Python Test Automation Frameworks You Need to Know in 2024

Python test automation frameworks are a collection of specialized tools and libraries that aim to improve efficiency and reliability via a structured approach that reduces manual intervention. Because of this, Python test automation frameworks have gained popularity across various domains, including web development and data analysis, and have become particularly successful when it comes to test automation.

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 and review them based on their community support, developer experience, and maturity in production environments. By the end of this article, you'll have a better idea of which framework is right for you. So let's jump right in!

The Robot Framework was released in 2008 and is a specialized test framework for automating acceptance tests and implementing acceptance test-driven development (ATDD). The framework is open source and has been widely adopted by Python developers and tech companies for its mature and robust architecture.

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.

One of the major benefits of the Robot Framework is that it's open source and has a large and engaging community of contributors. Thanks to its simple and easy-to-learn syntax, it's even accessible to users with limited programming experience.

Another benefit of the Robot Framework is that it supports keyword-driven testing and data-driven testing, making it easy to create reusable test cases. Moreover, it can be integrated with a variety of external libraries and tools, such as Selenium for web testing and Appium for mobile testing.

In addition, the Robot Framework offers a straightforward library API that allows users to develop customized test libraries that can be natively implemented using Python. This API enables users to extend the framework's capabilities and create their own reusable test libraries that can be seamlessly integrated with the framework's existing infrastructure and tooling.

One of the disadvantages of the Robot Framework is that it can cause issues when trying to customize HTML reports because you may need to restructure formatting elements. However, it does provide the ability to read logs and reports in HTML format.

Another drawback is that the Robot Framework's keyword-driven approach can make it difficult to write and maintain tests for complex, highly dynamic applications. For instance, if you're testing a large web application that changes dynamically based on user interactions, you may need to create various keywords that interact with these dynamic elements, which, over time, can become challenging to manage and maintain.

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.

However, it's important to note that this framework may sometimes struggle with stability in supporting IDEs and integrating with third-party plugins, such as IntelliBot (results will depend on your specific configurations and the setup of your environment). If your projects require customization and specific requirements for integration, pytest may be a better option, which you'll learn about next.

pytest is an extensively used Python testing framework that facilitates writing concise and scalable tests for databases and user interfaces, with a focus on API testing. Interestingly, it also covers a range of tests, from simple unit tests to complex functional tests.

Thankfully, using pytest requires only a basic understanding of Python. All you need is a desktop provisioned with a functioning CLI, an IDE, pytest, and version 3.7 or newer of Python.

As mentioned previously, one of the benefits of pytest is that it can be extended and customized to fit a variety of testing needs through the use of plugins, such as `pytest-cov`, `pytest-randomly`, `pytest-bdd`, and `pytest-django`. pytest simplifies test writing by reducing the need for boilerplate code through the use of plugins, function-based syntax, and built-in fixtures. Moreover, thanks to plugins like `pytest-catchlog` and `pytest-vscodedebug`, you don't need to manually check logs or debug.

In addition, pytest has a thriving community of supporters and boasts a rich plugin architecture with over 800 external plugins that enable users to extend its functionality and tailor it to their needs.

pytest also allows selective test execution for test files. This means that developers can save time and resources by only running tests that are relevant to the changes that have been made in the codebase rather than running the entire suite.

Moreover, pytest is license-free, so you don't have to worry about any licensing restrictions or costly fees.

The major drawback of using pytest is that integrating it with other frameworks can be challenging because it uses specific routines for writing tests, which may require a complete rewrite of the code. In other words, when integrating pytest with other frameworks (i.e., Django or Flask) the testing code may need to be rewritten to adhere to pytest's syntax and structure, which can be time-consuming, especially if the existing test suite is large.

Based on the previously mentioned considerations, if you want to adopt the pytest framework, it's important to consider the nature of your project and whether the special routines provided by pytest will align with your testing requirements.

If the special routines don't align and you need to integrate with other frameworks, you may want to consider other testing frameworks, such as Robot, that are easier to integrate. However, if you have unit tests that are compact and straightforward, pytest is a great choice.

behave is another popular Python test framework and is recognized for its effectiveness in behavior-driven development (BDD). It bears a resemblance to Cucumber in that both frameworks use the Gherkin natural language syntax to specify tests that are integrated during execution. Additionally, all desired code behavior is specified by behavior specifications and facilitates the reuse of once-defined steps in other use cases.

To use behave, you need Python (version 2.7 or above) as well as pip to manage and install Python dependencies.

As previously mentioned, behave utilizes the Gherkin language, so individuals do not need to possess any technical expertise to create feature files. It also offers integration with Django and Flask, which helps to improve collaboration, increase test coverage, improve code quality, and simplify test maintenance.

Another advantage of behave is that it follows BDD methodology, which is a popular testing approach that emphasizes collaboration. It helps developers clearly understand what an application should do and detect bugs early in the development process, leading to better-quality applications.

behave supports tests for APIs and visual testing, which detects visual regressions and ensures that your application's UI/UX remains consistent across different platforms and devices.

In addition, behave offers human-readable test scenarios, comprehensive online documentation, and a strong community of users who provide support and guidance through the official behave website, GitHub repository, Python subreddit, and Python Discord server.

The major drawback of behave is that it's only suitable for black-box testing since it lets users write tests that are behavior focused. This is because the BDD methodology emphasizes behavior over the underlying implementation details of a software. The implication is that behave is not well-suited for unit or integration tests, as the verbose nature of these tests may result in complex test scenarios.

If you need to perform black-box testing, behave is a great option. While other Python testing frameworks like the Robot Framework and pytest 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.

To determine if behave is a good fit for your needs, you need to consider how important certain functionalities are in your testing process. Let's say your team is building a web-based e-commerce platform and you need to carry out integration or unit testing. In this scenario, behave isn't optimal due to its verbose nature. In such cases, you should use a more lightweight framework, such as `pytest-bdd`, which is an alternative to behave that uses the benefits of pytest for testing behavior-driven scenarios.

However, behave is a good choice when you're testing APIs and web applications, specifically for black-box testing, and if your team has adopted BDD methodology. Moreover, its natural language syntax makes communication of test results easier for both technical employees and non-technical stakeholders alike.

PyUnit, also known as unittest, is a unit-testing framework in Python that takes inspiration from JUnit and comes pre-installed with Python. The framework is supported by a thriving community of developers, making it easy for users to find resources and answers to questions.

Because PyUnit comes pre-installed with Python, only a basic knowledge of Python and an IDE (with pip) are required to use it.

One benefit of PyUnit is that as part of the standard Python library, you don't need to install anything else. In addition, PyUnit offers simple and efficient test case execution that lets developers write test cases that consist of one or more test methods, each of which verifies a specific aspect of the code.

The framework also supports several testing features, such as `test fixtures`, `test cases`, `test runners`, `test discovery`, and command line options. This is especially convenient when you have a wide range of testing needs.

In addition, PyUnit can comfortably run small individual test cases and can handle larger tests, as proven with the Zope project. This project highlights PyUnit's versatility and scalability when it comes to adapting to the needs of a wide range of testing scenarios, from small-scale unit tests to large-scale integration tests.

Firstly, one drawback of PyUnit is that it doesn't use snake_case (like Python) but instead uses camelCase. This can be confusing for someone working with Python who has to switch to a different naming convention for testing.

Secondly, while abstractions can be beneficial in unit testing, PyUnit provides a very high level of abstraction that can make the intent of the test code unclear and difficult to understand and maintain.

PyUnit also makes extensive use of test fixtures. This allows for a lot of flexibility because you create your environment from scratch with every test. Unfortunately, this also leads to a significant amount of repetitive boilerplate code, which increases the time it takes to develop tests. This can be challenging when working in a rapidly changing environment where test scenarios need to frequently be updated.

If you're looking for a simple and straightforward framework that can handle small, medium, and large projects, PyUnit is a good choice. It also allows you to easily write tests and run them as a whole, either in text or GUI mode. This is particularly useful if you're looking to run tests with a simple click.

However, it's important to note that while PyUnit supports parameterized tests, the official Python documentation doesn't have a dedicated section on PyUnit test parameterization. Instead, you have to rely on third-party articles and tutorials for comprehensive details.

It's worth noting that when using PyUnit for parameterized testing, you'll need to define your tests using the `subTest()` method. While this approach can be effective, it can also be somewhat time-consuming, as it may require you to manually define your test iterations.

In contrast, the pytest framework provides a much more concise way of defining a variety of parameterized tests. If you anticipate needing to write a large number of parameterized tests or have complex testing requirements, pytest would be a better choice.

doctest is a test automation framework that allows you to embed test cases directly within your code documentation. The framework identifies sections of text that resemble interactive Python sessions and then runs these sessions to ensure they function as desired.

To use doctest, you need to install a version of Python 3 and have some familiarity with Python's standard library. You also need a text editor to write and edit your Python code, including the docstrings that define your doctest test cases.

One of the benefits of doctest is that when you install Python, doctest comes bundled with it, so you can start using it right away without any additional setup. Moreover, doctest allows you to embed test cases in the docstrings of your functions. This means you can quickly and easily test your Python code while writing it.

In addition, doctest boasts a supportive community of contributors and a wealth of helpful resources and documentation for developers looking to use this simple and lightweight testing framework.

One of the drawbacks of doctest is that the commands are interdependent, meaning they are built upon each other and cannot be run individually. This interdependence means that a failure in one test can cause subsequent tests to fail, even if those subsequent tests would have passed on their own. This makes it difficult to identify the root cause of a failure. Moreover, refactoring code can also be challenging, as it may require you to update multiple doctests that rely on that code.

While doctest is used for documenting and verifying code examples within the documentation, it can't be relied upon as the sole means of testing a codebase since it's less comprehensive at identifying edge cases or subtle regression bugs.

doctest also finds it difficult to test for an object's creation in Python, particularly if the object being tested depends on the default way that Python represents objects (i.e., as strings). Moreover, the current fix for this issue has its own limitations.

It's recommended that you use doctest for smaller projects and scripts that require brief verification rather than intense testing. It's also a suitable choice for testing functional programming, where code expressions don't cause any unintended changes.

doctest is also a good choice when you quickly need to verify your code without undergoing any additional setup or configuration since doctest is built into Python's standard library. Additionally, doctest's ability to embed tests directly into code documentation makes it easy to create self-documenting code and to keep your tests in sync whenever you make changes.

In this article, you learned about various Python test automation frameworks, including pytest, Robot, behave, PyUnit, and doctest. Selecting the appropriate testing framework requires careful consideration. Factors such as script quality, test-case definition, and module execution methods should be considered.

Additionally, integrating a cloud-based solution like Sauce Labs into your testing process can enhance the efficiency of your tests. With its comprehensive platform for cross-platform and cross-browser testing, Sauce Labs offers users the ability to run parallel tests across a vast range of browsers and devices. This capability reduces the time it takes to complete testing.