As the use of Python continues to increase, so, too, does the demand for Python testing frameworks. However, with all the options available, it can be challenging to figure out which option is right for you. In this article, you’ll learn about some of the best Python test automation frameworks.
What is a Python Testing Framework?
A Python testing framework is a set of tools, conventions, and libraries that provide a structured way to write and execute tests for Python code. Testing frameworks are essential for ensuring the correctness and reliability of software applications. They help developers automate the testing process and identify bugs or issues in their code.
Top Python Testing Frameworks
Here’s a list of top Python Testing Frameworks:
- Behave Framework
- Lettuce Framework
- Robot Framework
- Pytest Framework
- TestProject Framework
- PyUnit (Unittest) Framework
- Testify Framework
- Doctest Framework
Let’s go through the detailed of most preferred Python Testing Frameworks and understand their benefits and limitations that will help decide when to use which:
1. Behave Framework:
Behave is a Python testing framework specifically designed for behavior-driven development (BDD). BDD is a software development methodology that focuses on collaboration between developers, QA, and non-technical stakeholders by expressing requirements and specifications in a human-readable language.
Behave uses the Gherkin language, which is a simple, structured language for describing software behaviors. Gherkin is primarily used for writing executable specifications in a format that is easy for both technical and non-technical individuals to understand.
Prerequisites for Behave:
Behave can be used by anyone who possesses basic knowledge of Python. Before installing Behave, do the following:
- Install any version of Python over 2.7.14
- Install a Python package manager or pip
- Install Pycharm or a similar IDE.
Key Benefits of Behave:
- In Behave, system behavior is defined by semi-formal language and involves the use of a domain vocabulary that ensures behavior remains constant in the organization.
- There are building blocks available for the execution of a large variety of test cases.
- Facilitates effective coordination of development teams’ work on diverse modules with some similar features.
- All specs are in a similar format, offering managers better clarity on the output of developers and QAs.
Disadvantages of Behave:
- Works optimally only for black-box testing.
- Not the best fit for a unit or integration testing, as the verbosity inherent in these tests, can lead to complications in test scenarios.
When should you choose behave?
If you need to perform black-box testing, behave is a great option. While other Python testing frameworks like the Robot Framework and Pytest Framework can also be used for this type of testing, behave is particularly suitable when testing for web applications because its BDD methodology offers readable test scenarios for non-developers.
2. Lettuce Framework:
Lettuce is another BDD framework for Python. It is similar to Behave in that it allows you to write behavior specifications in a Gherkin-like language, and then map those specifications to Python code using step definitions.
Prerequisites for Lettuce:
Before installing Lettuce, do the following:
- Install Python 2.7.14 or above
- Install Pycharm or an equally capable IDE
- Install the Python package manager
Key Benefits of Lettuce:
- Enables developers to program more than one scenario and describe its characteristics in a simple, natural language
- Enables, much like Behave, productive coordination due to specs being defined in a similar format
Disadvantages of Lettuce:
- Lettuce requires a highly refined system of communication between QAs, developers, and stakeholders in order to be truly functional as a Python testing framework. There is no room for ambiguity here.
Python Automation Lettuce Framework
3. Robot Framework:
This framework is largely suitable for acceptance testing. While it has been developed with Python, it is also capable of running on IronPython (.net based) as well as Jython (Java-based). The Robot Framework is compatible with Windows, macOS, or Linux.
To use the Robot Framework, you need to have Python, Selenium WebDrivers, a text editor of your choice, and the Robot Framework installed on your operating system.
Prerequisites for Robot Framework:
Before installing Robot Framework, do the following:
- Install Python 2.7.14 or above
- Install the Python package manager (pip)
- Download a development framework such as the Pycharm Community Edition
Key Benefits of Robot Framework:
- RF is based on keyword-driven testing, it makes automation simpler by helping testers easily create readable test cases
- Enables easy usage of test data syntax
- Supports all operating systems (macOS, Windows, Linux) and all application types such as web and mobile apps.
- Easy-to-understand report data
- Its many APIs and rich ecosystem make it highly extensible and easy to integrate with third-party tools.
- It is equipped with numerous generic tools and test libraries, each of which can be individually used in separate projects.
- Good community support.
Disadvantages of Robot Framework:
- There is no default parallel test support. However, you can achieve this with Selenium Grid.
- While it is largely convenient to use, some complications may arise when creating customized HTML reports. However, it does allow for the presentation of xUnit formatted short reports.
- Requires a bit longer learning curve for beginners who need to learn to work within the RF’s predefined methodology.
- Creating generic keywords takes longer than writing coded tests.
- Report customization is quite difficult.
When should you choose the Robot framework?
While beginners may face a steep learning curve, the Robot Framework’s keyword-driven approach and easy-to-use domain-specific language (DSL) make it a versatile choice that can accommodate both inexperienced and experienced developers.
4. Pytest Framework:
Pytest is a widely-used testing framework for Python that simplifies the process of writing and executing test cases. It is known for its simplicity, scalability, and powerful features. Pytest supports various types of tests, including unit tests, functional tests, and acceptance tests.
Pytest is one of the most popularly used Python testing frameworks. It is an open-source testing framework. Pytest supports unit testing, functional testing, and API tests.
Prerequisites for Pytest Framework:
- Install Python version 3.5 or above.
Key Benefits of Pytest Framework:
- Provides a compact and simple test suite.
- Highly extensible with many plugins available, such as the Pytest HTML plugin, which can be added to your project to print HTML reports with a single command-line option.
- It has large community support.
- It helps to cover all parameter combinations without rewriting test cases.
Disadvantages of Pytest Framework:
Pytest’s proprietary routines prevent compatibility. This means that though the test cases can be easily written in this framework, you won’t be able to use those in other testing frameworks.
Python Automation- Pytest Framework
5. TestProject Framework:
TestProject is an end-to-end test automation platform that allows users to create and execute automated tests for web and mobile applications. It provides features such as record-and-playback, script-based testing using Selenium and Appium, a marketplace for reusable addons, and cloud-based services for test execution and reporting.
TestProject is an open-source automation framework. It provides cloud and local HTML reports and easy test automation development.
Supports the Pytest and Unittest frameworks and all required dependencies as part of a single executable cross-platform agent file.
Prerequisites for TestProject Framework:
- Install Python version 3.6 or above.
Key Benefits of TestProject Framework:
- Free automated reports in HTML/PDF format
- Easy access to execution history via RESTful API
- Always updated with the latest Selenium/Appium driver version
- Provides a single SDK for Android, iOS, web, and general testing
- Integrated test reporting capabilities
- Cross-platform support for all operating systems
- Extensive community and support.
Disadvantages of TestProject Framework:
- Requires support for parallel testing as only one test runs at a time.
- Team collaboration tools are limited in offline mode, and tests must be stored on a shared network drive/git when working in the field.
How to use:
If you are looking to use TestProject with Python, you can utilize the TestProject Python SDK. The Python SDK allows you to write your test scripts in Python and leverage the features provided by the TestProject platform.
Here is a simplified example of a Python test script using the TestProject Python SDK.
from src.testproject.sdk.drivers import webdriver
from src.testproject.enums import Platform
def test_example():
# Create a TestProject WebDriver instance
driver = webdriver.Chrome()
# Navigate to a website
driver.get("https://www.example.com")
# Perform actions
driver.find_element_by_css_selector("input[name='q']").send_keys("TestProject")
driver.find_element_by_css_selector("input[name='btnK']").click()
# Verify the results
assert "TestProject" in driver.title
# Close the browser
driver.quit()
To use TestProject with Python, you would need to install the TestProject Python SDK. You can find more information about the SDK and how to set up your testing environment in the TestProject documentation.
6. PyUnit (Unittest) Framework:
PyUnit (also known as Unittest) is a JUnit-inspired unit testing framework. This is the default Python testing framework that comes with the Python package by default. Therefore, most developers start testing with this.
Key Benefits of PyUnit Framework:
- It doesn’t require the installation of additional modules as it is part of the standard Python library.
- Provides simple, clean and quick test case execution.
- Fast generation of test reports, including XML and Unittest SML reports.
Disadvantages of Unittest Framework:
- Test code supports abstractions that can obscure intent and make it difficult to interpret.
- Requires a lot of ‘boilerplate’ code.
- Uses camelCase naming convention.
Read More about Python Unittest Framework