Unit test vs. Programmer test vs. Integration test

Almost anyone agrees about what is a Unit Test. QA engineers and developers usually give Integration Test different meanings. Extreme Programming defines the Programmer Test concept, whose objective is to create more useful tests. This article outlines the properties of each type of test and creates a common ground to speak about them.

Photo by Mike from Pexels

Unit test

You get the headlamp, place a sensor as a lightbulb, and connect the headlamp to a power source. The test passes if the sensor receives power.

Unit test: the lamp.

• Runs fast; the scale is milliseconds.
• Test a unit in isolation; one failure implies one single unit.
• Mock internal dependencies; replace other components by mocks.
• Mock externals; avoid databases, network, and external APIs.
• Failure spots precisely the origin of failure; it is one unit.
• Refactor is difficult; changes in code usually implies changes in the test.
• No business value; the test does not explain the objective of the unit.
• Low confidence; do not test the relation between components.
• Discourage the use of TDD; test and units are too close.

The test is: does the headlamp powers the lightbulb outlet when it receives power? Complete the Use Case with additional tests for the lightbulb, the switch, the battery, and the wire.

Unit tests have no business value. Probably you are a car factory company, not a headlamp factory company. If you read all the tests, you know which parts you need to build a car, but no idea how to make a car. Changes are hard; if you change any piece, you have to rebuild your tests for that piece, and you do not know how it affects other parts.

Confidence is low. You are not checking if the headlamp socket is compatible with the lightbulb, or if the headlamp glass is black, and it does not allow light to pass; just to name a few.

Programmer test

You get the headlamp, a light bulb, a battery, a light switch, interconnect them. Turn the switch on. The test passes if the light bulb emits light.

Programmer test: the lamp lights.

• Runs fast; the scale is milliseconds.
• Test a business rule in isolation; do not build the whole application.
• Use internal components; avoid mocks, and use actual parts.
• Mock externals; avoid databases, network, and external APIs.
• Failure spots reasonably well the origin of failure; a few units involved.
• Refactor is easy; the test is resilient to changes in code.
• Documents the code; connects test and business rules.
• High confidence; it tests a business rule with relevant components.
• Use TDD; the most recent edit is vital.

The test is: does turning the switch on lighting the lamp?

Programmer tests have business value. You know what the headlamp does and how it works. Each test becomes a small manual about how to build a car part. You can switch lightbulbs, headlamps, and other pieces, and the test still passes if it works.

Confidence is high. You are not checking each part and also that everything is working well together.

Integration test

You build a car and place it in a dark street. Unlock the door, turn the key, and turn on the light switch. The test passes if it illumines the street correctly.

Integration test: the car lights the street.

• Faster than human; each test takes seconds or minutes.
• Test a business rule; imitate the human using the application.
• Use real internal components; mock nothing here.
• May use real external dependencies; some mocks are necessary.
• Failure gives the origin of failure vaguely; consider the whole system.
• Refactor is hard; test is slow, and failure give vague hints.
• High business value; document the exact behavior of the system.
• Highest confidence; the next step is real users.
• Not suitable for TDD; test-code-refactor cycle is too long.

The test is: does turning the switch on making the car light the road?

These tests are real business value. You know what the car must do. You can change any part and still check if everything works. The problem is that if something fails, you have to debug the full car.

Confidence is the highest. You have a real car. It is also slow because you need to build a new car for each test.

#testing #integration-testing #unit-testing

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Software Testing 101: Regression Tests, Unit Tests, Integration Tests

Automation and segregation can help you build better software
If you write automated tests and deliver them to the customer, he can make sure the software is working properly. And, at the end of the day, he paid for it.

Ok. We can segregate or separate the tests according to some criteria. For example, “white box” tests are used to measure the internal quality of the software, in addition to the expected results. They are very useful to know the percentage of lines of code executed, the cyclomatic complexity and several other software metrics. Unit tests are white box tests.

#testing #software testing #regression tests #unit tests #integration tests

Unit test vs. Programmer test vs. Integration test

Almost anyone agrees about what is a Unit Test. QA engineers and developers usually give Integration Test different meanings. Extreme Programming defines the Programmer Test concept, whose objective is to create more useful tests. This article outlines the properties of each type of test and creates a common ground to speak about them.

Photo by Mike from Pexels

Unit test

You get the headlamp, place a sensor as a lightbulb, and connect the headlamp to a power source. The test passes if the sensor receives power.

Unit test: the lamp.

• Runs fast; the scale is milliseconds.
• Test a unit in isolation; one failure implies one single unit.
• Mock internal dependencies; replace other components by mocks.
• Mock externals; avoid databases, network, and external APIs.
• Failure spots precisely the origin of failure; it is one unit.
• Refactor is difficult; changes in code usually implies changes in the test.
• No business value; the test does not explain the objective of the unit.
• Low confidence; do not test the relation between components.
• Discourage the use of TDD; test and units are too close.

The test is: does the headlamp powers the lightbulb outlet when it receives power? Complete the Use Case with additional tests for the lightbulb, the switch, the battery, and the wire.

Unit tests have no business value. Probably you are a car factory company, not a headlamp factory company. If you read all the tests, you know which parts you need to build a car, but no idea how to make a car. Changes are hard; if you change any piece, you have to rebuild your tests for that piece, and you do not know how it affects other parts.

Confidence is low. You are not checking if the headlamp socket is compatible with the lightbulb, or if the headlamp glass is black, and it does not allow light to pass; just to name a few.

Programmer test

You get the headlamp, a light bulb, a battery, a light switch, interconnect them. Turn the switch on. The test passes if the light bulb emits light.

Programmer test: the lamp lights.

• Runs fast; the scale is milliseconds.
• Test a business rule in isolation; do not build the whole application.
• Use internal components; avoid mocks, and use actual parts.
• Mock externals; avoid databases, network, and external APIs.
• Failure spots reasonably well the origin of failure; a few units involved.
• Refactor is easy; the test is resilient to changes in code.
• Documents the code; connects test and business rules.
• High confidence; it tests a business rule with relevant components.
• Use TDD; the most recent edit is vital.

The test is: does turning the switch on lighting the lamp?

Programmer tests have business value. You know what the headlamp does and how it works. Each test becomes a small manual about how to build a car part. You can switch lightbulbs, headlamps, and other pieces, and the test still passes if it works.

Confidence is high. You are not checking each part and also that everything is working well together.

Integration test

You build a car and place it in a dark street. Unlock the door, turn the key, and turn on the light switch. The test passes if it illumines the street correctly.

Integration test: the car lights the street.

• Faster than human; each test takes seconds or minutes.
• Test a business rule; imitate the human using the application.
• Use real internal components; mock nothing here.
• May use real external dependencies; some mocks are necessary.
• Failure gives the origin of failure vaguely; consider the whole system.
• Refactor is hard; test is slow, and failure give vague hints.
• High business value; document the exact behavior of the system.
• Highest confidence; the next step is real users.
• Not suitable for TDD; test-code-refactor cycle is too long.

The test is: does turning the switch on making the car light the road?

These tests are real business value. You know what the car must do. You can change any part and still check if everything works. The problem is that if something fails, you have to debug the full car.

Confidence is the highest. You have a real car. It is also slow because you need to build a new car for each test.

#testing #integration-testing #unit-testing

Testing Microservices Applications

The shift towards microservices and modular applications makes testing more important and more challenging at the same time. You have to make sure that the microservices running in containers perform well and as intended, but you can no longer rely on conventional testing strategies to get the job done.

This is where new testing approaches are needed. Testing your microservices applications require the right approach, a suitable set of tools, and immense attention to details. This article will guide you through the process of testing your microservices and talk about the challenges you will have to overcome along the way. Let’s get started, shall we?

A Brave New World

Traditionally, testing a monolith application meant configuring a test environment and setting up all of the application components in a way that matched the production environment. It took time to set up the testing environment, and there were a lot of complexities around the process.

Testing also requires the application to run in full. It is not possible to test monolith apps on a per-component basis, mainly because there is usually a base code that ties everything together, and the app is designed to run as a complete app to work properly.

Microservices running in containers offer one particular advantage: universal compatibility. You don’t have to match the testing environment with the deployment architecture exactly, and you can get away with testing individual components rather than the full app in some situations.

Of course, you will have to embrace the new cloud-native approach across the pipeline. Rather than creating critical dependencies between microservices, you need to treat each one as a semi-independent module.

The only monolith or centralized portion of the application is the database, but this too is an easy challenge to overcome. As long as you have a persistent database running on your test environment, you can perform tests at any time.

Keep in mind that there are additional things to focus on when testing microservices.

• Microservices rely on network communications to talk to each other, so network reliability and requirements must be part of the testing.
• Automation and infrastructure elements are now added as codes, and you have to make sure that they also run properly when microservices are pushed through the pipeline
• While containerization is universal, you still have to pay attention to specific dependencies and create a testing strategy that allows for those dependencies to be included

Test containers are the method of choice for many developers. Unlike monolith apps, which lets you use stubs and mocks for testing, microservices need to be tested in test containers. Many CI/CD pipelines actually integrate production microservices as part of the testing process.

Contract Testing as an Approach

As mentioned before, there are many ways to test microservices effectively, but the one approach that developers now use reliably is contract testing. Loosely coupled microservices can be tested in an effective and efficient way using contract testing, mainly because this testing approach focuses on contracts; in other words, it focuses on how components or microservices communicate with each other.

Syntax and semantics construct how components communicate with each other. By defining syntax and semantics in a standardized way and testing microservices based on their ability to generate the right message formats and meet behavioral expectations, you can rest assured knowing that the microservices will behave as intended when deployed.

Ways to Test Microservices

It is easy to fall into the trap of making testing microservices complicated, but there are ways to avoid this problem. Testing microservices doesn’t have to be complicated at all when you have the right strategy in place.

There are several ways to test microservices too, including:

• Unit testing: Which allows developers to test microservices in a granular way. It doesn’t limit testing to individual microservices, but rather allows developers to take a more granular approach such as testing individual features or runtimes.
• Integration testing: Which handles the testing of microservices in an interactive way. Microservices still need to work with each other when they are deployed, and integration testing is a key process in making sure that they do.
• End-to-end testing: Which⁠—as the name suggests⁠—tests microservices as a complete app. This type of testing enables the testing of features, UI, communications, and other components that construct the app.

What’s important to note is the fact that these testing approaches allow for asynchronous testing. After all, asynchronous development is what makes developing microservices very appealing in the first place. By allowing for asynchronous testing, you can also make sure that components or microservices can be updated independently to one another.

#blog #microservices #testing #caylent #contract testing #end-to-end testing #hoverfly #integration testing #microservices #microservices architecture #pact #testing #unit testing #vagrant #vcr

Unit Testing Is NOT The Same As Integration Testing

When Integration Testing Gets Tricky

Certain truly external systems may be difficult to integrate into tests. This is because they have side effects in the real world that cannot be undone: A financial transaction, an email send, physically moving a paint robot. Before you give up and sidestep them in your testing, look around for solutions.

Many external systems will have a documented way to use them in an integration test. Payment processors often have test credit card numbers, and test users with test email accounts can be set up for testing delivery. The closer integration tests are to real-world interactions the more likely they are to catch problems and provide real value.

#testing #unit testing test #integration testing

Unit Testing Vs. Integration Testing In Android

Testing In Android: Unit Or Integration Or Both?
Integration tests don’t run as quickly as unit tests, and this could pose a predicament when it comes to delivery timelines. For instance, if we’re working on a project with a deadline, Integration testing isn’t agile and if we have to run 50-60 integration tests, it may take anywhere between 20-30 minutes for the build. Most developers prefer unit testing because of this very drawback and because bugs can be detected in the early stages.

Now the question remains — should we go for unit testing or integration testing? What if we say both?

#testing #unit testing #integration testing #android