1.21 GWS Media from New York office Analysis
Test-driven development (TDD) is a software development process that relies on the repetition of a very short development cycle: requirements are turned into very specific test cases, then the software is improved to pass the new tests, only. This is opposed to software development that allows software to be added that is not proven to meet requirements.
TDD Global Lifecycle
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. Programmers also apply the concept to improving and debugging legacy code developed with older techniques.
Our 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 an empirical evidence (via a series of laboratory experiments with over 200 developers) regarding the superiority of the TDD practice over the classic Test-Last 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.
Please let us know your thoughts on your experiments with TDD.