My Learning Blog

Code duplication is generally considered a mark of poor or lazy programming style. Good coding style is generally associated with code reuse. It may be slightly faster to develop by duplicating code, because the developer need not concern himself with how the code is already used or how it may be used in the future. The difficulty is that original development is only a small fraction of a product’s life cycle, and with code duplication the maintenance costs are much higher. Some of the specific problems include:

* Code bulk affects comprehension: Code duplication frequently creates long, repeated sections of code that differ in only a few lines or characters. The length of such routines can make it difficult to quickly understand them. This is in contrast to the “best practice” of code decomposition.
* Purpose masking: The repetition of largely identical code sections can conceal how they differ from one another, and therefore, what the specific purpose of each code section is. Often, the only difference is in a parameter value. The best practice in such cases is a reusable subroutine.

* Update anomalies: Duplicate code contradicts a fundamental principle of database theory that applies here: Avoid redundancy. Non-observance incurs update anomalies, which increase maintenance costs, in that any modification to a redundant piece of code must be made for each duplicate separately. At best, coding and testing time are multiplied by the number of duplications. At worst, some locations may be missed, and for example bugs thought to be fixed may persist in duplicated locations for months or years. The best practice here is a code library.

Regression testing is any type of software testing that seeks to uncover software errors after changes to the program (e.g. bugfixes or new functionality) have been made, by retesting the program. The intent of regression testing is to assure that a change, such as a bugfix, did not introduce new bugs.[1] Regression testing can be used to test the system efficiently by systematically selecting the appropriate minimum suite of tests needed to adequately cover the affected change. Common methods of regression testing include rerunning previously run tests and checking whether program behavior has changed and whether previously fixed faults have re-emerged. “One of the main reasons for regression testing is that it’s often extremely difficult for a programmer to figure out how a change in one part of the software will echo in other parts of the software.”[2] This is done by comparing results of previous tests to results of the current tests being run.

In object-oriented programming, mock objects are simulated objects that mimic the behavior of real objects in controlled ways. A computer programmer typically creates a mock object to test the behavior of some other object, in much the same way that a car designer uses a crash test dummy to simulate the dynamic behavior of a human in vehicle impacts.

Mocks, fakes and stubs

Some authors^[1] draw a distinction between fake and mock objects. Fakes are the simpler of the two, simply implementing the same interface as the object that they represent and returning pre-arranged responses. Thus a fake object merely provides a set of method stubs.

In the book “The art of unit testing” ^[2] mocks are described as a fake object that helps decide if a test failed or passed, by verifying if an interaction on an object occurred or not. Everything else is defined as a stub. In that book, “Fakes” are anything that is not real. Based on their usage, they are either stubs or mocks.

Mock objects in this sense do a little more: their method implementations contain assertions of their own. This means that a true mock, in this sense, will examine the context of each call— perhaps checking the order in which its methods are called, perhaps performing tests on the data passed into the method calls as arguments.