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In object-oriented programming, mock objects are simulated objects that mimic the behaviour of real objects in controlled ways, most often as part of a software testing initiative. A programmer typically creates a mock object to test the behaviour of some other object, in much the same way that a car designer uses a crash test dummy to simulate the dynamic behaviour of a human in vehicle impacts. The technique is also applicable in generic programming.

In a unit test, mock objects can simulate the behavior of complex, real objects and are therefore useful when a real object is impractical or impossible to incorporate into a unit test. If an object has any of the following characteristics, it may be useful to use a mock object in its place:

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For example, an alarm clock program which causes a bell to ring at a certain time might get the current time from a time service. To test this, the test must wait until the alarm time to know whether it has rung the bell correctly. If a mock time service is used in place of the real time service, it can be programmed to provide the bell-ringing time (or any other time) regardless of the real time, so that the alarm clock program can be tested in isolation.

Mock objects have the same interface as the real objects they mimic, allowing a client object to remain unaware of whether it is using a real object or a mock object. Many available mock object frameworks allow the programmer to specify which, and in what order, methods will be invoked on a mock object and what parameters will be passed to them, as well as what values will be returned. Thus, the behavior of a complex object such as a network socket can be mimicked by a mock object, allowing the programmer to discover whether the object being tested responds appropriately to the wide variety of states such mock objects may be in.

Classification between mocks, fakes, and stubs is highly inconsistent across the literature.[1][2][3][4][5][6] Consistent among the literature, though, is that they all represent a production object in a testing environment by exposing the same interface.

Which out of mock, fake, or stub is the simplest is inconsistent, but the simplest always returns pre-arranged responses (as in a method stub). On the other side of the spectrum, the most complex object will fully simulate a production object with complete logic, exceptions, etc. Whether any of the mock, fake, or stub trio fits such a definition is, again, inconsistent across the literature.

For example, a mock, fake, or stub method implementation between the two ends of the complexity spectrum might contain assertions to examine the context of each call. For example, a mock object might assert the order in which its methods are called, or assert consistency of data across method calls.

In the book The Art of Unit Testing[7] mocks are described as a fake object that helps decide whether a test failed or passed by verifying whether an interaction with an object occurred. Everything else is defined as a stub. In that book, fakes are anything that is not real, which, based on their usage, can be either stubs or mocks.

Consider an example where an authorization subsystem has been mocked. The mock object implements an isUserAllowed(task : Task) : boolean[8] method to match that in the real authorization class. Many advantages follow if it also exposes an isAllowed : boolean property, which is not present in the real class. This allows test code to easily set the expectation that a user will, or will not, be granted permission in the next call and therefore to readily test the behavior of the rest of the system in either case.

Similarly, mock-only settings could ensure that subsequent calls to the sub-system will cause it to throw an exception, hang without responding, or return null etc. Thus, it is possible to develop and test client behaviors for realistic fault conditions in back-end sub-systems, as well as for their expected responses. Without such a simple and flexible mock system, testing each of these situations may be too laborious for them to be given proper consideration.

A mock database object's save(person : Person) method may not contain much (if any) implementation code. It might check the existence and perhaps the validity of the Person object passed in for saving (see fake vs. mock discussion above), but beyond that there might be no other implementation.

Apart from complexity issues and the benefits gained from this separation of concerns, there are practical speed issues involved. Developing a realistic piece of software using TDD may easily involve several hundred unit tests. If many of these induce communication with databases, web services and other out-of-process or networked systems, then the suite of unit tests will quickly become too slow to be run regularly. This in turn leads to bad habits and a reluctance by the developer to maintain the basic tenets of TDD.

The use of mock objects can closely couple the unit tests to the implementation of the code that is being tested. For example, many mock object frameworks allow the developer to check the order of and number of times that mock object methods were invoked by the real object being tested; subsequent refactoring of the code that is being tested could therefore cause the test to fail even though all mocked object methods still obey the contract of the previous implementation. This illustrates that unit tests should test a method's external behavior rather than its internal implementation. Over-use of mock objects as part of a suite of unit tests can result in a dramatic increase in the amount of maintenance that needs to be performed on the tests themselves during system evolution as refactoring takes place. The improper maintenance of such tests during evolution could allow bugs to be missed that would otherwise be caught by unit tests that use instances of real classes. Conversely, simply mocking one method might require far less configuration than setting up an entire real class and therefore reduce maintenance needs.

Mock objects have to accurately model the behavior of the object they are mocking, which can be difficult to achieve if the object being mocked comes from another developer or project or if it has not even been written yet. If the behavior is not modelled correctly, then the unit tests may register a pass even though a failure would occur at run time under the same conditions that the unit test is exercising, thus rendering the unit test inaccurate.[10]

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Stubbing, like mocking, means creating a stand-in, but a stub only mocks the behavior, but not the entire object. This is used when your implementation only interacts with a certain behavior of the object.

Another benefit is that you can reproduce complex scenarios more easily. For instance, it is much easier to test the many error responses you might get from the filesystem then to actually create the condition. Say that you only wanted to delete corrupt files. Writing a corrupt file can be difficult programmatically, but returning the error code associated with a corrupt file is a matter of just changing what a stub returns.

Mocks and stubs are very handy for unit tests. They help you to test a functionality or implementation independently, while also allowing unit tests to remain efficient and cheap, as we discussed in our previous post.

A great application of mocks and stubs in a unit/component test is when your implementation interacts with another method or class. You can mock the class object or stub the method behavior that your implementation is interacting with. Mocking or stubbing the other functionality or class, and therefore only testing your implementation logic, is the key benefit of unit tests, and the way to reap the biggest benefit from performing them.

In integration testing, the rules are different from unit tests. Here, you should only test the implementation and functionality that you have the control to edit. Mocks and stubs can be used for this purpose. First, identify which integrations are important. Then, you can decide which external or internal services can be mocked.

In the test code above, the read_parse_from_content method is integrated with the class that parses the JSON object from the GitHub API call. In this test, we are testing the integration in between two classes.

You can use the idea of contracts to test internal services as well. When testing a large scale application using microservices architecture it could be costly to install the entire system and infrastructure. Such applications can benefit greatly from using contract testing. In the testing pyramid, contract testing sits in between the unit/component testing and integration testing layers, depending on the coverage of the contract testing in your system. Some organizations utilize contract testing to completely replace end-to-end or functional testing.

I am attempting to test a web service interface class using PHPUnit. Basically, this class makes calls to a SoapClient object. I am attempting to test this class in PHPUnit using the getMockFromWsdl method described here: 2351a5e196