Python is one of the most popular programming languages for automation testing. It offers a wide range of libraries and tools that simplify the testing process and make it more efficient. In this blog, we will discuss three advanced Python automation testing techniques that can help you improve the quality and reliability of your software.
1. Behavior-Driven Development (BDD) with Python
Behavior-driven development (BDD) is an agile software development methodology emphasizing collaboration between developers, QA, and business stakeholders. BDD focuses on creating a shared understanding of the requirements and behavior of the software being developed. BDD is implemented through a set of tools and frameworks that automate the testing process and make it more efficient.
Python has several BDD frameworks, including Behave, Lettuce, and PyStory. Behave is the most popular and widely used BDD framework in Python. Behave uses Gherkin, a simple and easy-to-understand language for describing the behavior of the software.
In Behave, you write your tests in Gherkin syntax, which is a set of keywords and phrases that describe the behavior of the software.
Here is an example:
SQLFeature: Calculator Scenario: Add two numbers Given I have a calculator When I enter 2 + 2 Then the result should be 4 |
This feature file describes a scenario in which we are testing the added functionality of a calculator. The Given, When, and Then keywords define the different steps of the scenario.
Behave allows you to write step definitions in Python, which map the Gherkin steps to Python code. Here is an example:
from behave import given, when, then @given(‘I have a calculator’)def step_impl(context): context.calculator = Calculator() @when(‘I enter {expression}’)def step_impl(context, expression): context.result = context.calculator.evaluate(expression) @then(‘the result should be {expected_result}’)def step_impl(context, expected_result): assert context.result == expected_result |
This code defines the step definitions for the scenario we defined in the feature file. The @given, @when, and @then decorators define the different steps, and the Python code implements the functionality.
Behave also provides hooks that allow you to set up and tear down your testing environment and define custom transformations to convert Gherkin step parameters into Python types.
Using Behave, you can easily create a suite of tests that describe the behavior of your software and run them using a simple command line interface.
2. Data-driven testing with Python
Data-driven testing is a testing methodology that focuses on testing the software with different sets of input data. Data-driven testing is beneficial for testing software that processes large amounts of data or performs complex calculations.
Python provides several libraries for data-driven testing, including PyTest and Robot Framework. PyTest is a popular testing framework for Python that provides several features for data-driven testing, including parameterized tests and fixtures.
Parameterized tests allow you to run the same test with different input data sets.
Here is an example:
import pytest @pytest.mark.parametrize(“input, expected_output”, [ (“2 + 2”, 4), (“3 * 4”, 12), (“8 / 2”, 4),])def test_calculator(input, expected_output): calculator = Calculator() result = calculator.evaluate(input) assert result == expected_output |
This code defines a parameterized test for the calculator class. The test is run three times, once for each set of input and expected output values.
Fixtures are a way to set up and tear down the testing environment for each test. Fixtures are beneficial for data-driven testing, as they allow you to set up the input data for each test.
Here is an example:
import pytest @pytest.fixture(params=[(“2 + 2”, 4), (“3 * 4”, 12), (“8 / 2”, 4)])def input_output(request): return request.param def test_calculator(input_output): input, expected_output = input_output calculator = Calculator() result = calculator.evaluate(input) assert result == expected_output |
This code defines a fixture that provides input and expected output data for each test. The test is run three times, once for each set of input and expected output values.
Robot Framework is another testing framework for Python that provides several features for data-driven testing, including data-driven test cases and test data files.
Here is an example:
*** Test Cases ***Addition Test [Template] ${input} ${expected_output} 2 + 2 4 3 + 3 6 4 + 4 8 *** Variables ***${calculator} Calculator() *** Keywords ***${result} Evaluate Expression ${calculator} ${input}Should Be Equal ${result} ${expected_output} |
This code defines a data-driven test case for the calculator class. The test is run three times, once for each set of input and expected output values. The ${input} and ${expected_output} variables are defined in a test data file, which allows you to modify the input data for your tests easily.
3. Parallel testing with Python
Parallel testing is a testing methodology that allows you to run multiple tests in parallel, which can significantly reduce the time it takes to run your tests. Python provides several libraries for parallel testing, including PyTest and multiprocessing.
PyTest provides several plugins for parallel testing, including the pytest-xdist plugin, which allows you to run tests in parallel on multiple CPUs or machines.
Here is an example:
$ pytest -n 4 |
This command runs your tests in parallel on four CPUs. PyTest automatically divides your test suite into four parts and runs each on a separate CPU.
Multiprocessing is a Python library that allows you to run functions in parallel using multiple CPUs.
Here is an example:
import multiprocessing def test_calculator(): inputs = [(“2 + 2”, 4), (“3 * 4”, 12), (“8 / 2”, 4)] with multiprocessing.Pool() as pool: results = pool.map(run_test, inputs) for result in results: assert result def run_test(input_output): input, expected_output = input_output calculator = Calculator() result = calculator.evaluate(input) return result == expected_output |
This code defines a test function that runs the calculator tests in parallel using the multiprocessing library. The inputs are divided into separate tasks, which are run in parallel using a pool of worker processes.
Additionally, you can use parallel testing infrastructure with thousands of machines on the cloud using third-party platforms like LambdaTest. Here you don’t need to invest in building the infrastructure as you get to use it as per your need with a subscription.
4. Headless Browser Testing
Headless browser testing is a technique for testing web applications without displaying the browser window. This technique is useful for running automated tests in the background without interfering with the user’s work. Python provides several libraries for headless browser testing, including Selenium and Puppeteer.
Selenium is a popular browser automation tool that provides a Python library for controlling web browsers. It supports several popular browsers, including Chrome, Firefox, and Safari. Here is an example of using Selenium to perform headless browser testing:
from selenium import webdriverfrom selenium.webdriver.chrome.options import Options chrome_options = Options()chrome_options.add_argument(“–headless”)driver = webdriver.Chrome(options=chrome_options) driver.get(“https://www.example.com”)assert “Example Domain” in driver.title driver.quit() |
This code creates a headless Chrome browser and navigates to the Example Domain website. The assertion checks that the page title contains the expected text. Finally, the browser window is closed using the driver.quit() method.
Puppeteer is a Python library for controlling headless Chrome and Chromium browsers using the DevTools Protocol. Here is an example of using Puppeteer to perform headless browser testing:
import asynciofrom Puppeteer import launch async def main(): browser = await launch(headless=True) page = await browser.newPage() await page.goto(“https://www.example.com”) assert “Example Domain” in await page.title() await browser.close() asyncio.run(main()) |
This code creates a headless Chromium browser using Puppeteer and navigates to the Example Domain website. The assertion checks that the page title contains the expected text. Finally, the browser window is closed using the browser.close() method.
5. API Testing
It is a technique for testing the functionality of an API by sending requests to it and examining the responses. Python provides several libraries for API testing, including Requests and PyTest.
The request is a popular library for sending HTTP requests in Python. Here is an example of using Requests to perform API testing:
import requests def test_api(): response = requests.get(“https://jsonplaceholder.typicode.com/todos/1”) assert response.status_code == 200 assert response.json()[“userId”] == 1 assert response.json()[“title”] == “delectus aut autem” |
This code sends a GET request to the JSONPlaceholder API and checks that the response status code is 200 and the expected JSON data is returned.
PyTest is a Python testing framework that provides several API testing features, including fixtures and assertions. Here is an example of using PyTest to perform API testing:
import pytestimport requests @pytest.fixturedef api_response(): return requests.get(“https://jsonplaceholder.typicode.com/todos/1”) def test_api(api_response): assert api_response.status_code == 200 assert api_response.json()[“userId”] == 1 assert api_response.json()[“title”] == “delectus aut autem” |
This code defines a fixture that sends a GET request to the JSONPlaceholder API and returns the response. The test function uses the fixture to perform API testing and checks that the response status code is 200 and that the expected JSON data is returned.
6. Mocking
Mocking is a technique for creating test doubles that simulate the behavior of dependencies or external services. This technique is useful for testing code that depends on external services, such as databases or APIs, without interacting with those services. Python provides several libraries for mocking, including unittest.mock and pytest-mock.
unittest.mock is a Python library for creating test doubles, including mocks, fakes, and stubs. Here is an example of using unittest.mock to perform mocking:
from unittest.mock import Mockimport requests def test_mock(): # Create a mock for the requests module requests_mock = Mock() # Set the mock’s return value for the get method requests_mock.get.return_value.status_code = 200 requests_mock.get.return_value.json.return_value = {“userId”: 1, “title”: “delectus aut autem”} # Use the mock instead of the requests module with patch(“requests.get”, requests_mock.get): response = requests.get(“https://jsonplaceholder.typicode.com/todos/1”) # Check that the mock’s behavior was correct assert response.status_code == 200 assert response.json()[“userId”] == 1 assert response.json()[“title”] == “delectus aut autem” |
This code creates a mock for the requests module and sets the mock’s return value for the get method. The test function uses the mock instead of the requests module by using the patch context manager. Finally, the test checks that the mock’s behavior is correct.
pytest-mock is a plugin for the PyTest testing framework that provides several features for mocking. Here is an example of using pytest-mock to perform mocking:
import pytestimport requests def test_mock(mocker): # Create a mock for the requests module requests_mock = mocker.Mock() # Set the mock’s return value for the get method requests_mock.get.return_value.status_code = 200 requests_mock.get.return_value.json.return_value = {“userId”: 1, “title”: “delectus aut autem”} # Use the mock instead of the requests module mocker.patch(“requests.get”, requests_mock.get) response = requests.get(“https://jsonplaceholder.typicode.com/todos/1”) # Check that the mock’s behavior was correct assert response.status_code == 200 assert response.json()[“userId”] == 1 assert response.json()[“title”] == “delectus aut autem” |
This code uses pytest-mock to create a mock for the requests module and set the mock’s return value for the get method. The test function uses the mock instead of the requests module by using the mocker.patch method. Finally, the test checks that the mock’s behavior is correct.
Conclusion
To summarize, Python offers several advanced automation testing techniques that can help improve the quality and reliability of your software. By utilizing techniques discussed in this blog, you can identify and fix defects in your code more efficiently and ensure that your software is robust and resilient. Incorporating these techniques into your testing process can help you deliver higher-quality software faster and more confidently. It is essential to select and apply the right techniques for your specific project and use case to optimize your testing efforts and achieve better results.