Automation: Concepts and Tutorial

In this tutorial, we focus on optimizing test performance by choosing the right fixture scope and managing complex test setups with proper teardown using yield . 1. Optimizing Test Performance: Choosing the Right Fixture Scope Fixtures can have different scopes, such as function , class , module , or session , depending on how long you want the fixture to persist. Simple Example: Module-Scoped Database Connection import pytest @pytest.fixture(scope="module") def db_connection(): print("Connecting to the database...") yield {"db": "connected"} print("Closing database connection") def test_query_1(db_connection): assert db_connection["db"] == "connected" def test_query_2(db_connection): assert db_connection["db"] == "connected" Explanation: The database connection is created once for the entire module and shared across multiple tests, reducing setup overhead. C...

In this tutorial, we'll explore advanced fixture features in Pytest: implicit fixtures using autouse=True , handling dependencies between multiple fixtures, and creating dynamic fixtures using factories. 1. Fixture Autouse: Implicit Fixture Usage Fixtures can be automatically applied to all test functions without explicitly including them as arguments, using the autouse=True option. Simple Example: Setting Up and Tearing Down a Temporary Directory import pytest import os @pytest.fixture(autouse=True) def create_tmp_directory(tmpdir): os.mkdir(tmpdir) print(f"Temporary directory created: {tmpdir}") def test_file_creation(): assert os.path.exists("some_file.txt") is False Explanation: The fixture automatically creates a temporary directory before the test runs, ensuring each test has a fresh, isolated environment. Complex Example: Automatically Set Up and Tear Down Database Connections import pytest @pytest.fixture(autouse=True)...

In this article, we will explore advanced techniques to master parametrization in Pytest, enabling you to write more scalable and flexible test cases. Pytest's @pytest.mark.parametrize is a powerful feature that allows us to run the same test multiple times with different data inputs. However, beyond the basics, there are more sophisticated ways to leverage this tool, which we’ll cover here. Recap: Basic Parametrization If you're already familiar with basic parametrization in Pytest, you'll know that it involves the @pytest.mark.parametrize decorator, which allows passing various inputs to a single test function. Here’s a quick recap of a basic example: import pytest @pytest.mark.parametrize("a, b, expected", [(1, 2, 3), (4, 5, 9), (10, -5, 5)]) def test_addition(a, b, expected): assert a + b == expected In this simple example, the test runs three times, each with a different set of input values for a , b , and expected . 1. Parametrizing Multip...