PyTest: Python Testing Framework for Backend Engineers

Introduction

As a support engineer or a QA/test engineer, it is important to ensure the quality and reliability of your code. One way to achieve this is to use Python testing frameworks to automate the testing process and identify bugs or issues in your code before releasing or deploying the software.

They offer the following benefits:

• Testing frameworks improve code quality.
• Automated testing saves time and effort.
• Code reusability optimizes testing efforts and reduces repetition.
• Python testing frameworks integrate seamlessly with other tools, modules, and libraries.

In this step-by-step guide, you will learn how to implement PyTest – one of the most popular Python testing frameworks.

Prerequisites

Before going into the details of Python testing frameworks, let's first focus on the prerequisites for implementing PyTest.

• Installing Python: Please make sure you have Python installed on your system. You can download the latest version of Python from the official Python website.
• Install Pip: Pip (Package Installer for Python) is a tool used to manage packages, libraries, and dependencies in Python. It allows us to install and manage Python packages. Make sure you have pip installed on your system.

You can check if pip is installed by running the following command in your command line/terminal:

python -m pip --version

Or

python3 -m pip --version

What is a Python testing framework?

Python testing framework is a set of tools and libraries that provide structure and guidelines for writing and executing tests in Python for automation testing. It ensures that the code meets the desired quality standards. It is essential and useful for every Backend developer, Software Engineer, DevOps and QA/Test engineer. Some of the famous and widely used Python testing frameworks are Pytest, PyUnit, DocTest, Testify, Robot and many more.

PyTest implementation

Installing PyTest

To get started with PyTest, you need to install it on your system. Open your terminal and run the following command:

pip install pytest

This command will download and install the latest version of PyTest and its dependencies.

Writing test items

Now that PyTest is installed, let's start writing some basic unit test cases.

Please create a new Python file and name it test_backend.py This file defines our test cases using PyTest syntax.

Note: By default, PyTest discovers and runs all test cases in files with names that start with «test_» or end with «_test». The pytest discovery process recursively scans the current folder and its subfolders for files that start with ”test_“ or end with ”_test”. The tests in those files are then run.

import pytest
def test_addition():
assert 3 + 3 == 6
def test_subtraction():
assert 5 - 4 == 1
def test_multiplication():
assert 4 * 4 == 16
def test_division():
assert 10 / 2 == 5

In the example above, we have defined four basic unit tests as functions: test_addition, test_subtraction, test_multiplication, and test_division. The assert keyword is used when debugging code and allows you to test whether a particular condition in your code returns True. If not, it returns an AssertionError.

Execute test cases, execute specific tests

To run the tests, go to the folder where you saved the test_backend.py file and then run the following command in your terminal or command line:

pytest

Or

python -m pytest

Output:

Output
test session starts
==================================================
platform darwin -- Python 3.9.6, pytest-7.4.3, pluggy-1.3.0
rootdir: ~/Python Testing Frameworks
collected 4 items
test_backend.py ....
==================================================
4 passed in 0.01s 

When using PyTest, you don't have to manually run each test in your file. The tool will automatically find and run all the defined test cases. If everything goes well and all the assertions are correct, you will get a nice summary of the test results along with the number of tests passed as shown above.

Let's assume that one of the tests fails at that time, we see the following output:

Output
test session starts
==================================================
platform darwin -- Python 3.9.6, pytest-7.4.3, pluggy-1.3.0
rootdir: ~/Python Testing Frameworks
collected 4 items
test_backend.py ...F 
================================================== [100%]
FAILURES
==================================================
test_division 
def test_division():
> assert 10 / 2 == 6
E assert (10 / 2) == 6
test_backend.py:13: AssertionError
short test summary info
==================================================
FAILED test_backend.py::**test_division** - assert (10 / 2) == 6
1 failed, 3 passed in 0.01s 

Experimental discovery

PyTest offers powerful test discovery capabilities that allow you to organize your test cases in a structured way. By default, PyTest discovers and executes all test cases in files with names that start with "test_" or end with "_test". However, you can customize the test discovery process using various command line options or by configuring a file. pytest.ini Customize.

For example, you can specify a specific directory to search for test modules by running the following command:

pytest tests/

This command will only run the test cases in the “tests” directory.

Testing equipment

Test tools in Pytest are amazing components that can be reused to set a consistent baseline for your test cases. They come in handy when you want to create prerequisites for your tests and when you want to clean up resources after your tests are done. PyTest makes it easy to define and use test tools beautifully.

To define a test fixture, you must use the decorator. @pytest.fixture For example, a fixture named db defined below will open a database connection and close the connection after the operation. We can define this fixture as follows:

import pytest
@pytest.fixture
def db():
# Set up the database connection
db = create_db()
yield db
# Clean up the database connection
db.close()

In this example, the yield statement defines the code to be executed after the test cases have been executed. The yield keyword is used to control the flow of a generator function, similar to the return statement used to return values in Python.

To use a fixture in a test, you must pass it as an argument to the test function.

For example:

def query_test(db):
results = db.query("SELECT * FROM users")
assert len(result) == 12

In this test case, the fixture db Automatically to function query_test() It is injected and allows you to use it to perform database operations. Fixtures are useful for developers to prepare specific test environments such as database connections, API clients, or mock objects. Using fixtures, developers can ensure that tests are isolated and repeatable.

Test engineers can also use equipment to prepare the system under test, manage test data, or simulate complex scenarios.

Additionally, the devices can be used to clean up resources after testing, improve reliability, and maintain the test set.

Result

In this tutorial, we learned how to implement and use PyTest, one of the most popular and feature-rich Python testing frameworks, and provided a step-by-step guide to implementing it in Python. Continuously writing and updating test cases is essential as your codebase evolves. By spending time and effort on testing, you can identify bugs sooner, improve code quality, and deliver more robust and reliable software.