Test-driven development

Test-driven development (TDD) is a software development process relying on software requirements being converted to test cases before software is fully developed, and tracking all software development by repeatedly testing the software against all test cases. This is as opposed to software being developed first and test cases created later.

Software engineer Kent Beck, who is credited with having developed or "rediscovered" the technique, stated in 2003 that TDD encourages simple designs and inspires confidence.

Test-driven development 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.Newkirk, JW and Vorontsov, AA. ''Test-Driven Development in Microsoft .NET'', Microsoft Press, 2004.

Programmers also apply the concept to improving and debugging legacy code developed with older techniques.Feathers, M. Working Effectively with Legacy Code, Prentice Hall, 2004

Test-driven development cycle

The following sequence is based on the book ''Test-Driven Development by Example'':

;1. Add a test
:The adding of a new feature begins by writing a test that passes iff the feature's specifications are met. The developer can discover these specifications by asking about use cases and user stories. A key benefit of test-driven development is that it makes the developer focus on requirements ''before'' writing code. This is in contrast with the usual practice, where unit tests are only written ''after'' code.
;2. Run all tests. The new test ''should fail'' for expected reasons
:This shows that new code is actually needed for the desired feature. It validates that the test harness is working correctly. It rules out the possibility that the new test is flawed and will always pass.
;3. Write the simplest code that passes the new test
:Inelegant or hard code is acceptable, as long as it passes the test. The code will be honed anyway in Step 5. No code should be added beyond the tested functionality.
;4. All tests should now pass
:If any fail, the new code must be revised until they pass. This ensures the new code meets the test requirements and does not break existing features.
;5. Refactor as needed, using tests after each refactor to ensure that functionality is preserved
:Code is refactored for readability and maintainability. In particular, hard-coded test data should be removed. Running the test suite after each refactor helps ensure that no existing functionality is broken.
:*Examples of refactoring:
:** moving code to where it most logically belongs
:** removing duplicate code
:** making names self-documenting
:** splitting methods into smaller pieces
:** re-arranging inheritance hierarchies

;Repeat
:The cycle above is repeated for each new piece of functionality. Tests should be small and incremental, and commits made often. That way, if new code fails some tests, the programmer can simply undo or revert rather than debug excessively. When using external libraries, it is important not to write tests that are so small as to effectively test merely the library itself, unless there is some reason to believe that the library is buggy or not feature-rich enough to serve all the needs of the software under development.

Development style

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. In ''Test-Driven Development by Example'', Kent Beck also suggests the principle "Fake it till you make it".

To achieve some advanced design concept such as a design pattern, tests are written that generate that design. The code may remain simpler than the target pattern, but still pass all required tests. This can be unsettling at first but it allows the developer to focus only on what is important.

Writing the tests first: The tests should be written before the functionality that is to be tested. This has been claimed to have many benefits. It helps ensure that the application is written for testability, as the developers must consider how to test the application from the outset rather than adding it later. It also ensures that tests for every feature get written. Additionally, writing the tests first leads to a deeper and earlier understanding of the product requirements, ensures the effectiveness of the test code, and maintains a continual focus on software quality. 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.

Keep the unit small

For TDD, a unit is most commonly defined as a class, or a group of related functions often called a module. Keeping units relatively small is claimed to provide critical benefits, including:
* Reduced debugging effort – When test failures are detected, having smaller units aids in tracking down errors.
* Self-documenting tests – Small test cases are easier to read and to understand.

Advanced practices of test-driven development can lead to acceptance test–driven development (ATDD) and specification by example where the criteria specified by the customer are automated into acceptance tests, which then drive the traditional unit test-driven development (UTDD) process.Koskela, L. "Test Driven: TDD and Acceptance TDD for Java Developers", Manning Publications, 2007 This process ensures the customer has an automated mechanism to decide whether the software meets their requirements. With ATDD, the development team now has a specific target to satisfy – the acceptance tests – which keeps them continuously focused on what the customer really wants from each user story.

Best practices

Test structure

Effective layout of a test case ensures all required actions are completed, improves the readability of the test case, and smooth's 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.

Individual best practices

Some best practices that an individual could follow would be to separate common set-up and tear-down logic into test support services utilized by the appropriate test cases, to keep each test oracle focused on only the results necessary to validate its test, and to design time-related tests to allow tolerance for execution in non-real time operating systems. The common practice of allowing a 5-10 percent margin for late execution reduces the potential number of false negatives in test execution. It is also suggested to treat test code with the same respect as production code. Test code must work correctly for both positive and negative cases, last a long time, and be readable and maintainable. Teams can get together with and review tests and test practices to share effective techniques and catch bad habits.

Practices to avoid, or "anti-patterns"

* Having test cases depend on system state manipulated from previously executed test cases (i.e., you should always start a unit test from a known and pre-configured state).
* Dependencies between test cases. A test suite where test cases are dependent upon each other is brittle and complex. Execution order should not be presumed. Basic refactoring of the initial test cases or structure of the UUT causes a spiral of increasingly pervasive impacts in associated tests.
* Interdependent tests. Interdependent tests can cause cascading false negatives. A failure in an early test case breaks a later test case even if no actual fault exists in the UUT, increasing defect analysis and debug efforts.
* Testing precise execution, behavior, timing or performance.
* Building "all-knowing oracles". An oracle that inspects more than necessary is more expensive and brittle over time. This very common error is dangerous because it causes a subtle but pervasive time sink across the complex project. by Pathfinder Solutions
* Testing implementation details.
* Slow running tests.

Benefits

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.

MadeyskiMadeyski, L. "Test-Driven Development - An Empirical Evaluation of Agile Practice", Springer, 2010, , pp. 1-245. DOI: 978-3-642-04288-1 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. Madeyski also measured the effect of the TDD practice on unit tests using branch coverage (BC) and mutation score indicator (MSI), 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. These findings have been subsequently confirmed by further, smaller experimental evaluations of TDD.M. Pančur and M. Ciglarič, "Impact of test-driven development on productivity, code and tests: A controlled experiment", Information and Software Technology, 2011, vol. 53, no. 6, pp. 557–573, DOI: 10.1016/j.infsof.2011.02.002D. Fucci, H. Erdogmus, B. Turhan, M. Oivo, and N. Juristo, "A dissection of the test-driven development process: does it really matter to test-first or to test-last?", IEEE Transactions on Software Engineering, 2017, vol. 43, no. 7, pp. 597–614, DOI: 10.1109/TSE.2016.2616877A. Tosun, O. Dieste Tubio, D. Fucci, S. Vegas, B. Turhan, H. Erdogmus, A. Santos, M. Oivo, K. Toro, J. Jarvinen, and N. Juristo, "An industry experiment on the effects of test-driven development on external quality and productivity", Empirical Software Engineering, 2016, vol. 22, pp. 1–43, DOI: 10.1007/s10664-016-9490-0B. Papis, K. Grochowski, K. Subzda and K. Sijko, "Experimental evaluation of test-driven development with interns working on a real industrial project", IEEE Transactions on Software Engineering, 2020, DOI: 10.1109/TSE.2020.3027522

Limitations

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.

Test-driven work

Test-driven development has been adopted outside of software development, in both product and service teams, as test-driven work. For testing to be successful, it needs to be practiced at the micro and macro levels. Every method in a class, every input data value, log message, and error code, amongst other data points, need to be tested. Similar to TDD, non-software teams develop quality control (QC) checks (usually manual tests rather than automated tests) for each aspect of the work prior to commencing. These QC checks are then used to inform the design and validate the associated outcomes. The six steps of the TDD sequence are applied with minor semantic changes:
# "Add a check" replaces "Add a test"
# "Run all checks" replaces "Run all tests"
# "Do the work" replaces "Write some code"
# "Run all checks" replaces "Run tests"
# "Clean up the work" replaces "Refactor code"
# "Repeat"

TDD and ATDD

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.

TDD and BDD

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

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