouped assertion all assertions are executed, even after a failure. The error messages get also grouped together.

If these tests fail, the result looks like the following:

If you want to ensure that a test fails, if it isn’t done in a certain amount of time you can use the assertTimeout() method. This assert fails the method if the timeout is exceeded.

If you want your tests to cancel after the timeout period is passed you can use the assertTimeoutPreemptively() method.

The @Disabled or @Disabled("Why disabled") annotation marks a test to be disabled. This is useful when the underlying code has been changed and the test case has not yet been adapted of if the test demonstrates an incorrect behavior in the code which has not yet been fixed. It is best practice to provide the optional description, why the test is disabled.

Alternatively you can use Assumptions.assumeFalse or Assumptions.assumeTrue to define a condition for test execution. Assumptions.assumeFalse marks the test as invalid, if its condition evaluates to true. Assumptions.assumeTrue evaluates the test as invalid if its condition evaluates to false. For example, the following disables a test on Linux:

This gives TestAbortedException which the test runners evaluate as skipped tests.

For example the following testMultiplyWithZero is skipped if executed on Linux.

You can also write an extension for @ExtendWith which defines conditions under which a test should run.

3. Dynamic and parameterized tests

JUnit 5 supports the creation of dynamic tests via code. You can also run tests with a set of different input values with parameterized tests.

Both approaches are described here.

Dynamic test methods are annotated with @TestFactory and allow you to create multiple tests of type DynamicTest with your code. They can return:

an Iterable

a Collection

JUnit 5 creates and runs all dynamic tests during test execution.

Methods annotated with @BeforeEach and @AfterEach are not called for dynamic tests. This means, that you can’t use thesm to reset the test object, if you change it’s state in the lambda expression for a dynamic test.

In the following example we define a method to return a Stream of DynamicTest instances.

Junit5 also supports parameterized tests. To use them you have to add the junit-jupiter-params package as a test dependencies.

If you are using Gradle:

For this example we use the @MethodSource annotation.

We give it the name of the function(s) we want it to call to get it’s test data. The function has to be static and must return either a Collection, an Iterator, a Stream or an Array. On execution the test method gets called once for every entry in the data source. In contrast to Dynamic Tests @BeforeEach and @AfterEach methods will be called for parameterized tests.

3.2.1. Data sources

The following table gives an overview of all possible test data sources for parameterized tests.

3.2.2. Argument conversion

JUnit tries to automatically convert the source strings to match the expected arguments of the test method.

If you need explicit conversion you can specify a converter with the @ConvertWith annotation. To define your own converter you have to implement the ArgumentConverter interface. In the following example we use the abstract SimpleArgumentConverter base class.

4. Additional information about JUnit 5 usage

The @Nested annotation can be used to annotate inner classes which also contain tests. This allows to group tests and have additional @BeforeEach method, and one @AfterEach methods. When you add nested test classes to our test class, the following rules must be followed:

All nested test classes must be non-static inner classes.

The nested test classes are annotated with @Nested annotation so that the runtime can recognize the nested test classes.

a nested test class can contain Test methods, one @BeforeEach method, and one @AfterEach method.

JUnit runs test methods is a deterministic but unpreditable order (MethodSorters.DEFAULT). You can use the @TestMethodOrder on the class to control the execution order of the tests, via:

@TestMethodOrder(MethodOrderer.OrderAnnotation.class) - Allows to use the @Order(int) annotation on methods to define order

@TestMethodOrder(MethodOrderer.DisplayName.class) - runs test method in alphanumeric order of display name

@TestMethodOrder(MethodOrderer.MethodName.class) - runs test method in alphanumeric order of method name

Custom implementation - Implement your own MethodOrderer via the orderMethods method, which allows you to call context.getMethodDescriptors().sort(..)

The following demonstrates this with OrderAnnotation.class .

The @TempDir annotations allows to annotate non-private fields or method parameters in a test method of type Path or File. JUnit 5 has registered a `ParameterResolutionException for this annotation and will create temporary files and paths for the tests. It will also remove the temporary files are each test.

The 5.8 release of JUnit 5 is planned to have test suite support included.

See https://github.com/junit-team/junit5/pull/2416 for the work.

At this time of writing you can use the milestone release of 5.8.0-M1 to check this. See the dependencies here https://search.maven.org/artifact/org.junit.platform/junit-platform-suite-api/1.8.0-M1/jar

5. Exercise: Writing a JUnit 5 test with Maven and Eclipse in 5 mins

In this exercise you learn you to write a JUnit5 test using Maven and the Eclipse IDE.

Create a new Maven project with the following settings:

Group: com.vogella

Artifact: com.vogella.junit.first

Version: 0.0.1-SNAPSHOT

Packaging: jar

5.2.1. Steps required to configure Maven to use JUnit5

5.2.2. configure maven, 5.2.3. update maven settings (in case you are using the eclipse ide).

Create a package named com.vogella.junit.first in the src/main/java and src/main/test folder.

In the src folder, create the following class in the com.vogella.junit.first package.

Position the cursor on the MyClass in the Java editor and press Ctrl+1. Select that you want to create a new JUnit test from the list.

In the following wizard ensure that the New JUnit Jupiter test flag is selected. The source folder should select the test directory.

Press the Next button and select the methods that you want to test.

Create a test with the following code.

Right-click on your new test class and select Run-As   JUnit Test .

The result of the tests are displayed in the JUnit view. In our example one test should be successful and one test should show an error. This error is indicated by a red bar.

You discovered a bug in the tested code!

The test is failing, because our multiplier class is currently not working correctly. It does a division instead of multiplication. Fix the bug and re-run the test to get a green bar.

After a few minutes you should have created a new project, a new class and a new unit test. Congratulations! If you feel like it, lets improve the tests a bit and write one grouped test.

The initialization of MyClass happens in every test, move the initialization to a @BeforeEach method.

Define a new test method which checks both condition at the same time with assertAll statement. Change the condition to make both tests fail, run the test and ensure that both are executed.

Afterwards adjust the test so that both are successfully executed.

6. Exercise: Writing a JUnit 5 test with Gradle and Eclipse in 5 mins

In this exercise you learn you to write a JUnit5 test using the Gradle build system and the Eclipse IDE.

Create a new Gradle project with the following setting:

Name: com.vogella.junit.first

See Create a Grade project with Eclipse to learn how to create a Gradle project with Eclipse.

The wizard should also have create the package com.vogella.junit.first in the src/main/java and src/main/test folder. Remove the generated classes from it.

7. Exercise: Writing JUnit5 unit tests

In this exercise, you develop some JUnit 5 tests for a given data model. You already learned how to create projects with Maven or Gradle and how to add Junit5 to them.

To review this information see:

Create a Grade project with Eclipse

Adding JUnit to an project

The following description assumes that you are familiar with these steps and will not repeat them.

Create a new project called com.vogella.unittest either with Maven or with Gradle and update their settings to use JUnit5.

Create the com.vogella.unittest.model package and copy and paste the following classes on it.

Create the com.vogella.unittest.services package and copy and paste the following classes on it.

Create the following test class.

Solve the TODO and ensure that all tests can be successfully executed from your IDE. You may find issues in the DataService with these tests, fix them if you encounter them.

The following is a possible implementation of the tests:

You actually found errors in the DataService implementations, adjust the following method:

Verify that your code compiles and your test are running via the command line with:

mvn clean verify in case you are using Maven

./gradlew test in case you are using Gradle

Add a fake update method to your DataService which takes a long time to update the data and returns true on success.

Create a new test method in your DataServiceTest . Use the assertTimeout assert statement to ensure that this test does not run longer than 3 seconds.

8. Exercise: Develop unit tests for a regular expression utility method for email verification

Create the com.vogella.unittest.email package and copy and paste the following classes on it.

Fix all the failing test, unfortunately the test specification is not very good. Try to write reasonable tests which fit the method name.

Run your new test via the IDE. Verify that your code compiles and your test are running via the command line.

The following listing contains a possible implementation of the test.

9. Exercise: Testing exceptions and conditional enablement

We also want to check that exceptions with the correct error messages are thrown, if we call the class under test with incorrect data.

Fix the disabled tests and enable them. The name should give a good indication what you have to do test here.

You may discover that the data model does not behave a expected by the test, fix them in this case.

Run your update test via the IDE. Verify that your code compiles and your test are running via the command line with the mvn clean verify .

The test indicates that you need to update the TolkienCharacter constructor.

Write this test and adjust it so that is only runs on the operating system you are using.

10. Exercise: Writing nested tests to group tests for display

Create the following test.

Run the test from your IDE and review how the grouped tests are displayed.

11. Exercise: Testing multiple parameter

Create the com.vogella.unittest.converter package and copy and paste the following class on it.

Run your new test via the IDE and ensure that you have 6 tests running succesfull.y

Verify that your code compiles and your test are running via the command line either with ./gradlew test`or with the `mvn clean verify depending on your build system.

12. Exercise: Testing with multiple input parameter

Dynamic tests are included in the regular JUnit 5 library, which you already included. To use parameters in your tests, you have to add the junit-jupiter-params library.

If you are using Maven add the following dependency to junit-jupiter-params to your Maven pom file.

If you are using Gradle add the following to your build.gradle file

Review the following code:

Create a new test method in ConverterUtilTest which also uses a parameterized test.

Run your new test via the IDE.

Verify that your code compiles and your test are running via the command line with the ./gradlew test or mvn clean verify command based on your build system.

ParameterizedTest are very flexible in sense of their input. The following lists a few more. Add these to your test and run the tests again.

13. Exercise: Using the @TempDir annotation to create temporary files and paths

In this exercise you learn how to use the @TempDir annotation to let JUnit 5 create files and paths on request in your test and to automatically remove them after the test.

Create the following class

Using the @TempDir annotation, create unit which test named FileWriterTest for the following:

Ensure that the Path given to you by the @TempDir annotation if writable

Ensure that a appending to a file with FileWriter.appendFile which has not yet been created with FileWriter.createFile throws an exception

Ensure that you can write to the file once you created it

13.2.1. Solution

14. exercise: testing for annotations.

In this exercise you write test to check class under test for certain annotations.

If you have not yet done this, add a dependency to javax.inject.

t validates that the Servic === Write tests

Write a test that validates that the Service class only has one constructor annotated with @Inject .

The class has a `getConstructors method.

The Constructor has a method getAnnotation

Both Maven and Gradle allow to generate HTML report for unit tests.

Gradle creates these automatically, if you run the ./gradlew build command and with Maven you run the mvn clean verify surefire-report:report command.

Run this for your project and check the build folder for the generated test reports.

Open JUnit5 Github page in your browser and clone the repo.

Import the project into your favorite IDE and review some of the tests, e.g. the platform-tests contains a lot of useful tests.

The following table gives an overview of the most important annotations in JUnit 5 from the org.junit.jupiter.api package.

JUnit 5 makes is easy to write software tests.

The implementation of all these examples and code snippets can be found over on Github . The Maven examples are located in JUnit with Maven and the Gradle examples are located in JUnit 5 with Gradle .

If you need more assistance we offer Online Training and Onsite training as well as consulting

See License for license information .

JUnit Homepage

JUnit 5 user guide

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Clean Unit Tests with Mockito

• April 25, 2021

In this article we will learn how to mock objects with Mockito. We’ll first talk about what test doubles are and then how we can use them to create meaningful and tailored unit tests. We will also have a look at the most important Dos and Don’ts while writing clean unit tests with Mockito.

Example Code

Introduction to mocks.

The basic concept of mocking is replacing real objects with doubles . We can control how these doubles behave. These doubles we call test doubles . We’ll cover the different kinds of test doubles later in this article.

Let’s imagine we have a service that processes orders from a database. It’s very cumbersome to set up a whole database just to test that service. To avoid setting up a database for the test, we create a mock that pretends to be the database, but in the eyes of the service it looks like a real database. We can advise the mock exactly how it shall behave. Having this tool, we can test the service but don’t actually need a database.

Here Mockito comes into play. Mockito is a very popular library that allows us to create such mock objects.

Consider reading Why Mock? for additional information about mocking.

Different Types of Test Doubles

In the world of code, there are many different words for test doubles and definitions for their duty. I recommend defining a common language within the team.

Here is a little summary of the different types for test doubles and how we use them in this article:

Mockito in Use

Consider the following example:

The green arrow with the dotted line and filled triangle stands for implements . CityServiceImpl is the implementation of CityService and therefore an instance of CityService .

The white arrow with the diamond says that CityRepository is part of CityService . It is also known as composition .

The remaining white arrow with the dotted line stands for the fact that CityServiceImpl owns a reference to CityRepository .

We don’t want to consider the CityRepository implementation when unit testing CityServiceImpl . If we used a real CityRepository implementation in the test, we would have to connect it to a database, which makes the test setup more complicated and would increase the number of reasons why our test could fail, since we have added complexity in our test fixture with potentially failing components.

Here Mockito comes to the rescue! Mockito allows us to create a suitable test double for the CityRepository interface and lets us define the behavior we expect from it. Applying this possibility we can create meaningful unit Here Mockito comes to the rescue! Mockito allows us to create a suitable test double for the CityRepository interface and lets us define the behavior we expect from it. Applying this possibility we can create meaningful unit tests to ensure the correct behavior of the service.

In summary, what we want is a simple, fast, and reliable unit test instead of a potentially complex, slow, and flaky test!

Let’s look at an example:

The test case consists of the system under test CityService and its dependencies. In this case, the only dependency is an instance of CityRepository . We need those references to test the expected behavior and reset the test double to not interfere with other test cases (more about that later).

Within the setup section, we create a test double with Mockito.mock(<T> classToMock) . Then, we inject this test double into the CityServiceImpl constructor so that its dependencies are satisfied. Now we are ready to create the test cases:

Here we have two example test cases.

The first one ( find() ) is about finding a city via the CityService . We create an instance of City as the object which we expect to be returned from the CityService . Now we have to advise the repository to return that value if - and only if - the declared ID has been provided.

Since cityRepository is a Mockito mock, we can declare its behavior with Mockito.when() . Now we can call the find() method on the service, which will return an instance of City .

Having the expected and the actually returned City objects, we can assert that they have the same field values.

In case a method has no return value (like cityService.delete() in the code example), we can’t create an assertion on the return value. Here Mockito’s spy features come into play.

We can query the test double and ask if a method was called with the expected parameter. This is what Mockito.verify() does.

These two features - mocking return values and verifying method calls on test doubles - give us great power to create various simple test cases . Also, the shown examples can be used for test-driven development and regression tests. Mockito fits both needs!

How to Create Mocks with Mockito

Until now, we have seen how to create fast and simple test cases. Now let’s look at the different ways of creating mocks for our needs. Before we’ll continue, we must understand what kind of test double Mockito creates.

Mockito creates test doubles of the type mock , but they have some features of a spy . These extra features allow us to verify if a certain method was called after we executed our test case. More about that later.

Creating Mocks with Plain Mockito

Let’s continue with the first variant to create a mock with Mockito. This variant doesn’t require any framework or annotations. It is applicable in every project where we have included Mockito.

We can simply declare a variable with the type of the component we want to mock. Taking the example from above, we want CityRepository to be a mock so that we don’t have to rely on its dependencies (like a database). The mock is then passed to the service, which is the system under test .

That’s all we need to set up our first mock with Mockito!

Initializing Mocks with Mockito Annotations

In case we have multiple dependencies that must be mocked, it gets cumbersome to create each and every mock manually with the variant shown above. So, we can also create mocks by using the @Mock annotation:

We can annotate each field to be a mock with the annotation of @Mock . Annotating them doesn’t initialize them yet. To do so, we call MockitoAnnotations.openMocks(this) in the @BeforeEach section of our test. The annotated fields of the provided object are then initialized and ready to use, which is in our case is the class instance itself ( this ). We don’t have to deal with boilerplate code anymore and can keep our unit tests neat and concise.

Using JUnit Jupiter’s MockitoExtension

As an alternative to the Mockito annotation style we can make use of JUnit Jupiter’s @ExtendWith and extend JUnit Jupiter’s context with MockitoExtension.class :

The extension assumes the initialization for annotated fields, so we must not do it ourselves. This makes our setup even more neat and concise!

Injecting Mocks with Spring

If we have a more complex test fixture, and we want to inject the mock into Spring’s ApplicationContext we can make use of @MockBean :

Note that @MockBean is not an annotation from Mockito but from Spring Boot! In the startup process, Spring places the mock in the context so that we don’t need to do it ourselves. Wherever a bean requests to have its dependency satisfied, Spring injects the mock instead of the real object. This becomes handy if we want to have the same mock in different places.

See Mocking with Mockito and Spring Boot for a deep dive on how to mock Beans in Spring Boot.

Defining the Behavior of Mocks

In this section, we have a look at how to define the behavior of the mocks in our test. What we have seen until now is what mocks are used for and how to create them. We are ready to use them in our test cases.

How to Return an Expected Object

The probably most common case when using Mockito is to return expected objects. If we call findByName(name) on CityService we would expect that the argument for name is forwarded to the repository which returns an Optional of a City . The service unpacks the Optional if present or otherwise throws an exception.

We first create the expected object for City . Having that expected instance for a City , we can define the behavior of the mock which is to return the Optional of the expected instance. We do so by calling Mockito.when() with the call we want to make. As a last step, we must declare the return value of that call at the end of the method chain.

If we try to find the expected city by its name, the service will return the previously declared object without throwing an exception. We can assert that the expected City equals the actual City from the service.

How to Throw an Exception

Mockito gives us developers also the possibility to throw exceptions instead of returning a value. This is mostly used to test error handling blocks in our code.

Declaring the behavior only differs by the last call in the method chain. With thenThrow() , we advise Mockito to throw an IllegalArgumentException in this case.

In our case, we just assert that our CityService implementation re-throws the exception.

How to Verify a Method Call

We can’t advise Mockito to return a value on void methods. In this case it is better to assert an underlying component was called. This can be achieved by using Mockito.verify() :

In this example, it isn’t necessary to declare the behavior of the mock beforehand. Instead, we just query the mock if it has been called during the test case. If not, the test case fails.

How To Verify the Number of Method Calls

We can verify how many times a mock was called by simply use the built-in verify() method. If the condition is not met, our test case will fail. This is extremely handy for algorithms or similar processes. There are other predefined verification modes such as atLeastOnce() or never() already present and ready to use!

Mockito Best Practices

Knowing how to create the mocks, let’s have a look at some best practices to keep our tests clean and maintainable. It will save us much time debugging and doesn’t let our team members guess what the intent of the test case is.

Don’t Share Mock behavior Between Tests

We might be tempted to put all behavior declarations using Mockito.when() into a setup method that runs before each test (i.e. annotated with @BeforeEach ) to have them in a common place. Even though this reduces the test cases to a minimum, the readability suffers a lot:

This will get us simple test cases like this because we don’t have to define the behavior in each test case:

But, because all mocking behavior is in a central place, we must pay attention to not break any test cases when modifying this central code. Also, we don’t know which test case requires which behavior when reading the test case. We have to guess or investigate the actual code to find out.

We better declare the behavior for each test case in isolation, so that the test cases are independent of each other. The code from above should be refactored to something like the following:

If we explicitly want to re-use a certain mock behavior in multiple test cases, we can move it into special methods like this:

We can then use these methods in the test cases like above. It’s important to make methods with shared mock behavior very specific and name them properly to keep the test cases readable .

Write Self-Contained Test Cases

The unit tests we write should be runnable on any machine with the same result. They shouldn’t affect other test cases in any way. So we must write every unit test self-contained and independent of test execution order.

It’s likely that the errors in non-self-contained test cases are caused by setup blocks that declare behavior shared between test methods. If we need to add a new behavior at the end of the block, each previous declaration must be executed before we can to call ours. Or vice versa: if a new declaration is inserted at the beginning, causes a shift of all other declarations towards the end. At least now our alarm bell should ring, and it’s time to reconsider our test case!

Avoid Mockito.reset() for Better Unit Tests

Mockito recommends in their documentation to prefer recreation of mocks over resetting them:

Smart Mockito users hardly use this feature because they know it could be a sign of poor tests. Normally, you don’t need to reset your mocks, just create new mocks for each test method.

We better create simple and small test cases than lengthy and over-specified tests. The cause of such tests might be testing too much in a single unit test. But let’s look at an example for this situation:

What is this test case doing?

• Tries to find a city and asserts that it’s equal to the expected city
• Deletes a city and verifies that the delete method on the repository has been called
• Tries to find the previously created city again but expecting an exception.

We must call cityRepository.reset() to let Mockito forget what was declared before that line. This is necessary, because we declared two different behaviors of cityService(expected.getId()) in the same test. This test case’s because we declared two different behaviors of cityService(expected.getId()) in the same test. This test case’s design is unfortunate. It tests too much for one single test and could be split in simpler and smaller units:

Now each test is simple and easily understandable. We don’t have to reset the mocks anymore, since this is achieved in the setUp() method. The effectively tested code is the same but a lot more meaningful than before.

Don’t Mock Value Objects or Collections

Mockito is a framework to mock objects with behavior that can be declared at the beginning of our test. It is common to have Data Transfer Objects (or DTOs). The intent of such a DTO is, as its name says, to transport data from a source to a destination. To retrieve this data from the object, we could declare the behavior of each getter. Albeit this is possible, we should rather use real values and set them to the DTO. The same rule applies for collections too, since they are a container for values as well.

As explained, it is possible to mock a City , which is a wrapper for the city name and other properties.

It’s not worth the effort to declare the behavior for numerous getters of an object. We better create a real object containing the values and don’t cover implicitly clear behavior of objects. Now let’s see a mocked List :

There is no value added to mock the list. It’s even harder to understand what we expected from our list. In comparison with a real List (i. e. ArrayList ) things get clearer right away.

Using mocks for collections we might hide the natural behavior of a List . In the worst case, our application fails in production because we assumed a List to behave differently from how it actually does!

Mockito is a framework to mock behavior of components based on values and not to mock values. This means that we better create tests for components that process DTOs rather than for the DTOs themselves.

Testing Error Handling with Mockito

We often only test the happy flow of our application. But how to test the correct behavior in our try-catch blocks? Mockito has the answer: Instead of declaring a return value, we can declare an exception to be thrown. This allows us, to write unit tests, that ensure our try-catch-blocks work as expected!

Important to know: In case we throw checked exceptions, the compiler doesn’t let us throw checked exceptions that are not declared on the method !

Mockito FAQ

In this section, we want to point out important things which are nice to know.

• What types can I mock? Mockito allows us to mock not only interfaces but also concrete classes.
• What is returned if I don’t declare a mock’s behavior? Mockito by default returns null for complex objects, and the default values for primitive data types (for example 0 for int and false for boolean )
• How many times does Mockito return a previously declared value? If we have declared a return value once, Mockito returns always the same value, regardless of how many times a method is called. If we have multiple calls to Mockito.when() with different return values, the first method call will return the first declared value, the second method call the second value, and so on.
• Can I mock final classes? No, final classes can’t be mocked and neither can final methods. This has to do with the internal mechanism of how Mocktio creates the mock and the Java Language Specification. If we want to do so, we can use PowerMock .
• Can I mock a constructor? Mockito can’t mock constructors, static methods, equals() nor hashCode() out of the box. To achieve that, PowerMock must be used.

Pros and Cons

Mockito helps us to create simple mocks fast. The Mockito API is easy to read since it allows us to write tests in fluent style. Mockito can be used in plain Java projects or together with frameworks such as Spring Boot. It is well documented and has lots of examples in it. In case of problems, there is a huge community behind it and questions are answered frequently on StackOverflow. It provides great flexibility to its users which can contribute their ideas since it is an open-source project. Therefore, the development is ongoing, and the project is maintained.

Mockito can’t mock everything out of the box. In case we want to mock final or static methods, equals() or the construction of an object, we need PowerMock .

In this post, we learned how to create mocks for unit tests in various variants. Mockito gives us a lot of flexibility, and the freedom to choose between numerous tools to achieve our goals. When working in teams, we define a common language and Mockito code style guideline on how we want to use this powerful tool for testing. This will improve our performance and helps to discuss and communicate.

Although Mockito comes with a lot of features, be aware of its restrictions. Don’t spend time to make the impossible possible, better reconsider our approach to test a scenario.

You will find all examples on GitHub .

Lukas Leuenberger

I am a professional Software Engineer and part time student from Zurich. I like the everyday challenge to solve problems and find simple solutions for complex problems. This can only be achieved if the problem is mastered from design to implementation. This is what I like the most about software engineering. It's not only about what, but also how.

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Writing Tests with JUnit 5

In this tutorial we’re going to look at features of JUnit 5 that can make it easier for us to write effective and readable automated tests. All code in this tutorial can be found in this GitHub repository .

This blog post covers the same material as the video. This provides an easy way for people to skim the content quickly if they prefer reading to watching, and to give the reader/watcher code samples and links to additional information.

Setting up Gradle for JUnit 5

This tutorial uses Gradle , for information on how to add JUnit 5 via Maven take a look at our blog and video on Migrating to JUnit 5 from JUnit 4 .

Given a Gradle build file , use ⌘N (macOS) or Alt+Insert (Windows/Linux) to add a new dependency. Typing "junit" in the artifact search box should give a list of possible dependencies.

Use Tab to jump into the dependencies list and use the down arrow until org.junit.jupiter:junit-jupiter is selected. Use the right arrow to open up the version options for this dependency, and choose version 5.6.2 (the most recent production version at the time of writing).

NOTE: if you try to search for a dependency and you don’t get the results you expect (either no results, or the versions seem out of date), make sure IntelliJ IDEA has an updated Maven Repository via the settings.

You should see an icon in the top right of the Gradle build file when it has been changed. You must load the Gradle changes if you want IntelliJ IDEA to apply them.

Click on the icon, or use ⇧⌘I , or Ctrl+Shift+O on Windows and Linux, to load the changes. Once the Gradle dependency changes have been loaded, we can see the junit-jupiter dependencies in the External Libraries section of our project window.

There’s one last step we need to do for Gradle in order to correctly use JUnit 5. We need to tell Gradle to use the JUnit Platform when running the tests, by adding useJUnitPlatform() to the test section. The final build.gradle file should look like this:

Creating and Running a Test

Now the JUnit dependency is set up correctly, we can create our first JUnit 5 test. Create an ExampleTest using the shortcut to generate code ( ⌘N or Alt+Insert ) in the project window.

Use the same shortcut again inside the class itself to get IntelliJ IDEA to generate a new valid test method for us.

If you’re familiar with JUnit 4, you’ll see the basic test method looks exactly the same, and we can use whichever format name we usually use for our tests. The only difference with JUnit 5 is that it uses the Test annotation from the jupiter package.

JUnit 5 has an Assertions class for all the common assertions we might want to make. We can use partial completion to find the assertion that we want, for example assertEquals.

Now we have our most basic test case:

Run it to make sure everything works. You can run with:

• Run : ⌃R or Shift+F10
• Run Context Configuration: ⌃⇧R or Ctrl+Shift+F10 (Windows/Linux) with the caret inside this method to run just this single test method. If the caret is outside the method, this will run all the tests in the class.
• You can click the green arrow in the gutter of either the test method (to run just the test) or the class name (to run all tests in the class).

When the test runs, IntelliJ IDEA shows the result in the run tool window ( ⌘4 or Alt+4 ). If the details of the passing tests are hidden, we can show all the tests that passed by clicking on the tick in the top left.

Double clicking on the test method name takes us back to that method in the code.

One thing to note for JUnit 5 tests is that the test method doesn’t need to be public in order to work. IntelliJ IDEA will let you know if the class or method can have reduced visibility and still work. Use Alt+Enter to have the IDE remove public from the class declaration, and re-run the test to make sure it works as expected.

Change the test so that it should fail:

When a test fails, IntelliJ IDEA shows the failing test in amber since the test failed an assertion, rather than causing an error (which would be shown in red). We can see the expected value and the actual value side by side, and this should give us an idea of what failed and how.

In our case the cause of the problem should be quite clear since we intentionally put the wrong number in as the "actual" argument.

Configuration: Parameter Hints

Note that IntelliJ IDEA’s parameter hints feature is really helpful for assertion methods. It’s not clear from the method signature which argument is the expected result and which is the actual result. IntelliJ IDEA shows the names of the method parameters as hints, so we can see at a glance which is which.

If we decide this is too much noise in the editor, we can turn off hints for a specific method using Alt+Enter on the hint and selecting "Do not show hints for current method". We can also configure the parameter hints from the IDE preferences, in Editor -> Inlay Hints -> Java -> Parameter hints . We can turn hints on or off and configure which types of methods show hints. We can also see the Exclude list, and remove items from the Exclude list if we decide we want to see hints for this method.

Configuration: Test Runner

We can configure how IntelliJ IDEA runs our unit tests if we’re using Gradle . By default IntelliJ IDEA uses Gradle to build and run the code and tests in Gradle projects. This ensures that when we run the application or tests in the IDE, it works the same way as it would in other environments like the command line or a continuous integration environment. It also ensures that any complex build or setup logic, or code generation, is done. However we might choose to use the IntelliJ IDEA runner to run our tests. In some circumstances this might be faster than using Gradle and provide a faster feedback loop.

Disabling or ignoring tests

Quite often we want to say we don’t want a test to be run. This is common with Test Driven Development as tests will, by definition, fail when we first write them. JUnit 5 supports this with a @Disabled annotation. We can add descriptive text to state why the test is not to be run.

NOTE: tests should usually only be disabled for a short period of time, until the code they are testing is working. If a test is disabled for a long time, perhaps because we don’t know why it doesn’t work or what its expected behaviour is, it’s not adding any value to the test suite. A test like this should be removed.

Like passing tests, IntelliJ IDEA usually hides the full list of disabled tests so we can focus on just the failures. Show all disabled tests by clicking on the grey disabled icon. Click on the test name to see the reason the test was disabled.

Helpful test names for display

JUnit 5 supports a @DisplayName for the test method, so we can add a helpful descriptive name for the test.

When we run the test, it’s this DisplayName that shows in the run window:

Not only does this encourage us to be descriptive, since it’s a text string and not a method name, it supports special characters, which can help readability.

IDE Tip: Live Templates

If we have a standard template for new test methods that we’d like to follow, we could change the default test method template in IntelliJ IDEA , or we could write a Live Template which helps us to create new test methods that look exactly the way we want.

Let’s create a live template to generate a new test method with a DisplayName that is initially converted into a CamelCase and applied to the method name. This encourages us to use the DisplayName annotation to write readable test descriptions, and uses them to create valid method names so the method name is also helpful. The code our Live Template should generate will look something like this:

It’s good practice to have generated tests automatically insert a fail into the generated method – any test should fail first even if we haven’t finished writing it yet

To create this live template , open the preferences and go to Editor -> Live Templates .

Using the "+" in the top right of the scroll pane, create a new live template group called "Test". With this group selected, using the "+" again to create a new live template.

In the live template details in the bottom of the screen:

• Give the template an abbreviation of "test"
• Give it a helpful description, like "JUnit 5 test method"

The key to live templates is creating the template text. This is quite a complex template, so the text is quite advanced:

NOTE: Use fully qualified names (package name plus class name) for the annotations so IntelliJ IDEA knows exactly which class you want. Tick "Shorten FQ names" to have IntelliJ IDEA automatically add the correct import and use only the class name in the annotation.

I like to tick:

• Reformat according to style
• Use static import if possible
• Shorten FQ names

on my live templates, then, when the code is inserted into the class file it usually follows the same standards as the rest of the application.

You need to define the scope the live template applies to, otherwise the IDE won’t know in which sorts of files and at which time it should suggest this template. Click the "define" link next to the "No applicable contexts" warning, and select Java -> Declaration . IntelliJ IDEA will now add this to the list of suggestions when we’re in a Java class file.

Notice the variables in the template. Some of these are built in to the IDE, for example $END is where the caret will end up when the live template finishes inserting all the code. Some are values you’re going to have to define. Let’s define those now. Click on the "Edit variables" button to bring up the variables window.

Set the following values for the variables:

• Expression=<leave blank>
• Default value= "Test name"
• Expression= camelCase(TEST_NAME)
• Default value= "methodName"
• Default value= "org.junit.jupiter.api.Assertions.fail(\"Not implemented\");"
• Tick "Skip if defined"

Press OK on the variables window, and OK on the preferences window.

Check the live template in the editor. Make sure the caret is inside the Java test class, but outside of an existing test method. Type test and press tab. IntelliJ IDEA should generate a test method skeleton, and the caret should be in the value of the DisplayName annotation. Type a readable test description here, and you should see the text description is turned into a valid Java camelCase method name as well. Press Enter when you’ve finished the value for DisplayName , and the caret should move to select the method name in case you want to edit it. Pressing Enter again should place the caret above the fail call.

Multiple Assertions

As we already saw, JUnit 5 supports standard assertions that may be familiar if we’ve used other testing frameworks. In the real world, we often have to check more than one thing to prove something worked the way we expected. Take a list, for example. If we want to check every item in it is correct, we might write multiple assertions to check each value.

This works, it will certainly pass if all the items in the list are as expected. The problem comes when one of the assertions fails. Change the first assertion so it fails:

The output shows that the test fails, and why that was.

What we don’t know though is whether the other assertions passed or failed, because JUnit won’t run the assertions after the first failure. You can see that if you change all the other assertions to fail:

NOTE: you can use column selection mode or multiple carets to easily edit all the "expected" values at once.

Run the test to see once again that only the first assertion fails, we have no idea the others are also broken.

This could be a problem – we’d go back and fix the first assertion, re-run the test, have to fix the next one, re-run the test, and so-on. This is not the fast feedback we’re looking for.

JUnit 5 supports an assertAll assertion . This will check every assertion even if one of them fails. We do this by putting all of the assertions we want to group together into the assertAll call as a series of lambda expressions.

Let’s keep the test with values that should fail, so we can see what happens when we run a failing assertAll :

We can see that all the assertions failed – they were all run even though the first one failed. This makes it much easier for us to see the issues and fix them all in one pass, instead of having to repeatedly re-run the test.

Make the changes to fix the test:

Re-running the test should show everything works:

Assumptions

Now let’s look at assumptions in JUnit 5. Later versions of JUnit 4 supported assumptions , but those of us who are used to working with older tests might not have come across this concept before. We may want to write tests that only run given some set of circumstances are true – for example, if we’re using a particular type of storage, or we’re using a particular library version. This might be more applicable to system or integration tests than unit tests. In these cases we can set an assumption at the start of the test, and the test will only be run if the criteria for that assumption are met. Let’s write a test that should only be run if we’re using an API version that’s higher than ten.

When we run the test, we see that this test runs and passes as expected because the Fixture is returning an API version higher than 10 (for this tutorial, Fixture.apiVersion() returns 13).

Let’s flip the check in the assumption, so the test only runs if the API version is less than 10:

Rerun the test – it should not go green. Since our API version is higher than ten, this check returns false, the assumption is not met, and the test is not run. It shows as a disabled or ignored test:

Data Driven Tests

Earlier we saw that we can use assertAll to group a number of assertions and make sure they’re all run. This is one way of performing multiple checks. There are other cases where we might want to do the same set of checks on different sets of data. For this, we can use parameterised tests . Parameterised tests are where we can pass data into the test as parameters, and with JUnit 5 there are a number of different ways to do this ( see the documentation , it’s very good). We’re going to look at the simplest approach to show how it works.

Let’s use the @ValueSource annotation to give the test method a series of individual values to test.

JUnit 5 supports many different types of array input for this annotation, let’s use an array of hardcoded ints for this test. Each one of these values will be passed into the method individually, so the test method needs a single int parameter, expectedNumberOfSides , to pass the value in.

NOTE: IntelliJ IDEA can help us with parameterised tests in JUnit 5. It lets us know that if we’re using a ValueSource annotation, we shouldn’t be using the @Test annotation but ParameterizedTest instead. We can use Alt+Enter to get IntelliJ IDEA to change any @Test annotations to @ParameterizedTest .

Inside the test method, call the constructor of Shape , passing in the number of sides given to us, and check that the Shape can give us the correct number of sides.

Run the test. In fact, the test runs more than once. The test is run for each one of the int values we put into the ValueSource annotation.

We can change the way these individual tests are shown in the results, by creating a custom name in the ParameterizedTest annotation. For this test, show the value of the number of sides the shape is being created with by using the first parameter ( expectedNumberOfSides ) as the test instance name:

When the test is run, we see the run window shows the number of sides used as the name for each test instance:

Checking Exceptions

Parameterized tests are very helpful for testing large sets of valid data, but they’re also really useful for checking lots of invalid input with the same assertions.

Create a new test to check invalid input. Set up a new ValueSource of int s, but this time the int values will all be invalid numbers of sides for a polygon. Assume that you need to check for too few sides, and assume the code doesn’t support creating Shapes with a very large number of sides:

At this point we should be asking ourselves: "what’s the expected behaviour when the input is invalid?". If we decide that the constructor should be throwing an exception when it is passed invalid values, we can check that with an assertThrows . We tell it which Exception we expect to be thrown, and we use a lambda expression to pass in the method that we expect to throw the exception.

Grouping tests with @Nested

In this final section we’re going to look at one of my favourite features of JUnit 5, nested tests . Nested tests allow us to group specific types of tests together inside a larger class. There are lots of reasons we might want to do this. For example, to group together tests with similar setup or tear down, but that are not so different from other tests in the class that they need to be in their own test file.

We’re going to use this feature to group together all the tests that require a Shape that’s already been set up.

Create an inner class, and add the Nested annotation. We can also add a DisplayName to this the same way we would to a test method.

The nested class can contain fields, of course, and we can use these to store values that all the tests inside this inner class will need. Let’s create a simple Shape to use in these tests.

We can even create Nested classes inside our Nested class. This can be useful to do further grouping. We’re going to use it in this example to group together Happy Path tests, the tests that check everything works as expected under normal circumstances.

Now we can create our specific tests inside our nested classes. With nested classes we’ll probably want to define a naming convention that makes sense when the test results are printed, which we’ll see in a minute. Let’s make this first happy path test a simple check that shows the Shape returns the correct number of sides. We can then create another test which checks the correct description is returned for our shape.

(Note that I’m just showing the inner-most class in this snippet, but it’s still part of the larger class)

Now let’s create a group for tests that show what behviour is not supported, or is not expected. Let’s say that in our example two Shapes with the same number of sides are not supposed to actually be the same shape. This is the listing for the whole class :

If we run all the tests in the class ( ⌃R or Shift+F10 ), we can see our nested tests in the test results. We can see the grouping means the results of similar tests are all grouped together. We can also see how the display name can help us to understand the grouping of the tests.

IDE Tip: Code Folding

If all of these annotations are adding too much noise to the editor, we can always collapse them by pressing on the minus in the gutter, or by using the keyboard shortcut to fold code, ⌘. or Ctrl+. – where "." is the full stop or period on the keyboard. We can hover over the collapsed annotations to see them.

This tutorial has just scratched the surface of the features offered by JUnit 5. To find out more, go to the JUnit 5 documentation , it covers a huge host of topics, including showing the features we’ve seen in this video in more detail. It also covers the steps to take to migrate to JUnit 5 from JUnit 4, which was also covered in blog and video .

Top shortcuts

This blog post includes some shortcuts, but many more were demonstrated in the video and not all of them were mentioned here:

• ⌘[ or Ctrl+Alt+left arrow Navigate back – makes it easy to navigate between all the places you’ve been while writing code and running tests
• ⌘⇧T or Ctrl + Shift + T Navigate between test and test subject. This is explored in the Top 5 Navigation Tips (blog and video).
• ⌃⇧J or Ctrl+Shift+J Join Lines to create compiling code.
• ⇧⌘⏎ or Ctrl+Shift+Enter Complete Statement close off the brackets and statement, and it also formats the code too.
• ⌃⇧Space or Ctrl+Shift+Space Smart completion
• Migrating from JUnit 4 to JUnit 5 – blog, with embedded video
• Unit Testing and Coverage in IntelliJ IDEA (video)
• Editor Tips & Tricks (blog and video)
• Top 15 IntelliJ IDEA Shortcuts Blog (blog and video)

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Unit Testing in C# With Moq – Wake up the Testing Genius Inside You

Unit testing is a powerful way to ensure that your code works as intended. It’s a great way to combat the common “works on my machine” problem.

Using Moq , you can mock out dependencies and make sure that you are testing the code in isolation.

Moq is a mock object framework for .NET that greatly simplifies the creation of mock objects for unit testing. Mocking is a popular technique for unit testing that creates test double objects, which gives you the ability to control the behavior of those objects by setting their outcomes.

This article will show you how to unit test your C# code with Moq.

Table of Contents

What is a test double, mock, and stub?

Test doubles are objects that mimic the behavior of real objects in controlled ways. The goal of test double objects is to allow for the concise development of tests that affect only one object in isolation. Mocking frameworks allow for the separation of a system under test from its dependencies to control the inputs and outputs of the system under test.

In a nutshell, a test double is a fake object used to ensure the behavior of some external system is the same as the original object.

For example, suppose you’re building a web application, which gets data from a database. If you want to write tests for that application, you would need to provide a clean, pre-populated database with real data for every test. This is where test double comes into play. It can substitute the concrete class that performs database fetch and return fake data. But the code you are testing won’t know the difference. This assumes you are using dependency injection in your code. So you can easily replace one implementation of an interface with another in tests.

Faking objects is a modern software engineering practice. By mocking objects, you ensure your unit test only covers the code you actually want to test. This also means your test code can be fast since your testable code is isolated from its environment.

Even though all these fake objects are often called mocks, when I’m talking about mocks, I distinguish them from stubs.

What is a stub?

A stub object is a fake object used to test some unit of code without using a real object. The idea is that you can simulate an object’s behavior by setting up some test code that will throw errors, return incorrect data, or otherwise do what an object would do in certain situations. This allows you to run tests without connecting to a database or make any other calls to the outside world. This is important in tests because you don’t want to wait for a web call to run the test.

What is a mock, then?

A mock object goes a bit further. When you use it in your unit test case, it checks that the system under test interacts with other objects as expected. It can check that a dependency was called with specific arguments or that a certain call didn’t happen.

The outcome of the unit test is then checked against the mock object.

You can learn a lot more about mock vs. stub difference and find out when to use each of them in my separate blog post .

Moq framework

The Moq framework is a set of interfaces that allow you to stub or mock your code for unit testing purposes. Clarius, Manas, and InSTEDD developed it to facilitate the writing of unit tests. Due to the framework’s ability to create test doubles, it is often used to develop automated tests.

The biggest benefit of using this mocking framework is that it can create those test doubles, dependencies on the fly. This greatly improves the speed of writing unit tests ( since many developers don’t have time for testing ). You can use it for testing code that interacts with web services, databases, or any other class that is likely to be used in a unit test.

The result is that you can verify that your code behaves the way it is supposed to and quickly discover when a bug occurs.

How to install it?

To install the Moq framework to your test project, you can easily add it as a NuGet package :

The other option is to select your unit test project and manage NuGet packages through UI.

Benefits of using Moq

There are many benefits of using the Moq testing framework:

• It is easy to set up a system under test – Moq makes testing easier because it generates appropriate mocks/stubs for you. This means that you can focus your time and energy on testing the logic of your code rather than on designing tests.
• It is easier to unit test more complicated objects – Moq makes it easier to unit test more complicated objects without writing a lot of boilerplate code. It also makes it easier to assert that your tests are passing.
• It is easy to use and understand – Moq has a clean and intuitive API interface, making it easy to use and understand for both new and experienced developers.
• It is easy to find examples on the web – Moq is a trendy open-source mocking framework. There are plenty of examples on the web, so you don’t have to worry!
• You can use it early in the development – if you practice test-driven development , you can create an interface that doesn’t yet have the implementation. After that, you can use Moq in your tests to simulate how the implemented interface will behave in the future. By doing this, you can refine methods of the interface to better suit your needs.

Simple Unit Test Example With Moq

Let’s see some examples of how to use Moq while writing unit tests in C#.

The class under test

We have the following AccountService class that has dependencies on IBookService and IEmailSender .

We want to test these three methods, GetAllBooksForCategory , GetBookISBN , and SendEmail . The methods use the IBookService and IEmailSender dependency.

You will see how to use Moq to have a fake instance of IBookService .

What about IEmailSender ? Should you also create a fake instance of it in your tests? Well, you don’t need to if you test GetAllBooksForCategory and GetBookISBN methods. They don’t use the email sending module, and that’s why feel free to pass null in those tests to the AccountService constructor.

Always write the least amount of code in your test method you need for the test to pass. – Kristijan Kralj

You can use Moq to provide fake instances of the dependencies, but if the test will pass with null, use the null value instead. This will decrease the logic in your unit test and make it simpler to understand.

Later, when we test the SendEmail method of the AccountService , we will use Moq to create a mock instance of the IEmailSender .

The first test using Moq

I will create a new test class AccountServiceTests , to write tests for these methods. If you want to learn how to name your methods, classes, and unit test project properly, I suggest you read the post about unit testing naming conventions .

The first method, GetAllBooksForCategory , is simple. It queries the IBookService and returns the result. To write a unit test that will check that the method works as expected, we need to write a stub for the IBookService .

What does the code mean in the GetAllBooksForCategory_returns_list_of_available_books unit test:

• Start by creating a new fake object using a generic Mock class.
• We need to set up this mock instance to return a list of books when the GetBooksForCategory method is called with the “UnitTesting” parameter.
• Pass the fake instance to the AccountService’s constructor by calling the Object property.

You can see in the test runner output window that the test passes if you run the test. This means that the fake service has returned 3 books when called.

How to set up multiple methods on a stub

The next method of AccountService we want to test is the GetBookISBN . It uses two methods of the IBookService , GetBooksForCategory and GetISBNFor .

• As in the last test, start by adding a stub for the GetBooksForCategory method.
• After that, set up the second method, GetISBNFor .

If you run the test, it should pass.

Common usage examples

Before we move to implement mocks with the Moq, let me show you some common usage examples.

Ignore input parameter value

In the last example, I have used the parameter matching to set up the fake object. This expects that the method is called with the “UnitTesting” paremeter.

However, you can simplify this. If you don’t care about the input parameter of the mocked method, you can use It.IsAny.

Throw exception

Sometimes, you want to check your code’s robustness and resistance to failure by throwing an exception. To throw an exception, use the Throws mock function:

Setup Properties

Often, you need to set up properties of an interface to return a specific value:

If you want to stub all properties, you can use the StubAllProperties setup method:

Moq support various event’s setup options:

The Callback method is a nice way to capture the parameter that was passed to a method. Let’s take a look at the following test.

In this example, the passedParameter variable will get the value “UnitTesting”. That’s the value that was passed to the GetBooksForCategory method.

Sequential calls

There are also times when you will call a single method multiple times during a single test. In that case, you can choose what value will be returned on every call.

How to create mocks and verify expectations

Creating mocks and verifying expectations in unit tests using can be a tricky and frustrating process. Especially if you don’t use a mocking framework. Then you need to implement your mocks manually and track which method was called and when.

If you use a Moq and know how to set up mocks, this doesn’t have to be difficult. You just need to follow a few simple steps, and you can create effective mocks and verifying code that works for you.

The easiest way to understand how mocks works is by writing a test.

We want to test that the SendEmail method calls IEmailSender with correct arguments. We can’t test the actual email sending, since that wouldn’t be a unit test anymore.

The test does the following:

• It creates the IEmailSender mock.
• It passes that mock to the constructor.
• It calls the method we want to check, SendEmail , with parameters.
• The Verify method is used to check that what were the passed arguments to the SendEmail method. It also asserts that the method was called only once using the Times.Once parameter.

Testing Asynchronous Code with Moq

Modern applications are typically made up of large microservices with many different moving parts. When you’re writing these sorts of applications, the applications need to talk asynchronously. But it also important to test your code.

Unit testing asynchronous code is the black sheep of the software testing world. For some reason, it’s often thought to be so difficult that people avoid it altogether.

This can be a huge mistake because testing asynchronous code is not only possible but also relatively straightforward.

So, how can you tell Moq to return a Task or Task<T> ?

Well, you can use ReturnsAsync :

Starting with Moq 4.16, you can also use the Returns method by combining it with the Result property of your task:

Testing Exception was thrown

Modern applications use exceptions to signalize that the error has occurred or something unexpected has happened. When you get an exception in your code, there is a whole chain of events that occur. The exception usually propagates through the call stack until it is handled properly.

While writing unit tests, you want to cover cases where an exception will happen if the input is invalid.

Luckily, Moq also covers this.

Multiple mocks in a single test

Can you have more than one mock instance in a single test method? The answer is an absolute yes.

Should you have more than one mock instance? The answer is absolutely no.

You can have several stubs and one mock, but avoid having more then one mock in a single test case.

What’s the problem if you have more than one mock in a single unit test?

If you have two or more mocks that you are asserting against in your test, this means you are probably checking more than one scenario. Remember, a single unit test should only cover one scenario of your system under test. By checking more scenarios, you are making the test more unstable and less maintainable. Once the test fails, you need to go and check every object you have mocked to see what’s the issue. And this will take more time if you have multiple mocks.

The best thing you can do for your unit test is to make it as simple as possible.

What’s the big deal about testing in isolation?

You should write unit tests to test the smallest possible piece of functionality in an application. By isolating the unit tests from any external influence, you can be sure that the test is testing the functionality and not some other part of the application that may change in the future.

Can you mock a class?

While mocking a class is not recommended, it is possible to do so. To make a class mockable, it needs to be a public class, and the method you want to mock has to be virtual.

Can you use Moq with MSTest?

Yes, you can use Moq with MSTest. Moq should support every popular unit testing framework, including xUnit, NUnit, and MSTest.

Why is mocking necessary in unit testing?

Mocking is necessary in unit testing because you have to isolate the code that you are testing from the surrounding code. This can be done by replacing the actual dependencies with a mock or stub.

What is a mock repository? How do you create a mock repository?

A mock repository is a way to centralize the management of your mocks. You can do that using MockRepository.

Kristijan Kralj

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Best Practices for Writing Unit Tests

Unit Tests. That sounds exciting. Everyone knows the importance of tests and having good code test coverage, at least in theory.

But there is only one problem with it. Nobody likes to write unit tests. Typically, the importance of unit testing is discovered only when the fundamental flaws of the application are found and need serious refactoring.

This article will discuss the most modern practices for writing unit tests that your team could use. It might be surprising, but these days you can use automated unit test generation tools to help you achieve that goal.

Let’s start with the theory and then progress further to discuss how things could be done in a more efficient and modern way.

What is unit testing?

Unit testing is a software testing technique that involves breaking down a piece of code, typically a function or a method, into small and independent units and testing each unit individually. Unit testing aims to ensure that each unit of code works as intended and can be integrated into a larger system without issues.

The main idea behind unit testing is to isolate each unit of code and test it in isolation. This is achieved by replacing any external dependencies, such as a database or a network connection, with mock objects or stubs.

This way, the unit of code being tested depends only on itself and its immediate inputs, making it easier to test and debug.

The benefits of unit testing are numerous. Primarily, unit testing helps catch bugs early in the development process, making them easier and cheaper to fix.

By testing each unit of code in isolation, test engineers and developers can quickly pinpoint the root cause of any bugs and fix them before they propagate to other parts of the system.

Why perform unit testing?

There are multiple benefits of writing and executing unit tests:

• Early detection of defects: Unit testing helps to identify defects early in the development cycle when they are less expensive to fix.
• Improved code quality: Unit testing promotes the development of high-quality code by forcing developers to write testable and maintainable code.
• Faster development cycles: Unit testing reduces the time required for debugging and troubleshooting, allowing developers to focus on new features and functionality. Less QA time is necessary, too.
• Better collaboration: Unit testing helps developers to collaborate more effectively by providing a common language and a shared understanding of the software.
• Greater confidence in changes: Unit tests provide developers a safety net when making codebase changes, giving them the confidence to refactor and optimize without fear of breaking existing functionality.

Unit testing is an essential practice for any software development team looking to improve the quality and maintainability of their code while reducing the cost of development and maintenance over time.

What makes a good unit test?

The main point of writing unit tests is to improve your team’s software development efficiency. Here are some best practices for writing effective unit tests:

• Keep tests small and focused: A unit test should only test a single unit of functionality in isolation. Keep tests small and focused on making them easier to maintain and quickly identify the source of any issues.
• Write tests before code: Writing tests before writing code helps you to think about the desired functionality and to clarify requirements. This also makes it easier to write testable code.
• Use descriptive test names: Descriptive test names help to communicate the intent of the test and make it easier to understand the test’s purpose when reading the code.
• Use assertions to verify behavior: Use assertions to check that the output of the code matches the expected behavior. Make sure that your assertions check the right things to ensure the test is testing the functionality you intend.
• Use test fixtures to set up and tear down state: Use test fixtures to set up the state required for the test and clean up after the test. This ensures that each test runs in isolation and doesn’t interfere with other tests.
• Test edge cases: Be sure to test edge cases such as boundary conditions, empty or null inputs, and other less common scenarios that could still occur.
• Run tests regularly: Set up an automated test runner to run tests regularly as part of your continuous integration process. This helps to catch issues early on and ensure that changes don’t break existing functionality.
• Refactor tests with code changes: If you make changes to the code, make sure to refactor the tests as well to ensure they still test the intended functionality.
• Use code coverage tools: Use code coverage tools to ensure that your tests cover all the code that needs testing.
• Unit test automation: Last but not least, you can use specific tools to perform the unit test generation. You don’t need to write everything manually anymore. We will discuss using such tools in a moment.

By following these best practices, you can write effective unit tests that help to ensure that your code is robust and behaves as intended.

How to write a unit test manually?

We will discuss the Python example in this article.

Step 1: Set Up the Environment

Before diving into writing unit tests, we must set up our environment. For this, we will use Python’s built-in testing framework, unittest. Unittest is a testing framework that comes with Python and provides tools for writing and running tests. To use unittest, we need to import it into our Python script:

Step 2: Write the Test Cases

Once we have set up the environment, we can start writing our test cases. A test case is a unit of testing that verifies the functionality of a specific feature or behavior of the code. Test cases are typically organized into test suites, which are collections of related test cases.

Suppose we have a simple function that takes two arguments and returns their sum. Here’s how we can write a unit test for this function:

In this example, we have defined a function called add_numbers that takes two arguments and returns their sum. We have also created a test case called TestAddNumbers that contains a single test method called test_add_numbers.

This method tests whether the add_numbers function returns the expected result for different input values. We have used the assertEqual method of the TestCase class to compare the actual output of the add_numbers function with the expected output.

The assertEqual method raises an AssertionError if the two values are not equal. In this case, if the add_numbers function returns the wrong result, the test case will fail.

Step 3: Write the Test Cases

Once we have written our test cases, we need to run them. We can do this by executing the following command in the terminal:

While unit_test.py is the name of the Python file that contains our test cases, this command will run all the test cases defined in the file and report any failures or errors.

Step 4: Analyze the Results

After running the tests, we must analyze the results to determine whether our code works as expected. Unittest provides a report of the test results that we can use to identify any errors or failures. For example, the following is a sample report:

Ran 1 test in 0.000s

This report indicates that our tests have passed.

How to automate unit testing?

Manually writing the unit tests can be repetitive and challenging, and you might miss important code paths. However, this task could be automated. We could use the power of AI tools such as Codium.AI to write those tests for us.

This tool can help write automated unit tests using machine learning techniques to generate test cases covering a wide range of inputs, outputs, and edge cases.

Let’s follow the previous example of our add_numbers test. We need to install the Codium.AI extension from Visual Studio Code Marketplace and write our sample function once again:

Once we do that, we will see the hint that the Codium AI plugin shows us. Click it to see the unit test generation magic.

Here are some ways Codium.AI can assist in automated unit testing:

Those are the example automated unit tests that Codium.AI has provided for us:

As you can see, it expanded the unit test coverage and allowed us to enhance our coverage. We can use it to understand the scope of testing in more complex examples.

Can unit testing replace manual testing?

Unit testing can complement manual testing but cannot replace it entirely. Manual testing involves a tester executing a software application’s features and functionalities to identify defects, bugs, and usability issues. On the other hand, unit testing involves writing automated tests for individual units or components of the software to ensure they function as intended.

Unit testing can help reduce the number of defects found during manual testing by catching issues early in the development cycle. This can save time and money by allowing developers to fix issues before they become more challenging and costly to address.

However, unit testing cannot replace manual testing entirely because it cannot capture all the issues a manual tester might find. Manual testing can identify usability issues, performance problems, and other issues that might not be apparent through unit testing alone.

Therefore, unit and manual testing should be used in combination to achieve the best results. Unit testing can help ensure that individual units of code are functioning correctly, while manual testing can help ensure that the software as a whole is meeting the intended requirements and is usable for end-users.

We have discussed the benefits of unit testing and explored automatic unit test generation using the power of CodiumAI and Python. We have seen how to write test cases using the unittest framework, how to run the tests, and how to analyze the results.

Implementing unit testing strategies early in the development process is important, ideally before any code is written. This can help catch issues early on and prevent them from snowballing into larger problems later.

By following these best practices, developers can create robust and reliable unit tests that help ensure the quality and stability of their code.

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Last Updated: January 5, 2024

This article was reviewed by Anne Schmidt . Anne Schmidt is a Chemistry Instructor in Wisconsin. Anne has been teaching high school chemistry for over 20 years and is passionate about providing accessible and educational chemistry content. She has over 9,000 subscribers to her educational chemistry YouTube channel. She has presented at the American Association of Chemistry Teachers (AATC) and was an Adjunct General Chemistry Instructor at Northeast Wisconsin Technical College. Anne was published in the Journal of Chemical Education as a Co-Author, has an article in ChemEdX, and has presented twice and was published with the AACT. Anne has a BS in Chemistry from the University of Wisconsin, Oshkosh, and an MA in Secondary Education and Teaching from Viterbo University. This article has been viewed 28,606 times.

Unit tests are a good way to check programs as they are being developed. To write them, you’ll need to break your program down into independent units, and create tests that examine each unit one by one in a controlled way. Analyze your results and use them to improve your program’s code. Though no test can check for all potential bugs, running effective unit tests will help ensure that your program works as expected.

Planning Unit Tests

• The definition of a “unit” varies widely depending on the type of program you are developing. A unit could be a class, but also a single function or procedure.

• Simple tests will help ensure that you really are testing only one unit at a time.
• The tests’ code will be reliable. If you have complex test code, it will be more prone to problems, making it just that harder to see bugs in the code of the program you are testing.
• The tests will be faster, decreasing the overall amount of time it takes to do the testing.
• A simple test will be readable, meaning you may see some potential problems just by looking at the code itself.

• Integration tests also usually require external elements, such as web servers or a database. To keep unit tests controlled, write them so that they don’t require external elements.

Using the Arrange, Act, Assert (AAA) Approach

• You can try running your unit test with really simple data, or "dummy data." This can help you quickly assess if the unit works well.

• For example, you might initialize a unit that performs some arithmetic on a set of numbers.

• For example, the requested action might be to give the sum of a set of numbers.

• For example, if you want a unit to give the sum of only the even numbers from a set, you will expect the sum to also be an even number. If the unit gives an odd number as a result, then it has failed the test.

• If your hypothetical SUT from the previous example provided an odd sum instead of an even one, for instance, you can check the code that produced the sum, as well as the code that retrieved the even numbers from the set, in order to see where the error is.

• Continuing with the previous example: if your SUT produces even sums, that doesn’t necessarily prove that it’s working correctly--it might just be giving false sums. Try the unit test with some bad data, like a set of only odd integers. The program should indicate that it could not produce the sum of all even numbers in the set because there were none in the set.
• If you put in bad data and the test makes it seem like nothing is wrong (for example, it still provides a sum), then you know that there’s a problem with the unit (for example, perhaps the code is retrieving odd numbers instead of even ones).

Writing Testable Code

• Writing the tests first encourages you to write just enough code to make the program do what it needs to, without inadvertently including unnecessary or bad code.

• For example, avoid things like hidden inputs and non-deterministic factors in your program’s code.

Expert Q&A

• If you want specific guides on best practices in writing unit tests for a particular programming language, a quick internet search should pull up some helpful tutorials. Thanks Helpful 0 Not Helpful 0

You Might Also Like

• ↑ https://www.toptal.com/qa/how-to-write-testable-code-and-why-it-matters
• ↑ https://www.codeproject.com/Articles/10105/Writing-Your-First-Unit-Test
• ↑ http://radio-weblogs.com/0100190/stories/2002/07/25/sixRulesOfUnitTesting.html
• ↑ https://msdn.microsoft.com/en-us/library/hh694602.aspx

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How to Write Test Cases in Java Application using Mockito and Junit?

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Mockito is an open-source testing framework used for unit testing of Java applications. It plays a vital role in developing testable applications. Mockito is used to mock interfaces so that a dummy functionality can be added to a mock interface that can be used in Unit Testing. Unit Testing is a type of software testing in which individual components of the software are tested. The major objective of using the Mockito framework is to simplify the development of a test by mocking external dependencies and using them in the test code. And as a result, Mockito provides a simpler test code that is easier to understand, more readable, and modifiable. Mockito can also be used with other testing frameworks like JUnit and TestNG . 

JUnit framework is a Java framework that is also used for testing. Now, JUnit is used as a standard when there is a need to perform testing in Java. So in this article, we will be discussing test cases in a java application using Mockito and Junit .

Step by Step Implementation

Step 1: Create a Maven project in your favorite Java IDE (IHere we are using IntelliJ IDEA )

Step 2: When you have successfully created a maven project you have to add some dependencies in your pom.xml file. We have to add the following dependency in your pom.xml file.

Dependency for Mockito is as follows :

Dependency for Junit is as follows :

Implementation: Below is the complete code for the pom.xml file

Step 3: Now you have to create one interface named as TodoService and one class named as TodoServiceImpl. 

Step 4: Now we are going to perform unit testing for the retrieveTodosRelatedToJava() method that is present inside the TodoServiceImpl.java file. To create the test class follow these steps. At first Right-click inside the TodoServiceImpl.java file. 

Then click on the Generate button.

Then click on the Test button.

A pop-up window will be shown like this. Here you can modify your test class name. Also, check the setUp and the method that you want to perform unit testing. 

Now you have successfully written two test cases for your java application. Now to run the test cases click on the green button as shown in the below image. And you can see your test cases have been passed. 

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Software Unit Testing: A Complete Guide

Unit Testing is a Software Testing technique where small units or components of a Software Application are tested in isolation to ensure they perform as expected. Explore the use cases of Software Unit Testing, why it's essential for building robust applications, and how it contributes to the overall quality assurance process.

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What if we told you that the secret to flawless software lies hidden in plain sight? Imagine a team of developers working on critical banking software, where a single error could mean financial chaos for thousands of customers. One day, a new feature is introduced, and with it, a subtle bug slips through the cracks. However, thanks to the vigilant practice of Software Unit Testing, the anomaly is caught early. The test cases, meticulously crafted to simulate real-world transactions, reveal the flaw before it ever reaches production.  

This blog on Software Unit Testing will provide insights into this meticulous craft. Here, we unravel the art and science of testing each minuscule unit of code, ensuring they perform as intended. Software Unit Testing isn’t just about catching bugs; it’s about fostering a culture of excellence and precision that elevates your code to new heights. Read along to learn more.  

Table of Contents  

1) What is Software Unit Testing? 

2) Approaches to Software Unit Testing 

3) How Unit Tests work? 

4) Software Unit Testing Methodologies 

5) Exploring Software Unit Testing Best Practices 

6) Software Unit Testing Advantages  

7) Software Unit Testing Disadvantages 

8) Conclusion 

What is Software Unit Testing?  

In Unit Testing, individual units or components of a Software Application are tested in isolation to confirm their proper operation. These units might be functions, methods, or classes, and the testing focuses on verifying their behaviour against designated test cases. 

During Unit Testing, each component is evaluated separately from others and any external dependencies, using frameworks such as JUnit for Java or NUnit for .NET. The tests are designed to cover all possible scenarios, including typical usage, edge cases, and error conditions, ensuring the unit performs correctly under various circumstances. 

The benefits of Unit Testing are numerous. It helps catch defects early in the development cycle, speeds up feedback loops, and enhances the overall quality and maintainability of the code. Automated testing allows developers to quickly identify and resolve issues, which reduces the risk of future regressions and boosts the software’s reliability and stability. 

Overall, Unit Testing is vital in the Software Development Lifecycle. It supports the creation of robust, maintainable codebases and promotes best practices such as modular design and a proactive approach to quality and accountability within development teams.  

Approaches to Software Unit Testing  

Let’s look at some best tactics to Software Unit Testing:  

Image Desc.- Unit Testing tactics 

Boundary validation  

Boundary validation in Unit Testing focuses on examining how a unit behaves at the extremes of its input domain. This method tests the unit's behaviour when inputs are at their minimum, maximum, or precisely at boundary values. By rigorously checking these boundary conditions, developers can identify potential issues, such as off-by-one errors or unexpected behaviours that could emerge as inputs near these critical points. 

Error handling evaluation  

Error handling evaluation tests how a unit reacts to various error scenarios or exceptional circumstances. This testing ensures that the unit correctly identifies, reports, and handles errors or unexpected conditions robustly and reliably. By evaluating error-handling mechanisms, developers aim to confirm that the software manages failures gracefully and avoids unintended outcomes or crashes. 

Logic assessment  

Logic assessment in Unit Tests is the procedure conducted to test and verify the correctness of decision-making and computational processes executed by the software. In particular, the method tests the internal logic of the code, including control flow (if-else, loops), algorithms, and business rules. Test cases should be created such that they keep in view the fact that they cover each logical path and branch of the application so that the software can run properly in every kind of situation.  

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How Unit Tests work?  

Unit Testing involves four main phases: 

a) Planning and setting up the environment: During this phase, developers decide which units of the code need testing and how to effectively execute all relevant functionality for each unit. This setup is crucial for preparing the testing landscape. 

b) Writing the test cases and scripts: Here, developers write the actual Unit Test code and prepare scripts to execute the tests. This stage involves creating detailed tests that cover various scenarios the software might encounter. 

c) Executing the Unit Testing: In this phase, the Unit Tests are run, and the behaviour of the code for each test case is observed. This step reveals whether the code performs as expected under different conditions. 

d) Analysing the results: After testing, developers analyse the results to identify any errors or issues in the code. Based on this analysis, necessary fixes are applied to improve the software’s stability and functionality. 

Test-Driven Development (TDD) is a common strategy employed in Unit Testing, where a test is written before the actual application code. Initially, this test will fail since the corresponding functionality doesn't exist yet. The developer then writes just the code required to make the test pass, promoting high-quality, consistent coding practices. 

Effective Unit Testing Practices include the following: 

a) Running each test case in isolation, using stubs or mocks to simulate external dependencies. This ensures that tests only focus on the unit under test. 

b) Concentrating on critical features of the unit under test rather than testing every single line of code. 

c) Using assertions in the code to verify each test case. These assertions help the testing framework to execute tests and report failures. 

d) Performing tests frequently and early in the development cycle to catch issues as soon as possible. 

e) After thorough Unit Testing, the next step typically involves integration testing, which evaluates how different components of the software interact with each other. 

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Software Unit Testing Methodologies  

Here are some of the most effective Unit Testing Methodologies:  

Functional Unit Testing  

This type of testing evaluates individual units of code based on their functional requirements or specifications. Test cases are crafted to ensure that each unit performs its intended function correctly, adhering to the specified behaviour or business logic. Functional Unit Testing verifies that the units deliver the expected outputs for a given set of inputs, thus validating their functionality and compliance with requirements. 

Error-driven techniques  

These techniques focus on creating test cases specifically designed to trigger and assess the error-handling capabilities within code units. The goal is to expose vulnerabilities or weaknesses in error detection, reporting, and recovery processes. By intentionally inducing errors or exceptional conditions, developers can evaluate the robustness and effectiveness of error handling mechanisms, ensuring that the software can handle failures gracefully and avoid undesirable outcomes. 

Structural Unit Testing  

Also known as white-box testing, this approach tests the internal structure and implementation details of code units. Test cases are designed based on the code structure, such as branches, loops, and paths, to achieve comprehensive code coverage. Structural Unit Testing aims to verify the correctness of the code's logic, identify any potential bugs or defects, and ensure that all execution paths are tested. This enhances the reliability and quality of the software. 

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Exploring Unit Testing Best Practices  

Here are some of the essential practices to Unit Testing: 

Asserting once  

This best practice recommends limiting the number of assertions in each Unit Test to just one. By focusing on verifying a single logical behaviour or outcome, the tests become clearer, more maintainable, and easier to read. This approach helps ensure that each test case remains focused on a specific aspect of the unit's behaviour, simplifying the process of diagnosing and fixing any issues. 

Implementing Unit Testing  

Integrating Unit Testing at the start of the Software Development process is crucial. This involves writing unit-level tests alongside the implementation code to ensure they are comprehensive, relevant, and aligned with functional requirements. Early adoption of Unit Testing allows developers to identify and rectify defects sooner, ultimately enhancing code quality and reducing overall quality assurance costs. 

Using a unit test framework  

Adopting a Unit Test framework provides a structured and standardised method for writing and executing tests. These frameworks typically offer features such as test runners, assertions, and setup/teardown mechanisms, which streamline the testing process and promote consistency across tests. By leveraging such frameworks, developers can facilitate easier and more productive test development, which enhances collaboration within development teams. 

Automating Unit Testing  

Automating Unit Testing involves using tools and scripts to run tests automatically without manual intervention. This increases the efficiency, repeatability, and reliability of the tests, enabling quicker feedback cycles and continuous integration. Automation helps developers quickly identify regressions, validate changes to the code, and maintain the integrity of the software, fostering a culture of quality and agility within the team. 

Unit Testing Advantages  

Here's a breakdown of the benefits of Unit Testing in Software Development: 

a) Early defect detection : Unit Testing allows developers to catch defects early in the development process while they are still localised, enabling them to address issues before they escalate into more significant problems. 

b) Improved code quality : By testing individual units of code in isolation, Unit Testing helps enhance the overall quality of the code. This isolation reduces the likelihood of introducing bugs and errors into the software. 

c) Faster development cycles : Unit Testing provides rapid feedback on code changes, which facilitates quicker iterations and refinements. This speed helps developers make multiple small improvements efficiently, shortening the overall development cycle. 

d) Facilitates refactoring : Unit Testing acts as a safety net that allows developers to refactor code confidently. It ensures that the system’s functionality remains intact even after changes are made. 

e) Supports regression testing : Unit Tests form the basis for regression testing, ensuring that new code changes do not inadvertently disrupt existing functionality. 

f) Encourages modular design : Unit Testing promotes the separation of concerns and modularisation of code, leading to more maintainable and extensible software. This approach supports building software that is easier to understand and update. 

g) Enhances collaboration : Unit Tests serve as executable documentation of code behaviour, facilitating collaboration among team members by promoting a shared understanding of the codebase. 

h) Validates requirements : Unit Testing ensures that each code unit operates according to the specified requirements and behaves as expected, thereby validating that the software functions correctly. 

Unit Testing Disadvantages  

Here are some challenges and limitations associated with Unit Testing:  

a) Time-consuming : Scripting and maintenance of Unit Tests can be particularly time-consuming for large and complex codebases, potentially slowing down the development process. 

b) False sense of security : Unit Tests check the behaviour of individual units in isolation, which might not reveal integration issues or interactions between components, creating a false sense of security. 

c) Code duplication : Writing Unit Tests often involves duplicating logic from the implementation code, leading to redundancy and increased maintenance overhead. 

d) Test maintenance : As the codebase evolves, Unit Tests require frequent updates to stay relevant, adding to the maintenance burden. 

e) Difficulty in testing external dependencies : Unit Tests can be challenging to write for code units that rely heavily on external dependencies like databases, network services, or third-party libraries. 

f) Overhead of test infrastructure : Establishing and maintaining the infrastructure for testing, including test frameworks, mock objects, and test data, can be complex and resource intensive. 

g) Limited coverage : Unit Tests may only cover a limited portion of the codebase, potentially leaving significant gaps that could harbour undetected defects. 

h) Resistance to change : Developers might resist writing Unit Tests due to perceived time constraints or lack of expertise, which can result in poor test coverage and diminished testing effectiveness. 

Conclusion  

In Software Development, Software Unit Testing is the thread that weaves reliability into every feature. It’s the quiet assurance that our digital creations will stand the test of time and use. May this guide inspire you to craft code that’s not only functional but truly formidable. 

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Frequently Asked Questions

Unit Testing should be performed during the development phase, after each new piece of code is written, to ensure that each individual unit functions correctly. It’s a continuous process that ideally occurs before integration testing and throughout the coding lifecycle. 

Unit Testing is typically conducted by developers on their local machines or in a dedicated development environment. It’s done to validate each unit of the software code independently, ensuring that it performs as expected before integration into larger systems. 

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The Knowledge Academy offers various Software Testing Courses , including the Certified Software Testing Professional (CSTP) Course and Introduction to Unit Testing Course. These courses cater to different skill levels, providing comprehensive insights into Quality Assurance in Software Testing .  

Our Business Analysis Blogs cover a range of topics related to Software Testing, offering valuable resources, best practices, and industry insights. Whether you are a beginner or looking to advance your Unit Testing skills, The Knowledge Academy's diverse courses and informative blogs have got you covered. 

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