A Live Intro to Python Testing [Testμ 2023]

Open VS Code

Andrew navigated and launched the Python project folder in VS Code, as he preferred using VS Code for Python coding. This was due to the helpful extension provided by Microsoft for Python development.

Python Extension

Andrew had a Python extension installed already. He guided his audience on installing the Python extension if it was not installed.

Click on the Extension icon from the left menu items on VS Code, then on the search bar, type in Python, and then click on the Install button from the right side details page.

Andrew mentioned that Microsoft maintains the Python extension, a go-to extension with a large community and good support. He also mentioned that its simple syntax made Python a good starting point.

Pytest Testing Framework

Andrew preferred to use pytest as his testing framework as it provides unique advantages. Pytest comes with standard Python libraries called unittest. Before he delved into the specifics of the directory structure, he informed the audience that it was essential to grasp some key concepts related to the unittest.

Unittest

The unittest module is a pre-existing library in Python designed for testing individual parts of code. It enables developers to create and run consistent tests, ensuring their code works as intended and stays dependable over time.

• Test Case: In software testing, a test case refers to an individual assessment designed to verify a specific aspect of a piece of code. It encompasses inputs and the anticipated outcomes for those inputs. Test cases are typically authored using the unittest module and are often stored separately from the main codebase.
• Test Suite: A test suite comprises a collection of test cases executed together. Test suites can group related tests, facilitating collective execution as a single entity.
• Test Runner: A test runner is a software tool responsible for executing an entire test suite and presenting the results. In Python, various test runners are available, including the built-in unittest runner and external frameworks like pytest. Test runners help streamline running and managing tests, ensuring software behaves as expected.

Live Coding with Basic Python Code

Andrew further guided his audience through the step-by-step procedure and gave an insight into how easy it can be to write Python code and to test using the pytest framework.

• He started by creating a folder called tests under the root folder. He mentioned that the test folder consists of all the test modules he will develop further.

• Next, Andrew created a Python file under the test folder. The file name was test_basics.py

• Andrew wrote a straightforward code in Python where there was no need to create a package, as it can get complicated.

Code Explanation

Andrew explained that this simple code is a pytest code, with no need to write separate classes, methods, or test functions. He further explained each line of the code.

• def test_one_plus_one(): : This line defines a test function named test_one_plus_one(). It’s like a mini-program to check if 1 + 1 ==2 or not, test_one_plus_one() is a user-defined function in Pytest. Each function is a test function.
• assert 1+1 == 2: This line checks if the calculation of 1+1 equals 2. The assert is used for validation purposes; in this code, it ensures that if this statement is false, the test will fail.

In short, the code sets up a test to confirm if adding 1 and 1 equals 2. If it’s true, the test passes; if not, it fails.

Running the Test

To run the above pytest code, the installation of pytest is needed. In some other testing environments, like when working with JavaScript, use npm, and for Java, there is a Maven project where all the Maven dependencies are together. Likewise, to run a Python project, There is a need to create a Virtual Environment.

What is a Virtual Environment?

Virtual Environment stores all the packages on which the Python project depends; rather than making it global for your whole machine, creating a virtual environment can make it local for your project. Andrew preferred to use the local Virtual Environment for this live coding session.

• The command installed the Virtual Environment files that support the Python project.

• Once the virtual environment has been created and the packages are installed, the activation of the virtual environment must be done before use and deactivated after use.

• Andrew mentioned that the command to activate the virtual environment could be different for Windows. For more details, visit the official site of venv Python.
• Once you are in the virtual environment, install any needed libraries. Andrew installed the pytest package using the following command.

• After the installation of pytest third-party libraries, to run any test, the command is as follows: to run the particular test function, mention the file name at the end, or else, say the folder. All the files under the given folder will be executed.

Passed Test Case Output

Once the command is executed, the summary of the test run is given, whether pass or fail.

Andrew further explained what the test command displays in the terminal.

• The test summary.
• The number of test cases it collected.
• The file path, along with the green dot, indicates the Pass of the test case.

Failed Test Case

Andrew wrote another test script that checks if value a + value b == value c. He explained each line of code in further detail.

• def test_one_plus_two(): : This line defines a function named test_one_plus_two().
• a = 1: Store the value 1 in a variable named a.
• b = 2: Store the value 2 in a variable named b.
• c = 0: Store the value 0 in a variable named c.
• assert a + b == c: Check if the sum of a and b equals c (which is not true in this case).

Failed Test Case Output

Explanation:

• test_one_plus_two is the name of the test function.
• F indicates that one test failed.
• The assertion assert a + b == c failed because 1 + 2 is not equal to 0.
• The summary at the end indicates that there was one test failure.

Pytest with Playwright

Andrew further involved Playwright in his test script, nothing too fancy, with basic details on installing third-party Playwright libraries. He installed Playwright to write the next set of test instructions to run a Playwright script with Python.

Command to Install Playwright & pytest-Playwright Library

Andrew used the pytest command to install the Playwright library.

Now run the playwright install command to install the Playwright browser projects, which are three of these: Chrome, Firefox, and WebKit.

Create a New Test File

Andrew created a new project file, test_web_stuff, under the same folder called tests.

He wrote a set of Python instructions where he automated the flow of visiting Playwright’s official website.

Explanation

These instructions use the Playwright library to perform automated testing on a webpage.

• import : The code imports necessary modules.
• def : Define a test function that uses a Playwright page to navigate to a specified URL (likely “dev/”)
• except : Expect the page title to match a pattern using regular expression (regex) that includes the word Playwright.

The test aims to check if the webpage’s title contains the word, Playwright.

Output

The command is the same as before: pytest followed by the file and folder names if required.

He used headed and slowmo and gave some milliseconds to grab the screen load, as the original test ran in 16 milliseconds.

It was a wonderful warp-up by Andrew, and he hopes his audience learned and enjoyed the live coding session. It was a great opportunity for his audience to get hands-on experience with Python Testing. He ended the session with some Q&A’s