Even though the main objective of test-driven development is the approach to code design, tests are still a very important aspect of TDD and we should have a clear understanding of two major groups of techniques as follows:

In order for tests to run fast and provide constant feedback, code needs to be organized in such a way that the methods, functions, and classes can be easily replaced with mocks and stubs. A common word for this type of replacements of the actual code is test double. Speed of the execution can be severely affected with external dependencies; for example, our code might need to communicate with the database. By mocking external dependencies, we are able to increase that speed drastically. Whole unit tests suite execution should be measured in minutes, if not seconds. Designing the code in a way that it can be easily mocked and stubbed, forces us to better structure that code by applying separation of concerns.

More important than speed is the benefit of removal of external factors. Setting up databases, web servers, external APIs, and other dependencies that our code might need, is both time consuming and unreliable. In many cases, those dependencies might not even be available. For example, we might need to create a code that communicates with a database and have someone else create a schema. Without mocks, we would need to wait until that schema is set.

Another very useful aspect of TDD (and well-structured tests in general) is documentation. In most cases, it is much easier to find out what the code does by looking at tests than the implementation itself. What is the purpose of some methods? Look at the tests associated with it. What is the desired functionality of some part of the application UI? Look at the tests associated with it. Documentation written in the form of tests is one of the pillars of TDD and deserves further explanation.

The main problem with (traditional) software documentation is that it is not up to date most of the time. As soon as some part of the code changes, the documentation stops reflecting the actual situation. This statement applies to almost any type of documentation, with requirements and test cases being the most affected.

The necessity to document code is often a sign that the code itself is not well written.Moreover, no matter how hard we try, documentation inevitably gets outdated.

Developers shouldn't rely on system documentation because it is almost never up to date. Besides, no documentation can provide as detailed and up-to-date description of the code as the code itself.

Using code as documentation, does not exclude other types of documents. The key is to avoid duplication. If details of the system can be obtained by reading the code, other types of documentation can provide quick guidelines and a high-level overview. Non-code documentation should answer questions such as what the general purpose of the system is and what technologies are used by the system. In many cases, a simple README is enough to provide the quick start that developers need. Sections such as project description, environment setup, installation, and build and packaging instructions are very helpful for newcomers. From there on, code is the bible.

Implementation code provides all needed details while test code acts as the description of the intent behind the production code.

Assuming that some form of Continuous Integration (CI) is in use, if some part of test-documentation is incorrect, it will fail and be fixed soon afterwards. CI solves the problem of incorrect test-documentation, but it does not ensure that all functionality is documented. For this reason (among many others), test-documentation should be created in the TDD fashion. If all functionality is defined as tests before the implementation code is written and execution of all tests is successful, then tests act as a complete and up-to-date information that can be used by developers.

What should we do with the rest of the team? Testers, customers, managers, and other non coders might not be able to obtain the necessary information from the production and test code.

As we saw earlier, two most common types of testing are black-box and white-box testing. This division is important since it also divides testers into those who do know how to write or at least read code (white-box testing) and those who don't (black-box testing). In some cases, testers can do both types. However, more often than not, they do not know how to code so the documentation that is usable for developers is not usable for them. If documentation needs to be decoupled from the code, unit tests are not a good match. That is one of the reasons why BDD came in to being.

Customers need to be able to define new functionality of the system, as well as to be able to get information about all the important aspects of the current system. That documentation should not be too technical (code is not an option), but it still must be always up to date. BDD narratives and scenarios are one of the best ways to provide this type of documentation. Ability to act as acceptance criteria (written before the code), be executed frequently (preferably on every commit), and be written in natural language makes BDD stories not only always up to date, but usable by those who do not want to inspect the code.

Documentation is an integral part of the software. As with any other part of the code, it needs to be tested often so that we're sure that it is accurate and up to date.

TDD as a methodology is a good way to move towards this direction. On a low level, unit tests are a best fit. On the other hand, BDD provides a good way to work on a functional level while maintaining understanding accomplished using natural language.

We (authors of this book) almost never debug applications we're working on!

This statement might sound pompous, but it's true. We almost never debug because there is rarely a reason to debug an application. When tests are written before the code and the code coverage is high, we can have high confidence that the application works as expected. This does not mean that applications written using TDD do not have bugs—they do. All applications do. However, when that happens, it is easy to isolate them by simply looking for the code that is not covered with tests.

Tests themselves might not include some cases. In that situation, the action is to write additional tests.

In this chapter, you got the general understanding of test-driven development practice and insights into what TDD is and what it isn't. You learned that it is a way to design the code through short and repeatable cycle called red-green-refactor. Failure is an expected state that should not only be embraced, but enforced throughout the TDD process. This cycle is so short that we move from one phase to another with great speed.

While code design is the main objective, tests created throughout the TDD process are a valuable asset that should be utilized and severely impact on our view of traditional testing practices. We went through the most common of those practices such as white-box and black-box testing, tried to put them into the TDD perspective, and showed benefits that they can bring to each other.

You discovered that mocks are a very important tool that is often a must when writing tests. Finally, we discussed how tests can and should be utilized as executable documentation and how TDD can make debugging much less necessary.

Now that we are armed with theoretical knowledge, it is time to set up the development environment and get an overview and comparison of different testing frameworks and tools.