How do you name your test cases to make them readable and consistent?

In the context of React, describing the input/output of the unit makes the most sense. For example, when writing a test for the service layer (pure functions like API adapter, view model factory): it('returns model when iban is from a trusted bank it('returns model when instant payments are accepted it('returns model when iban has security claims When writing an acceptance test (e.g., integration or E2M) for a customer journey, it's best to speak the customer's words: it('see error when iban is invalid it("see iban details when it's valid it('create scheduled international payment The idea is to make test descriptions decoupled from any technical implementation details. These kind of tests will serve as a living documentation.

I write mostly TDD in C#. My tests are usually in one of two folders - one for “unit tests” and the other for “integration tests”. I’m not strict about what’s a unit test - the main differentiator is whether the test hits a database or an external api (either makes it integration) or not. The test classes are named for the classes they test. TimeSlotFinder_Tests, for example. Given all that context, my tests don’t usually mention the class that they’re testing. Instead they are about capabilities as I am adding them. Test example names for hypothetical scheduling app: ThrowsArgumentError_WhenDesiredSlotIsLessThan15Minutes CanFindATimeSlot_InEmptySchedule Etc. in each of these I set up the minimal conditions to test the capability

To ensure readability and consistency, we follow a naming convention that includes a clear description of the functionality being tested, followed by any relevant input or conditions, and finally the expected outcome or result. This format helps testers quickly understand the purpose and scope of each test case, leading to more effective testing processes.

Test methods name should tell the method being used, scenario and expected behavior. Having a good naming convention improves the maintainability of the code, clarity etc. Different programming languages use different naming conventions like CamelCase, Snake Case, Kebab Case. Naming convention should be consistent across the codebase in relation to the programming language used. Follow a structure to keep the related tests unit, integration etc in different test classes or folders. Using java as example to unit test for `boolean createUser(User user)` with user having attributes like name, email Test methods can be like createUser_withValidInput_ReturnsTrue() createUser_withInvalidEmail_ReturnsFalse()

For Java and other object oriented languages I prefer using "Should" instead of "Test". If I'm testing the "AccountService" then I would name the test class "AccountServiceShould". That way, the methods of the test class can start with a verb to describe the intented behaviour you want to test. For example, "createADepositTransaction". By doing this, the output of your test runner will be very clear and readable since it will read like plain English: "AccountServiceShould -> createADepositTransaction". In test runners like Jest/Vitest, I would do something similar. I would start with "describe("AccountService", ...). I would then continue with "it("should create a deposit transaction", ...). This should make tests much more readable.

With capabilities like test organization, parallel execution, and comprehensive result logs, test runners are vital tools for software testing. They carry out tests and report results. Test runners that are recommended for a given programming language include pytest for Python, JUnit for Java, Mocha for JavaScript, and RSpec for Ruby. Installing, customizing to project requirements, and creating tests in the test runner's own syntax are all necessary steps in using it. For example, tests must be defined as functions with a test_prefix or _test suffix in Python, utilizing assert statements for validation, according to pytest. This simplified method guarantees effective and consistent testing throughout software development initiatives.

It doesn't matter what tools you use. In most cases, you can easily find them in Google for your language, and you can also easily select them on Github based on the number of stars and contributors

The most beneficial part for me is how TDD can bring confidence to me, although it's part of the advantage of having tests. I found I likely to do refactoring and change the code for good only if I know I won't break anything with the new changes. The small iterative way of making changes is also a great benefit. With it, you can stop at any point without leaving a broken temporary code.

You save time on modifications, switching developer contexts and meetings due to the fact that the requirements are known in advance and the code fits them

Para mí pensar que TDD es una metodología que retrasa el desarrollo es medio verdad, me explico. En un inicio si el equipo no está acostumbrado a hacer TDD es cierto que en un inicio la velocidad de desarrollo es mucho más lenta, sin embargo cuando nos vamos acostumbrando el nivel de desarrollo crece incluso más que sin aplicar TDD. Está práctica junto a otras como Pair Programming pueden elevar la velocidad de desarrollo y nos puede asegurar que la calidad del código producido sea mejor. Sin embargo no debemos tomarnos TDD como algo dogmático, se puede aplicar poco a poco o no aplicar al 100% todo los pasos, es cuestión de adaptarse al equipo y hacer lo que más beneficie a todos.

Test-Driven Development (TDD) has its challenges. Firstly, there's a learning curve, especially for those new to the approach, as writing tests before code requires a shift in mindset. Balancing comprehensive tests without over-engineering is tricky. Additionally, initial development may seem slower, impacting deadlines. Tackling legacy code with TDD can be complex and time-consuming due to writing tests for existing code. These hurdles require patience and skill to overcome, making TDD adoption a gradual process for many teams.

Modern development uses flexible methodologies. Sometimes the degree of uncertainty can go off scale and this will lead to the complexity of implementing TDD. This methodology requires clearly defined acceptance criteria. At the same time, reality can make its own adjustments, which leads to the absence of acceptance criteria or their strong variability. As a result, you may find that the tests you write are not suitable for changing requirements during development.

The main problem you will encounter is which script to write a test for and how many tests to write. Thus, if the business scenarios with your customer are not worked out, it will be difficult to write capacious tests, which is why their volume will increase. Once the scenarios are worked out, it is enough to write 1 positive and 1 negative scenario

To excel at Test-Driven Development (TDD), several tips can be beneficial. Firstly, start with simple test cases and gradually increase complexity to avoid feeling overwhelmed. Always focus on one test at a time, keeping them small and specific. Refactor code after each passing test to maintain clean and efficient code. Embrace the red-green-refactor cycle religiously, ensuring failing tests before writing production code. Strive for high test coverage but prioritize testing critical and complex areas. Lastly, leverage TDD as a design tool, letting it guide the architecture and structure of your code. These tips help harness TDD's power for improved code quality and maintainability.