Shopware plugin unit testing helps you verify that every unit of code is working as expected and error-free and helps you create higher quality plugins. Unit testing is a WhiteBox testing method that is usually performed by a developer. How to unit test Shopware 6 plugins correctly?

The starting point for testing your own plugin is phpunit.xml [.dist] _ in the plugin root directory and the Bootstrap.php file in ./Tests/Functional/ your plugin directory.

With Bootstrap.php, you can properly set up your environment to prepare it for testing. You can initialize the kernel, register event subscribers and namespaces, or initialize the store context to make currencies available. To prepare the plug-in for testing, you need to require the Shopware startup helper.

The lack of unit testing when writing code significantly increases the level of defects during further (integration, system, and acceptance) testing. Good unit testing during development saves time and, therefore, ultimately money.

Want to know more about testing? Then hurry up to read our article Shopware vs. Magento 2: Performance Testing in 2021. There is a lot of useful stuff!

In the hiring process as well as the recruitment process, most developers are asked about their favourite tool for doing angular application testing. To test an angular component, directive, pipe, etc. there are various frameworks available in the market. We recommend the Jasmine framework and Karma tool. Although there are other frameworks too, they are mentioned below in the form of an image.

Tools and Frameworks required to make testing simpler for Angular developers.

Jasmine

Jasmine, a behavior-driven development (BDD) framework, stands as a cornerstone for testing JavaScript code. Renowned for its simplicity and robust features, it is programming language that's extensively employed in testing Angular applications.

With Jasmine, developers benefit from a clean syntax for articulating test cases and an embedded assertion library for defining expectations. Its suite and spec organization feature facilitates the management of extensive test suites with ease.

Karma

Karma, a test runner crafted by the Angular team, specializes in executing unit tests. Seamlessly integrating with Jasmine and other testing frameworks, Karma enables developers to run tests across diverse browsers, languages and devices.

Providing real-time feedback during development, Karma automatically re-runs tests upon detecting code changes. Additionally, it offers code coverage reports to pinpoint areas of insufficient test coverage.

Protractor

Protractor, primarily an end-to-end testing framework, extends its functionality to support unit testing using Jasmine. Ideal for scripting functional tests that emulate user interactions, Protractor's Angular-specific capabilities include testing directives and components. Its comprehensive approach makes it invaluable for thoroughly testing Angular applications.

TestBed

TestBed, a utility provided by Angular, facilitates the isolation testing of Angular components. Developers can create a testing module containing essential dependencies like services and providers. By utilizing TestBed, developers can instantiate and manipulate components within a controlled environment, simplifying the testing of individual components without reliance on the entire application context.

Below code snippet below shows the usage of TestBed for TestComponent.

import { ComponentFixture, TestBed } from '@angular/core/testing'; 
import { TestComponent } from './test.component'; 
describe('TestComponent', () => { 
  let component: TestComponent; 
  let fixture: ComponentFixture<TestComponent>; 
  beforeEach(async () => { 
    await TestBed.configureTestingModule({ 
      declarations: [ TestComponent ] 
    }) 
    .compileComponents(); 
  }); 
  beforeEach(() => { 
    fixture = TestBed.createComponent(TestComponent); 
    component = fixture.componentInstance; 
    fixture.detectChanges(); 
  }); 
} 

Angular Testing Utilities

Angular offers a suite of utilities tailored for testing Angular applications. These utilities include ComponentFixture for programmatically interacting with Angular components, TestBed for configuring testing modules, and async and fakeAsync utilities for testing asynchronous code. Leveraging these utilities empowers developers to craft concise and expressive tests that accurately reflect their application's behavior.

ngMock

While AngularJS is an older version of Angular, ngMock, a module provided by AngularJS, remains relevant for testing AngularJS applications. ngMock enables developers to mock dependencies like HTTP requests and services, enhancing the efficiency and reliability of unit testing in AngularJS applications.

Unit testing is an integral process of software where even the smallest part of code is assessed to deliver valuable information in the early stages of development. It is a very important part of software development because it can affect the whole process if it’s not done properly. Failures in this phase can create breakable code units and lines which can cause the crashing of the application in the early stages of development. The main purpose of this is the performance validation of each unit of code to meet expected values, which are imperative for establishing a stable and secure frame for the further development process. There are 2 types of tests: manual and automated.

Manual testing is still in use, but it requires plenty of documentation and is time-consuming in contrast to automated testing. Automated testing has recently grown in popularity because of how easy it is to use. Especially now that AI is incorporating this sector and allows the unit tests to be done quickly while being highly effective and bearing reliable results. The best example of this kind of tool is Codium • Code Fast. Break fewer things.. CodiumAI makes suggestions for tests as you write your code, docstring, and comments and analyze them. You just need to include them in your package. It simply means that while you are writing the code, the tool marks the suspicious parts and makes suggestions on how to improve it so you can confidently build your project. It can be a valuable add-on to your coding routine. If you ever tried to write stable code in the first few attempts, you know how nerve-wracking and time-consuming it can be. With this automated tool, it becomes a thing of the past and you can confidently shift your focus on more complex parts of development.

In earlier versions of Eclipse, you had to install a m2eclipse plugin. This allowed you to open a maven project from eclipse natively. Eclipse would parse the pom.xml and generate project files with the correct imports and everything. The plugin will take care of it. Also, once you had m2eclipse, you could update the pom.xml from within Eclipse, and Eclipse would refresh the project configuration from the pom.xml. Basically, once you have m2eclipse, the pom.xml becomes the build configuration and you never change the eclipse project configuration

The later version of Eclipse comes bundled with m2eclipse. So, you should have maven support out of the box.

Either way, you should be able to open your maven project from within Eclipse, and wouldn't have to configure an Eclipse project yourself. Of course, there is nothing stopping you from creating an Eclipse project and importing all the sources, libraries etc. DOing this makes your work harder. You'll have to make sure your pom.xml and eclipse project are in sync.

Answering: What are the advantages of Unit Testing vs Functional Testing?

Let’s talk about cooking first. Suppose, you wanna make an omelette.

(Image source: IHOP)

A breakfast, nothing fancy. You need some ingredients though, like

• Eggs
• Milk
• Bacon
• Cheese
• Tomatoes
• Salt

And here are some things you [most likely] would inspect, evaluate, verify and so on.

• How many eggs? Are they fresh?
• What kind of milk? How much? Is it spoiled?
• Is your bowl (for scrambling) clean?
• Is your frying pan heated enough? When you put bacon in?
• And so on..

Whenever you check, inspect, evaluate, taste,.. - you’re testing. You’re making observations and making judgments while being in the process of creating the product. You’re testing your work and components. You’re doing a white box testing in this sense.

But note that you’re not testing the product.

In theory, one may try to cook giving no attention whatsoever to the process and to the components, and taste the final product - the omelette - afterwards, upon completion.

But there are implications:

• It certainly takes longer time till the evaluation results;
• It’ll be too late to take corrective actions;
• Neither you’ll be able to respond to change;
• Certain problems (like spoiled milk or dirty bowl) will not manifest themselves immediately upon tasting but may have serious health consequences.

All of that translates well into software development. If you wanna be effective and efficient then unit testing is necessary.

By all means, even excellent unit testing can’t mitigate absent or poor functional [black box] testing. The latter is out-of-the-box approach. More here: What is black box testing?

Black box testing may help to avoid failures by raising questions like these.

Are we cooking the right order? Maybe the customer wanted these but miscommunication happened.

(Image source: IHOP)

Are we using the right recipe? (List of egg dishes - Wikipedia)

Are there any threats to the value and success? Including those not explicitly stated. You know, some people don’t eat bacon, others are allergic to milk.

Are there any threats to the process?

And so on.

Thank you for reading!

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• Don’t agree or didn’t like it? Shoot me a comment! I’m sure there’s a room for improvement.

Off the top of my head, here are some distinctions:

• How many code paths are executed? A unit test will typically execute very few code paths, functional and integration testing many.
• If a test fails, how many things could be broken? For a unit test, the ideal answer is 1. Functional and integration tests the answer could be large.
• How long do they take to execute? Unit tests execute quickly, functional and integration tests relatively slowly (in general).
• Whose perspective is used to read the test? Unit tests are read by programmers. Functional tests are read by domain experts. Integration tests are read by architects (those with a broad understanding of the design of the system).

That said, here some distinctions I don't make:

• Who writes them? I write tests at all these levels. Where I work (Facebook), programmer responsibility is very broad, so mastering all styles of testing is essential.
• When are they written? I move fluidly between these different styles of tests--a functional test to make sure that I understand the domain, a few unit tests to capture subtle variations, an integration test to make sure I'm calling an external service correctly, a few more unit tests, ...
• When are they run? Ideally, they are all run at the same time, and are fast and stable enough to give nearly-instant feedback.

Here are some key advantages and disadvantages of using a unit test framework over writing your own unit tests:

Advantages of using a framework:

- Ease of use - Frameworks provide utilities like assertion methods, test discovery, reports to simplify writing and running tests.

- Standardization - Tests follow common patterns and conventions, making them easy for others to understand and maintain.

- Support for best practices - Features promote practices like test isolation, independence, repeatability.

- Debugging tools - Frameworks have debuggers, profiling to pinpoint test failures.

- Continuous integration - Tests can be easily integrated into CI/CD pipelines using framework plugins.

Disadvantages of using a framework:

- Learning curve - Need to learn the framework's API and features. Overhead of additional dependency.

- Rigidity - Framework structures may not fit some unconventional test cases/projects.

- Control loss - Less flexibility to optimize tests as needed compared to custom solution.

- Vendor lock-in - Relying on a specific framework vendor over time.

So in summary, frameworks make testing easier and standardized at cost of some flexibility. For small projects or prototypes, rolling your own may suffice but frameworks are generally recommended for serious test automation.

I hope this helps

Automated functional tests are only expensive to maintain if they have been designed incorrectly. e.g. they go through the GUI.

A poor design is alway what makes them slow. e.g. they go through the database.

And when you don't trust failures it is because you have coupled the test to things that can randomly fail, like the network.

Functional tests that are properly designed do not exhibit these failures.

This does not mean that we leave parts of the system untested. It is possible to test the GUI, without the rest of the system in place. This makes the GUI tests isolated, fast, and reliable. It is possible to test the database without going through the whole system. This allows you to use test data that is fast, simple, and reliable.

And, in the end, you should still do _some_ functional tests all the way through, from the GUI to the Database (tracer bullets). But the number of such tests must be small enough to allow the tests to be reliable and fast. Thus, you must carefully choose those tracer bullet tests.

Units tests don’t catch other tests. Unit tests catch regressions or bugs. A regression means that something that was expected to work a certain way no longer works this way. A bug, more generally, means that something doesn’t work.

The difference between unit tests and integration tests is that unit tests are very small in scope and cover things happening within one part of a system. Integration tests are larger in scope, and cover scenarios where at least two independent components talk to each other. The internal logic of these components can be covered with other tests such as unit tests, but with integration tests, we can also check how they interact with each other.

A unit testing framework typically tests itself through self-hosted tests. This means the framework contains its own test suite to validate core functionality. These tests check if key components, like assertion methods and reporting, behave correctly.

The process involves running sample test cases within the framework to ensure the expected outcomes. For example, if an assertion fails, the framework must correctly identify it as a failure. Additionally, frameworks rely on mock objects or meta-testing techniques to ensure reliability. Continuous integration helps detect issues early by running these self-tests with every code update.

I’m kind of biased in that I’m writing a book on Spock

That said I do really like Spock

I also really like Jasmine

Footnotes

There isn’t really a one-to-one relationship between classes/methods and unit tests/assertions. But in general, you should try to do something like this:

• Each unit test covers one case of one method, using a single set of inputs. It uses a single assertion.
• It is acceptable to include multiple assertions in one test case, assuming they are logically intertwined. It would be wasteful to set up a second test just to separate the assertions.

So just to make up an example, let’s say you’re writing a sort method that takes an array of ints and returns the same array in ascending order. You would want to have a single test for each of these conditions:

• emptyArrayReturnsEmtpyArray() // [] => []
• singleItemIsNotChanged() // [1] => [1]
• twoItemsSorted() // [1, 2] => [1, 2]
• twoItemsUnsorted() // [2, 1] => [1, 2]

Depending on the language and your error handling strategy, you might also need to have tests that check the behavior for incorrect input types. Of course you also need more complex examples than an array of two elements.

The second test is an important case because your sort algorithm might forget to check for the presence of at least two elements, which could result in an accidental out-of-bounds error.

Also notice how the third test is not redundant. If your code blindly reversed every array, the third test is the only one that would fail.

That is the level of focused detail that you should have in every test you write.

I used to think it was good practice to have one assertion per test. That is..until I learned TDD from the pros and you most certainly should not stick to the idea that you must only have one assert per test.

I remembered the first time I paired with 8th light….they had more than one assert per test most the time and I asked isn’t that bad? Little did I know that it totally isn’t bad as long ass those set of assertions test the scope of the behavior I am testing. When I say scope it’s an experience thing. You could also most certainly have too many asserts in a test where you just aren’t experienced enough to understand when you have added too many or when you have have added non-value added asserts or redundant asserts too. What you don’t want however is a single test testing more than one behavior..so if you see a ridiculous number of asserts in a test whoever made that test doesn’t quite understand unit testing yet and they are creating bad tests that are fragile.

How do you know if you are testing more than one behavior in your tests which would be bad? Well, that’s why BDD/TDD is so valuable. You are creating code and tests at small lean increments along the way guided by whatever story you are working on. If you are focused on baby steps which is what TDD expects, your chances of over testing or testing more than one behavior in a test….that mistake is drastically reduced just due to the nature of using BDD with lower level TDD testing. That’s year another reason why adding tests after is not the best approach to testing software. TDD forces you to think about design, BDD forces you to think about behavior. When both are used together it guides you so that you end up with not only leaner tests but leaner design based on behavior. So test drive your apps, don’t be that cider who codes prod code first always then comes back around wondering what and how he/she should test after the fact. It’s just not the same experience or outcome when you din’t test drive your code.

It just takes practice to become familiar with the types of sets of assertions that are needed for the context of a certain behavior.

When you test a “unit” of functionality that might require 1–3 assertions depending on what unit of behavior you are testing. To say that a certain unit (behavior) works sometimes multiple assertions are required to test that behavior.

Unit testing isolates your code from external/slow/non-deterministic components, so for instance you would abstract away webservers, databases, file access, 3rd-party libraries, and even potentially things like multithreading, RNG and time.

Integration testing, on the other hand, tests your code with these “pointy bits" in place.

As a result unit tests should run with 100% passing, they should be fast, and small, but they only test if your code works as it should.

Integration tests, on the other hand, may sometimes fail for reasons outside your control(disk is full, network is down, credentials expired), they're slower (you might need to start a web server or connect to a database) and they need more code to set up your testing environment, but they test if all of the pieces are working together correctly to have the desired result.

TL;DR: Sort of. Part of TDD is usually unit testing.

TDD stands for Test Driven Development (though some say it should stand for Test Driven Design). The idea is that you should:

1. write your tests before your code,
2. verify that the tests fail (because the code that would make it pass doesn’t exist yet; this is usually called the “red” stage),
3. write the code that makes the tests pass (getting you to the “green” stage),
4. and let your experience of doing all that, affect your design of the code.

That last part is why it’s test-driven, as opposed to simply test-first. For instance, if you’re finding it difficult to get at certain necessary internal results, your tests are “telling you” that you need to refactor the code so as to make those results more easily available. This results in more modular, loosely coupled code.

TDD is usually applied to unit tests, as opposed to integration, feature, end-to-end, smoke, sanity, etc. tests. However, there is also something very similar, called BDD, standing for Behavior Driven Development, that is essentially the same but usually applied at the feature level and dealing with what’s shown on the screen (e.g., the browser content of a web app), rather than the inputs and outputs (or side effects) of a function (or method). And one could certainly do something very similar for the other levels, such as integration tests, especially when integrating with something you’re in the process of building.

PS: Some people have described TDD as being something like “making sure that your most critical code has decent test coverage within a reasonable amount of time, for definitions of ‘critical’, ‘decent’, and ‘reasonable’ appropriate for your situation”. While that’s certainly a worthy goal, especially with inherited legacy code (and particularly using Michael Feathers’ definition of “legacy” as “code without tests”), it’s not necessarily TDD. If the code already exists, you’re obviously not designing/developing it.

Unit testing - testing of a unit of software in isolation from the system utilizing it. Typically unit tests are written by the developers developing the unit and are run by an automated build system. Unit testing is almost always automated.

System testing - the testing of the system. Such tests may or may not be automated, and may or may not be developed and run by a dedicated QA team. It may even be the case that some of the system testing is automated and others aren’t. There’s a lot of variability in system testing.

Acceptance testing - this is typically done by a user advocate representing the stakeholders paying for the development of the software. They typically have no knowledge of the architecture and design of the system and are looking at the software through the user’s perspective. Does the software meet the user’s needs? Can users be reasonably expected to successfully use the software for its intended purpose? Agile software development methodologies employ acceptance testing at the end of every development cycle so the development team gets quick feedback should they be slowly going off course. In my experience acceptance testing is rarely automated.

Unit testing - ideally black box testing which tests individual functions and classes each executable ‘thing’ is effectively a unit. Testing normally includes passing bad data to each unit as well as good data across a wide range of valid data. testing is normally organised into repeatable scripts per unit, so that the testing can be easily repeated each time the unit is changed. testing is against the requirements for that unit.

System testing - testing of the entire system (normally consisting of many different units, and maybe even many different processes) - testing is normally top to bottom (good and bad data is passed into the top of the system at the user or external interfaces and data is logged or retrieved from the bottom of the system - a test database or test system emulators). The system is tested using testing interfaces only - not live or ‘production’ systems Testing is against the system test requirements.

Acceptance testing (I prefer the term user acceptance testing since it makes the intention obvious). Here a select few users (Gold users) do normal transactions through the system. Testing is against Acceptance tests specifications written by the Gold Users. Where possible acceptance testing is carried out using realistic data (maybe a live system backup). Whether the acceptance testing uses live destination systems is dependent on risk assements etc - I have seen both done.

A unit test framework is just another piece of code. If the framework can test units in the language the framework is written it, you just create tests for the units the framework consists of.

One small hiccup can in some environments (such as traditional compiled C, C++, etc.) be that you have two versions of the same unit, the one under test and the one in the framework. But there are many ways to solve this.

E.g., as (currently) the primary committer for cgreen-devs/cgreen I often macro-rename the function(s) which gets duplicated using macro magic like:

#ifdef UNITTESTING 
#define some_function(x) unittesting_some_function(x) 
#endif 

The Makefile for building the unittests will set UNITTESTING and the above is included in the unit and the unittests. Then I can compile and link the unit with some_function() in it to the Cgreen framework even though some_function() is already a part of the framework. (Because the version that is the unit being tested is now actually called unittesting_some_function(x) and that's the name the linker, and the unittests see.

With unit tests you want to think about the code (functions, classes, methods, whatever) and some type of Code coverage . Most often the unit is not responsible for what other units do.

With integration tests someone does/implements what they want to do for integration scenarios they imagine on some composition of different interconnected units. Integration tests may actually not care about code structure at all.

Different logic works for two: something like “fuel controller testing” (unit) and “car test drive” (integration).

P.S. A real life story-1: programmer volunteered “to cover new functionality with unit tests” and wanted it his way. “Okay”, I said, “If after your unit tests I find no bugs, we will leave it your way, but if I find bugs, I’m going to ask for my separate integration tests”. Two serious bugs were found in testing session, one of them was found worth of whole new development story, then we had a team meeting, and decided that integration tests should always come separately.

Story-2: I was not careful enough to find 2 issues in other functionality (application behavior looked correct, but output data were not), but programmer found them later developing his unit tests. Unit tests may be very helpful too :)

In general: the term “integration” suggests that you are testing the software as an “integrated” thing—i.e. in conjunction with everything else in the system, on a platform much like your production platform, and interacting with external things that include services, databases, and file systems.

The term “unit” suggests that you are testing a small, isolated piece of logic (a “unit”) that does not necessarily depend on such integration with a production-like system.

There are nuances to these definitions and differences in goals. I prefer to test the vast majority of the system logic with fast-feedback unit tests; they are generally the easiest to maintain if done well.

Integration tests exist to provide useful information about what the system does when it hits the real world (or some pieces of the real world). They are slower by definition and harder to maintain.

*Disclaimer* I work at Typemock the Unit Test company

Since you already got well explained answers, I’ll examine integration test vs unit tests with a funny gif:

Unit testing is the first level in Software Testing Life Cycle (STLC). In this individual component of the software are tested. It is done to validate that there are no defects in the modules/components of the software and they perform as expected.

Testing is done by the developers in the early stage of the Software Development Lifecycle to remove any defects in the initial phase itself. In this, each unit of the system is isolated to identify, analyse, and fix the defect.

Unit Testing can be implemented by the following steps:-

1. Follow the AAA pattern

This AAA pattern suggests that the test should be divided into three parts namely

Arrange-Assert-Act to enable readability of code.

· Arrange – Prerequisites of test and arrangement of code is made in this step.

· Assert- Actual test is performed

· Act- Outcomes are compared with the requirements. Results are verified

2. Always verify one use-case per unit test

It is always advisable to check one use case per test. This means using a minimum of assertions per test. This makes the test easy and more understandable.

3. Give a proper name to your test

Giving a good test name makes it easy to maintain. Test name should be self-explanatory and should provide information about what is being tested, conditions under which it is being tested and expected results

4. Always isolate the unit test codes

Testing should be done in an isolated condition and should have no dependency on external factors or environments. Isolation helps in easy identification of defects and easy fixing of bugs.

5.Unit test should be automated

Automating the unit test helps in better planning and action. Running test daily, every hour helps in building stable software and gives reports code coverage, performance, frequency of test, etc.

They look like this in Java

@Test 
public void returnsSumOfTwoNumbers(){ 
  assertThat( addTwoNumbers( 3, 5 ), is( 8 ));  
}  

This tests a function called addTwoNumbers that takes two numbers. It supplies 3 and 5 as example numbers, then checks that the returned value is 8.

If it isn’t 8, the test runner would give you a red bar, and report all failed tests.

They are very useful to ensure your code does what you expect it to do, to ensure that future changes don’t accidentally break things, and to help you think about the design of public interface of your code.

The last point is critical to TDD. You’ll probably assume that addTwoNumbers() is implemented using the addition operator. But it could just as well look up the answer in a database, or a lookup table, or call special hardware. The test would still pass, and be unchanged, as it only tests the public interface of our function.

Unit tests also are coded to avoid relying on environmental factors. So they do not rely on a database being in a particular state, nor do they scrape web output. There are tests that do this, but generally get referred to as integration tests or system tests.

Kent Beck's answer is pretty solid, as one might expect it to be. ;) I'd like to step back a bit from criteria and go to definitions -- another way of looking at the same problem.

First of all, I'd say that people apply all of these terms relatively loosely, and it's important not to get too hung up on one specific definition if you're trying to communicate clearly. That said, I think there are some parts of the definition that most people will agree with on a general level.

For me, Unit Test and Integration Test are roughly the two ends of a long spectrum that talks about how much code is being tested at once, whereas Functional Test is a different sort of categorization.

Unit Test
This implies that a very small relatively indivisible thing is being tested, like a class or a method that doesn't need many other objects to complete it's job. Again, I wouldn't worry too much about what exactly unit is -- if a function needs another function to complete, or a class needs one other class, I wouldn't say this means it isn't a Unit test, but if it tests a large number of methods and interacting classes at once, I'd have a hard time calling it a unit test.

Integration Test
This is roughly the other end of the spectrum -- typically an integration test is going to involve several 'units' of code, several classes and methods working in concert. In particular, I tend to think of multiple subsystems working together, like domain/service/persistence or multiple code systems separated by messaging, and the like. Again, I wouldn't get too hung up on the specifics -- I'm ok with calling a test an integration test if it uses several loosely coupled classes in a single subsystem.

Functional Test
A functional test implies to me that it is a test that concerns itself with an end-user visible feature and how that works in a way that makes sense at a business/domain level. This is a test specified in the terms of the domain, rather than one that concerns itself with how the feature is implemented. It tries not to care what code is being executed and should ideally not fail when you change the way the feature is implemented without changing what the feature does.

Typically, a functional test will also be an integration test, because an end-user feature usually requires more than one 'unit' of code to accomplish its goal.