Разработка через тестирование: Red-Green-Refactor цикл.

Coding technique of repetitively writing test code then production code

Test driven development (TDD) is a way of writing code that involves writing an automated unit level test case that fails, then writing just enough code to make the test pass, then refactoring both the test code and the production code, then repeating with another new test case. Alternative approaches to writing automated tests is to write all of the production code before starting on the test code or to write all of the test code before starting on the production code. With TDD, both are written together. TDD is related to the test first programming concepts of extreme programming, begun in 1999, but more recently has created more general interest in its own right. Programmers also apply the concept to improving and debugging legacy code developed with older techniques.

There are various aspects to using test driven development, for example the principles of "keep it simple, stupid" (KISS) and "You aren't gonna need it" (YAGNI). By focusing on writing only the code necessary to pass tests, designs can often be cleaner and clearer than is achieved by other methods. When writing feature first code, there is a tendency by developers and organizations to push the developer on to the next feature, even neglecting testing entirely. The first TDD test might not even compile at first, because the classes and methods it requires may not yet exist. Nevertheless, that first test functions as the beginning of an executable specification. Each test case fails initially: This ensures that the test really works and can catch an error. Once this is shown, the underlying functionality can be implemented. This has led to the "test driven development mantra", which is "red/green/refactor", where red means fail and green means pass. Test driven development constantly repeats the steps of adding test cases that fail, passing them, and refactoring. Receiving the expected test results at each stage reinforces the developer's mental model of the code, boosts confidence and increases productivity.

Test code needs access to the code it is testing, but testing should not compromise normal design goals such as information hiding, encapsulation and the separation of concerns. Therefore, unit test code is usually located in the same project or module as the code being tested. In object oriented design this still does not provide access to private data and methods. Therefore, extra work may be necessary for unit tests. In Java and other languages, a developer can use reflection to access private fields and methods. Alternatively, an inner class can be used to hold the unit tests so they have visibility of the enclosing class's members and attributes. In the NET Framework and some other programming languages, partial classes may be used to expose private methods and data for the tests to access. It is important that such testing hacks do not remain in the production code. In C and other languages, compiler directives such as #if DEBUG #endif can be placed around such additional classes and indeed all other test related code to prevent them being compiled into the released code. This means the released code is not exactly the same as what was unit tested. The regular running of fewer but more comprehensive, end to end, integration tests on the final release build can ensure (among other things) that no production code exists that subtly relies on aspects of the test harness. There is some debate among practitioners of TDD, documented in their blogs and other writings, as to whether it is wise to test private methods and data anyway. Some argue that private members are a mere implementation detail that may change, and should be allowed to do so without breaking numbers of tests. Thus it should be sufficient to test any class through its public interface or through its subclass interface, which some languages call the "protected" interface. Others say that crucial aspects of functionality may be implemented in private methods and testing them directly offers advantage of smaller and more direct unit tests.

Effective layout of a test case ensures all required actions are completed, improves the readability of the test case, and smooths the flow of execution. Consistent structure helps in building a self documenting test case. A commonly applied structure for test cases has (1) setup, (2) execution, (3) validation, and (4) cleanup. Setup: Put the Unit Under Test (UUT) or the overall test system in the state needed to run the test. Execution: Trigger/drive the UUT to perform the target behavior and capture all output, such as return values and output parameters. This step is usually very simple. Validation: Ensure the results of the test are correct. These results may include explicit outputs captured during execution or state changes in the UUT. Cleanup: Restore the UUT or the overall test system to the pre test state. This restoration permits another test to execute immediately after this one. In some cases, in order to preserve the information for possible test failure analysis, the cleanup should be starting the test just before the test's setup run. Testing implementation details. Slow running tests.

Test driven development is related to, but different from acceptance test–driven development (ATDD). TDD is primarily a developer's tool to help create well written unit of code (function, class, or module) that correctly performs a set of operations. ATDD is a communication tool between the customer, developer, and tester to ensure that the requirements are well defined. TDD requires test automation. ATDD does not, although automation helps with regression testing. Tests used in TDD can often be derived from ATDD tests, since the code units implement some portion of a requirement. ATDD tests should be readable by the customer. TDD tests do not need to be.

BDD (behavior driven development) combines practices from TDD and from ATDD. It includes the practice of writing tests first, but focuses on tests which describe behavior, rather than tests which test a unit of implementation. Tools such as JBehave, Cucumber, Mspec and Specflow provide syntaxes which allow product owners, developers and test engineers to define together the behaviors which can then be translated into automated tests.

There are many testing frameworks and tools that are useful in TDD.

Developers may use computer assisted testing frameworks, commonly collectively named xUnit (which are derived from SUnit, created in 1998), to create and automatically run the test cases. xUnit frameworks provide assertion style test validation capabilities and result reporting. These capabilities are critical for automation as they move the burden of execution validation from an independent post processing activity to one that is included in the test execution. The execution framework provided by these test frameworks allows for the automatic execution of all system test cases or various subsets along with other features.

Testing frameworks may accept unit test output in the language agnostic Test Anything Protocol created in 1987.

Exercising TDD on large, challenging systems requires a modular architecture, well defined components with published interfaces, and disciplined system layering with maximization of platform independence. These proven practices yield increased testability and facilitate the application of build and test automation.

A 2005 study found that using TDD meant writing more tests and, in turn, programmers who wrote more tests tended to be more productive. Hypotheses relating to code quality and a more direct correlation between TDD and productivity were inconclusive. Programmers using pure TDD on new ("greenfield") projects reported they only rarely felt the need to invoke a debugger. Used in conjunction with a version control system, when tests fail unexpectedly, reverting the code to the last version that passed all tests may often be more productive than debugging. Test driven development offers more than just simple validation of correctness, but can also drive the design of a program. By focusing on the test cases first, one must imagine how the functionality is used by clients (in the first case, the test cases). So, the programmer is concerned with the interface before the implementation. This benefit is complementary to design by contract as it approaches code through test cases rather than through mathematical assertions or preconceptions. Test driven development offers the ability to take small steps when required. It allows a programmer to focus on the task at hand as the first goal is to make the test pass. Exceptional cases and error handling are not considered initially, and tests to create these extraneous circumstances are implemented separately. Test driven development ensures in this way that all written code is covered by at least one test. This gives the programming team, and subsequent users, a greater level of confidence in the code. While it is true that more code is required with TDD than without TDD because of the unit test code, the total code implementation time could be shorter based on a model by Müller and Padberg. Large numbers of tests help to limit the number of defects in the code. The early and frequent nature of the testing helps to catch defects early in the development cycle, preventing them from becoming endemic and expensive problems. Eliminating defects early in the process usually avoids lengthy and tedious debugging later in the project. TDD can lead to more modularized, flexible, and extensible code. This effect often comes about because the methodology requires that the developers think of the software in terms of small units that can be written and tested independently and integrated together later. This leads to smaller, more focused classes, looser coupling, and cleaner interfaces. The use of the mock object design pattern also contributes to the overall modularization of the code because this pattern requires that the code be written so that modules can be switched easily between mock versions for unit testing and "real" versions for deployment. Because no more code is written than necessary to pass a failing test case, automated tests tend to cover every code path. For example, for a TDD developer to add an else branch to an existing if statement, the developer would first have to write a failing test case that motivates the branch. As a result, the automated tests resulting from TDD tend to be very thorough: they detect any unexpected changes in the code's behaviour. This detects problems that can arise where a change later in the development cycle unexpectedly alters other functionality. Madeyski provided empirical evidence (via a series of laboratory experiments with over 200 developers) regarding the superiority of the TDD practice over the traditional Test Last approach or testing for correctness approach, with respect to the lower coupling between objects (CBO). The mean effect size represents a medium (but close to large) effect on the basis of meta analysis of the performed experiments which is a substantial finding. It suggests a better modularization (i. e., a more modular design), easier reuse and testing of the developed software products due to the TDD programming practice. which are indicators of the thoroughness and the fault detection effectiveness of unit tests, respectively. The effect size of TDD on branch coverage was medium in size and therefore is considered substantive effect.

Increased Confidence: TDD allows programmers to make changes or add new features with confidence. Knowing that the code is constantly tested reduces the fear of breaking existing functionality. This safety net can encourage more innovative and creative approaches to problem solving. Reduced Fear of Change, Reduced Stress: In traditional development, changing existing code can be daunting due to the risk of introducing bugs. TDD, with its comprehensive test suite, reduces this fear, as tests will immediately reveal any problems caused by changes. Knowing that the codebase has a safety net of tests can reduce stress and anxiety associated with programming. Developers might feel more relaxed and open to experimenting and refactoring. Improved Focus: Writing tests first helps programmers concentrate on requirements and design before writing the code. This focus can lead to clearer, more purposeful coding, as the developer is always aware of the goal they are trying to achieve. Sense of Achievement and Job Satisfaction: Passing tests can provide a quick, regular sense of accomplishment, boosting morale. This can be particularly motivating in long term projects where the end goal might seem distant. The combination of all these factors can lead to increased job satisfaction. When developers feel confident, focused, and part of a collaborative team, their overall job satisfaction can significantly improve.

Test driven development does not perform sufficient testing in situations where full functional tests are required to determine success or failure, due to extensive use of unit tests. Examples of these are user interfaces, programs that work with databases, and some that depend on specific network configurations. TDD encourages developers to put the minimum amount of code into such modules and to maximize the logic that is in testable library code, using fakes and mocks to represent the outside world. Management support is essential. Without the entire organization believing that test driven development is going to improve the product, management may feel that time spent writing tests is wasted. Unit tests created in a test driven development environment are typically created by the developer who is writing the code being tested. Therefore, the tests may share blind spots with the code: if, for example, a developer does not realize that certain input parameters must be checked, most likely neither the test nor the code will verify those parameters. Another example: if the developer misinterprets the requirements for the module they are developing, the code and the unit tests they write will both be wrong in the same way. Therefore, the tests will pass, giving a false sense of correctness. A high number of passing unit tests may bring a false sense of security, resulting in fewer additional software testing activities, such as integration testing and compliance testing. Tests become part of the maintenance overhead of a project. Badly written tests, for example ones that include hard coded error strings, are themselves prone to failure, and they are expensive to maintain. This is especially the case with fragile tests. There is a risk that tests that regularly generate false failures will be ignored, so that when a real failure occurs, it may not be detected. It is possible to write tests for low and easy maintenance, for example by the reuse of error strings, and this should be a goal during the code refactoring phase described above. Writing and maintaining an excessive number of tests costs time. Also, more flexible modules (with limited tests) might accept new requirements without the need for changing the tests. For those reasons, testing for only extreme conditions, or a small sample of data, can be easier to adjust than a set of highly detailed tests. The level of coverage and testing detail achieved during repeated TDD cycles cannot easily be re created at a later date. Therefore, these original, or early, tests become increasingly precious as time goes by. The tactic is to fix it early. Also, if a poor architecture, a poor design, or a poor testing strategy leads to a late change that makes dozens of existing tests fail, then it is important that they are individually fixed. Merely deleting, disabling or rashly altering them can lead to undetectable holes in the test coverage.

First TDD Conference was held during July 2021. Conferences were recorded on YouTube