Use Hypothesis,Schemathesis,Mock,MonkeyPatching and Pytest for Robust Property-Based Testing in Python
Pytest is a Python testing framework. It was designed to make testing more efficient, reliable, and enjoyable. Pytest is built on top of the popular unittest testing module and adds many features and enhancements. With Pytest, you can run tests for individual files or directories, use plugins to customize your testing experience, and generate detailed . For more details on Pytest, check out the Pytest Readme.
Hypothesis is a property-based testing library for Python. It lets you write tests that check the correctness of your code by specifying properties that your code should satisfy. Hypothesis then generates test data to check if those properties hold true. This can help you catch a wide range of edge cases and unexpected behaviour that may not be covered by example-based testing.
Schemathesis is a modern API testing tool based on the OpenAPI and GraphQL specifications. It uses Hypothesis under the hood to apply property-based testing to API schemas. In other words, given a schema, Schemathesis can automatically generate test cases for you. Since FastAPI is based on OpenAPI standards, Schemathesis works well with it.
In unit testing, a code block that you want to test may depend on other code. When we unit test, we test only a code block without those dependencies. To isolate the behavior of the code you need to test, you need to replace the other code with mocks that simulate the behavior of the real code. So, mocking is using mocks that simulate real code behavior.
pytest-mock is a plugin for Pytest that provides a simple way to create and use mocks in your tests. It allows you to replace dependencies with mocks, and then check if the code under test interacts with those dependencies correctly.
Monkeypatching is a term for mocking or patching a piece of code (class, function, module, variable or object) as part of a Unit Test. It is a technique used to replace a piece of code with a mock, so that the code under test interacts with the mock instead of the real code
By the end of this tutorial you should be able to:
- Understand the key differences between example-based, property-based and model-based testing
- Use the Hypothesis library with Pytest to test your code and ensure coverage for a wide range of test data
- Apply property-based testing to your Python apps
- Build a Shopping App and test it using property-based testing
Example-based testing and property-based testing are two common approaches to software testing along with model-based testing.
Example-based testing involves writing test cases that provide specific inputs and expected outputs for functions or methods.
- These tests are easy to write and understand, and they can catch many common errors.
Property-based testing involves testing properties or invariants that code should satisfy, and then automatically generating test data to check if those properties hold true.
- This approach can catch a much broader range of edge cases and unexpected behaviour that may not be covered by example-based testing.
Is MonkeyPatch The Same As Mocking or Patching? Yes and No ;)
The two are very similar and have subtle differences.
`Monkeypatching` is the act of replacing a function, method, class or variable at runtime.
`Mock` actually uses Monkeypatch under the hood to mock or change certain objects being evaluated at run time as part of your test.
The exact differences are not really important, what’s more important is that you understand that it’s possible to override functions, classes, libraries and variables in Unit Tests.