- pytest Quick Start Guide by Bruno Oliveira
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- Mocks and Monkeypatching in Python
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- An Introduction to Mocking in Python
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Common uses for Mock objects include:. You might want to replace a method on an object to check that it is called with the correct arguments by another part of the system:. Once our mock has been used real. In most of these examples the Mock and MagicMock classes are interchangeable. As the MagicMock is the more capable class it makes a sensible one to use by default. Once the mock has been called its called attribute is set to True. This example tests that calling ProductionClass. In the last example we patched a method directly on an object to check that it was called correctly. Another common use case is to pass an object into a method or some part of the system under test and then check that it is used in the correct way. The simple ProductionClass below has a closer method. If it is called with an object then it calls close on it. So to test it we need to pass in an object with a close method and check that it was called correctly. Accessing close creates it. A common use case is to mock out classes instantiated by your code under test. When you patch a class, then that class is replaced with a mock. Instances are created by calling the class. The call to patch replaces the class Foo with a mock. It can be useful to give your mocks a name. The name is shown in the repr of the mock and can be helpful when the mock appears in test failure messages. The name is also propagated to attributes or methods of the mock:. Often you want to track more than a single call to a method. This is useful because as well as asserting that the calls you expected have been made, you are also checking that they were made in the right order and with no additional calls:. However, parameters to calls that return mocks are not recorded, which means it is not possible to track nested calls where the parameters used to create ancestors are important:. Sometimes you want to mock up a more complex situation, like for example mock.
Mocks and Monkeypatching in Python
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The Python unittest library includes a subpackage named unittest. Note: mock is newly included in the standard library as of Python 3. As a developer, you care more that your library successfully called the system function for ejecting a CD with the correct arguments, etc. Or worse, multiple times, as multiple tests reference the eject code during a single unit-test run! Our test case is pretty simple, but every time it is run, a temporary file is created and then deleted. Additionally, we have no way of testing whether our rm method properly passes the argument down to the os. We can assume that it does based on the test above, but much is left to be desired. With these refactors, we have fundamentally changed the way that the test operates. Now, we have an insideran object we can use to verify the functionality of another. Well, Python is somewhat of a sneaky snake when it comes to imports and managing modules. At runtime, the mymodule module has its own os which is imported into its own local scope in the module. If you need to mock the tempfile module for myproject. MyElaborateClassyou probably need to apply the mock to myproject. The rm method defined earlier is quite oversimplified. Our testing paradigm has completely changed. We now can verify and validate internal functionality of methods without any side-effects. Great, so we now know that the RemovalService works as planned. The mock library has a special method decorator for mocking object instance methods and properties, the mock. Notice anything interesting in there? The patching mechanism actually replaced the rm method of all RemovalService instances in our test method. That means that we can actually inspect the instances themselves. If you want to see more, try dropping in a breakpoint in your mocking code to get a good feel for how the patching mechanism works. Basically, when mapping decorators to method parameters, work backwards. Consider this example:. Notice how our parameters are matched to the reverse order of the decorators? Since the patch to sys is the outermost patch, it will be executed last, making it the last parameter in the actual test method arguments. Take note of this well and use a debugger when running your tests to make sure that the right parameters are being injected in the right order. Instead of mocking the specific instance method, we could instead just supply a mocked instance to UploadService with its constructor. The mock. What this means, practically speaking, is that when the returned instance is interacted with, it will raise exceptions if used in illegal ways. More specifically, if a method is called with the wrong number of arguments, an exception will be raised. This is extremely important as refactors happen. As a library changes, tests break and that is expected. Without using an auto-spec, our tests will still pass even though the underlying implementation is broken. The mock library also includes two important classes upon which most of the internal functionality is built upon: mock. Mock and mock. When given a choice to use a mock. Mock instance, a mock. MagicMock instance, or an auto-spec, always favor using an auto-spec, as it helps keep your tests sane for future changes. This is because mock.