Real World Python Test Automation with Pytest (Django app)

• Why use pytest

• pytest history

• What are pytest's must know features

Here we will learn the very basics of pytest by running very simple tests.

We will cover here:

• Pytest test runner

• Skip, SkipIf

• Xfail

• Which pytest tests are executed

• How pytest works

  
  Note that this lecture is not committed to the repository because it is very basic

Here we will learn the very basics of pytest by running very simple tests.

We will cover here:

• Fixtures

• Parametrize

• Asserting Exceptions

  
  Note that this lecture is not committed to the repository because it is very basic

The Differences Between Unit Testing, Integration Testing And Functional Testing

• Unit testing means testing individual modules of an application in isolation (without any interaction with dependencies) to confirm that the code is doing things right.

• Integration testing means checking if different modules are working fine when combined together as a group.

• Functional testing means testing a slice of functionality in the system (may interact with dependencies) to confirm that the code is doing the right things.

Here I describe how to setup our project with bitbucket.

• How to signup (free tier)

• create a git repository to work with

• configure repository it with SSH keys

Here we will install pipenv to be our package manager.

• what is pipenv

• We will learn how to use it to install the packages we need for this course

• discover some cool useful commands pipenv is offering us.

• Reviewing our fully functional django web server we are going to build in this course

• Create a django project

• Create our companies service

• Creating a Company Django model to represent our Companies table in our database

• Creating a complete REST API with a few lines of code using Django Rest Framework

• Using postman to manually test our django application and database constraints

• Writing our first unit tests for our cool API

• Pytest

• Unittest

• Using unittest test classes to avoid duplicate code

• How can pytest runs unittest tests

• @pytest.mark.skip - a mark to skip a test with the pytest test runner

• @pytest.mark.xfail - a mark to not fail the test run if the marked test failed (for falky tests)

• How to write pytest tests that assert an exception was raised

• How to write tests that assert log messages

• How to use the pytest test runner

• pytest common flags to run custom tests

• Which pytest tests are executed

• Testing Django applications

• Django native test framework

• Pytest Django

• Test databases

• Why pytest is better than unittest

• Refactor our test suite from unittest style to pytest native

• Comparing our unittest test suite with our pytest native test suite

• Why pytest is better than unittest

• What is continuous integration (CI) ?

• Where did CI come from?

• How can we practice CI?

• What are bitbucket pipelines

• Create bitbucket pipelines which will run our tests every push

• What are bitbucket environment variables

• Defining bitbucket environment variables to decouple our environments (dev and remote)

• Adding an email endpoint /send-email

• Send an email to our predefined email when posting to the endoint

• Convert all tests to use only pytest features

• Pytest-django special fixtures: client, settings

What are we going to do in this section?

How to use pytest parametrize and run tests with different inputs

Here we are implementing from zero the pytest parametrize feature.
This will help us understand what is pytest doing under the hood when we use pytest paramterize.

The principal here seem complex, but this is just creating a decorator which receives arguments.

Using caching to solve fibonacci

Here we will add all the tests we added so far, to our bitbucket continuous integration pipeline

Here we are writing a pytest fixture that will help us keep track on our test's runtime.

The final version of our fibonacci function, using dynamic programming.
This is the best solution for this problem which is has  O(n)  runtime complexity and O(1) space complexity.

How to write a performance test.

We will write a test that if the runtime of the test exceeds the runtime threshold we define, then we fail our tests.
This way, we can discover if we introduced bottlenecks to our codebase.

Why did we implement the performance test?
We can simply use the pytest-timeout plugin which basically performs the same.