Python can be an exceptional programming language, since it’s readily accessible and it delivers a unique way of creating the projects you want. However, you do need to take into account a multitude of aspects, especially when it comes to finalizing the project. It’s imperative to focus on things like unit testing and ensure that everything works exactly as expected. Here are some things to take into account for Python unit testing.
Create repeatable tests
The reason why you want repeatable tests is because they can be reused a multitude of times. These need to be consistent too, since you want to ensure the program can pass all the tests properly and without any error. If a test is not reliable, then that creates its own share of issues.
Use stubs and mocks
These are designed to mimic real classes, even if they are fake. They can isolate the code unit you are testing. It’s a great way to eliminate any type of dependencies between the unit tests. Once you start doing that, it delivers a much better value and result.
Test both negative and positive scenarios
A lot of developers follow the steps for unit testing in positive scenarios only. But it’s also a great idea to test under negative scenarios. You can accumulate a lot of data and see if things hold up even in the case of an error or any negative scenario.
Try to have a high code coverage
You always want to have a higher coverage, maybe up to 100% if possible. That way, you can handle the right input and also ensure the user behavior changes are addressed properly. The higher you aim with the coverage, the better it will be, so try to take that into account.
Use descriptive test names
The reason you need to do that is because you will know exactly what the test does and also what might need changed for it. Whenever a test fails, you know its functionality and the reasons why it’s not working the way it should.
Go for those focused, smaller tests
Some people choose to have a more complex, large test. But a good rule of thumb is to try and keep things at a smaller scale. Once you have a small scale, it becomes much easier to narrow things down and eliminate any problems. You can also understand what failed and solve the issue a lot quicker.
We recommend using these Python unit testing ideas and implement them soon if possible. They can deliver an exciting result and great value, while also pushing the boundaries when it comes to quality and professionalism. There will always be demanding situations when it comes to unit testing. And that’s why we think you need to create descriptive test names, along with sticking with smallest tests. Making sure every test can test multiple scenarios and aiming for a higher code coverage can also help. These tests can be versatile, and you can always use stubs and mocks as well!
