How can you ensure effective unit tests?

In addition to writing testable code, it's essential to incorporate proper documentation. Documenting your code helps both yourself and other developers understand its purpose, functionality, and expected behavior. Include comments, inline documentation, and README files to provide insights into the logic behind your code, its dependencies, and any potential edge cases. Moreover, maintain consistency in your documentation style and keep it up to date as your code evolves. This practice not only facilitates effective unit testing but also enhances collaboration, code review processes, and overall project maintainability.

Completely agree with the statement. But, to add a bit to it, we must also understand the purpose i.e. what kind of skills we are going to measure and accordingly test cases as a milestone to be introduced so that the level of expertise can be quantified.

Here is a simple example: There is a function calculate_rectangle_area() that takes 2 params, length and width, and calculates the area of the rectangle. The function includes a check for valid input values (both length and width must be positive). The unit tests cover scenarios with valid values, invalid values, and zero values, ensuring the function behaves as expected. To run the tests, you would execute the test_geometry.py file. If all tests pass, it indicates that the calculate_rectangle_area function is working correctly for the specified cases. This example demonstrates a simple and testable piece of code where the functionality is well-contained, and the tests cover various cases to ensure the correctness of the implementation.

To ensure effective unit tests: isolate tests, write clear cases, use mocking, ensure fast execution, integrate with CI, maintain regularly, aim for high coverage, keep tests readable, refactor redundancies, and maintain a quick feedback loop.

It's essential to prioritize encapsulation and dependency injection in your code to enhance testability. Encapsulation helps isolate the internal implementation details of each unit, allowing you to test its behavior independently without needing to know about the inner workings of other components. Dependency injection enables you to inject mock objects or stubs into your code during testing, simulating external dependencies and controlling their behavior. By decoupling components and managing dependencies effectively, you can create code that is more modular, flexible, and easier to test, ultimately improving the reliability and maintainability of your software system.

Writing effective tests is not always about test coverage , We should make sure that the test is really doing what it’s meant for .. If we change a single line in the code, it should result in at least 1 test failure..!! How to ensure about this - 1. Make sure stubs are done for specific required inputs instead of stubbing for any input 2. Make sure proper assertions are written with specific input parameters instead of any input 3. Make sure assertion is also done on method calls for 0 times execution which are not supposed to be executed as part of that test 4. Make sure about stub creations memory consumption, If possible use the same object for multiple tests if required. To be precise - Be as specific as possible

Something that I recently learnt which has improved the quality of my tests is performing mutation testing on my code. There are libraries available to help out with that, one of them being pitest. Once I started performing mutation testing on my code I realised how many things my tests were missing even with 100% coverage.

One thing I see constantly to ensure successfull tests is to make test-driven development, this means to create the tests before you start developing. That way you've already automatized your tests and deliver time to think about everything that can can (possibly) go wrong.

I would also mention that it must be tested edge cases and not only the happy path. What would happen if that line throws an exception? Is there any logic to encapsulate that error? Another good approach is mutation test. Test containers are another tools that really helps simulate real scenarios. For example, it's much better to use a real database and real Redis instance. H2 database and Redis mem cache can trick you.

AAA pattern for unit test can help you in writing clean and manageable code.AAA stands for arrange,act and assert. In Arrange we setup the necessary precondition and mocks to run the unit test. In Act we exexute the unit under test with necessary input and capture the output . In Assert step we check whether the output is of expeced outcome. With this pattern it makes the code clear consise and readable.

Test cases must be isolated, independent, and properly ordered. Test suites should be maintained, adapting to code changes. Clear documentation and consistent naming conventions enhance test readability. Regular test execution within the development cycle ensures continuous validation. Developers should embrace the "arrange, act, assert" pattern and utilize testing frameworks effectively.

Der Name eines Komponententests sollte klar beschreiben, was der Test tut und welche Erwartungen er hat. Ein gut gew?hlter Testname hilft dabei, den Zweck und den Umfang des Tests zu identifizieren und erleichtert das Debuggen und die Wartung. Es ist ratsam, eine Namenskonvention zu verwenden, die dem Format [Prüfling]_[Szenario]_[Erwartetes Ergebnis] folgt. Beispiel: Ein Test mit dem Namen calculator_addTwoPositiveNumbers_returnsSum gibt an, dass es sich um einen Test für die Rechnereinheit handelt, bei dem das Szenario das Hinzufügen von zwei positiven Zahlen ist und das erwartete Ergebnis die Summe dieser Zahlen ist.

1. Test Doubles: Staging material for tests. 2. Justification for Use: Manage test inputs and outputs. 3. Types: Stubs (fixed values), mocks (interactions), fakes (lightweight), spies (tracking). 4. Best Option: Select according to exam requirements. Don’t Overdo: Keep tests simple and steer away of complexity. 6. Tools: Python (unittest.mock), Java (Mockito), C# (Moq), and JavaScript (Sinon). 7. Vital Guidance: - Maintain test duplicate concentration. Make them easy to understand. - Update as necessary.

Test cases are not meant to be written just for the sake of coverage, test cases should be meaningful and for the same , review is utmost necessary. Correct test cases test the business logic behind the implementation rather than just testing the lines of code

Too often, code reviewers are guilty of the “LGTM” method of reviewing unit tests. Unit tests must be thoroughly reviewed before approved, just like functional code. During review, check for clarity, redundancy, and coverage. Refactor tests to improve readability and maintainability, using techniques like extracting methods or renaming variables for better understanding. Ensure refactored tests maintain the same coverage level.

Understanding what is a UNIT? and what it isn't? is what makes a unit test effective. A Unit is not a class, method, function etc. Its the BEHAVIOR. Even If you write the most cleanest unit test and the unit is not a behavior then I would argue the test is pointless.