Python automation testing frameworks are widely used, and their usage is increasing rapidly. Python is the language that people worldwide want to learn most, according to a recent survey. Python stood third among the most popular programming languages, and it is anticipated that it will soon take the top spot.
Python can be customized to fit a variety of business use cases, but testing your code is essential before making any Python-based products public.
This article will explain Python testing and go over the top six Python testing frameworks. By the conclusion, you’ll be able to identify the ideal testing framework for your particular project.
What Is Python?
The conventional description of Python is that it is an interpreted, high-level, generic programming language that aids developers in creating logical and manageable code for both small and large-scale projects. A user-friendly language is Python. Because of this, creating test cases is not too difficult. Thus, there is a meaningful demand for efficient, user-friendly Python testing frameworks.
Some advantages of pythons include
- The absence of compilation causes.
- Simple bug fixing
- Broad support library
- Learnable data structure
- Extremely productive
- Group cooperation
- The interpreter reads the source file’s python code and checks it for syntax errors.
- If there are no errors in the code, the interpreter converts it to “byte code,” which is its equivalent.
- The Python Virtual Machine (PVM) then receives this byte code and compiles it once more, checking it for errors if there are any.
What Is Python Testing?
In testing, automated testing is a well-known context. In this scenario, a script rather than a human is used to carry out the test plans. Python has the tools and modules needed to facilitate automated system testing.
Writing test cases in Python is comparatively simple. Python-based test automation frameworks are rising in popularity along with the use of Python.
A Python Testing Framework: What Is It?
Python is well renowned for its simplicity of use in web development and test automation, and the Python testing framework is a dynamic framework built on Python. Excellent testing tools are therefore required as a result of these changes throughout time. Automated testing is supported by a number of Python frameworks and packages.
According to the Developer Survey by StackOverflow, Python will be the programming language with the fastest growth in 2020. Python is the third most popular language overall. The widespread support of test automation frameworks is one of the many factors contributing to Python’s success. The majority of well-known Python frameworks can be utilized for Selenium Python testing and cross-browser testing because they are compatible with the Selenium test automation framework.
2020 will see an increase in the number of Python automation testing frameworks, making it vital to select the best Selenium test automation framework for your needs. There are numerous other Python frameworks available, in addition to unittest (or PyUnit), which is the standard Python testing framework in 2020.
Let’s examine a list of the most popular Python testing frameworks and weigh their advantages and disadvantages to determine when to use which:
- Behave Framework
One of the popular BDD (Business Driven Development) testing frameworks for Selenium Python is called Behave. Gherkin is the language used by Behave to create scenarios and feature files. Team members who are not technically savvy can write tests because Gherkin uses an easy-to-read language for test case development, facilitating Business Driven Development. Using a BDD framework facilitates improved teamwork because engineers, managers, quality analysts, business development managers, and others work more closely together. If you are familiar with other behavior-driven development (BDD) frameworks like SpecFlow, Cucumber, Cucumber-JVM, etc., it is relatively simple to get started with a behavior-driven Python testing framework like Behave.
Behave differs from existing Selenium Python testing frameworks like unittest (or PyUnit), PyTest, etc. because it is a BDD framework. Behave’s most recent release is 1.2.7.dev1.
This framework and Cucumber are fairly similar. All test scripts are created in a straightforward language and are then added to the running code. Relevant specs determine code behaviour. Behave enables the reuse of previously defined steps in other use case scenarios.
Behave’s prerequisites are:
Anyone who has a working knowledge of Python can use behave. Do the following before installing Behave:
- Install Python 2.7.14 or a later version.
- Install pip or another Python package manager.
- Set up Pycharm or a comparable IDE.
Benefits of Behave
The benefits of adopting the Behave framework for test automation are:
- If the team has prior expertise with any existing BDD framework, such as SpecFlow, Cucumber, and so forth, ramp-up is simple.
- Easy setup and cleanup are made possible by the presence of environmental functions, configuration settings, fixtures, and so forth
- Behaviour is supported by PyCharm (Professional Edition).
- It allows for integration with other web frameworks like Flask and Django.
- It has excellent tutorials and support materials that can get started with the behave framework.
- Since tests contain a variety of scenario steps, scenario outcomes, feature files, and other components, adding fresh test cases is easier.
- Non-technical team members can be just as important to the development of test cases (or test scenarios) as technical ones.
- PyCharm does not support it (Community Edition).
- Sharing scenario steps (*_steps.py) between distinct feature files (*.feature) in Behave is really challenging.
- One of the key prerequisites for automated browser testing is parallel test execution, which is not supported by built-in functionality. The installation of additional software is necessary for workarounds for simultaneous test running, as described in two well-liked discussion threads here and here.
- Behave is restricted to usage in black-box testing.
- Deprecated is the plugin behave-parallel that made behave’s support for parallel test execution possible.
Is the Python testing framework Behave the best choice for you?
The Behave framework is appropriate for web testing since test scenarios can be created using straightforward English. However, behave is not the best choice for integration testing because its verbosity can result in unnecessarily complicated test scenarios.
2. Lettuce Framework
A Python testing framework called Lettuce makes BDD testing easier. It finds inspiration in cucumbers (that was designed for Ruby). Plants influence lettuce’s album names. Lettuce’s most recent version, 0.2.23, is known as Kryptonite.
For automating behavior-driven test frameworks, Lettuce Framework also excels. Python and Cucumber are the foundation for its operation. Lettuce is mostly useful for making it simpler to complete the typical duties of a BDD structure. This specific Python testing framework is hosted on GitHub and is open-source. Lettuce was created, as stated on their official website, to make testing simple and entertaining.
Lettuce need the following:
- Perform the following before to installing Lettuce:
- Set up Python 2.7.14 or a later version.
- Install Pycharm or a similar IDE.
- Install the package manager for Python.
Benefits of Lettuce
The Lettuce framework has the following benefits:
- Lettuce employs the Gherkin language to create scenarios and feature files, just like other Python testing frameworks like Behave that support BDD. If you’ve used a BDD testing tool in the past, using Lettuce will be simple for you.
- Other project stakeholders, such as marketing professionals, project managers, etc., can also play a significant role in the test process besides developers and other technical staff.
- It is simple to create new test cases because feature files and scenario files need to be updated regularly.
The Lettuce framework has several serious drawbacks, which are listed below:
- It is mostly employed for testing black boxes.
- Proper communication between each project stakeholder is necessary for flawless execution utilizing a BDD testing tool like Lettuce.
- Which Python automation framework, Lettuce, is best for you?
- Lettuce streamlines, scales, and makes the Selenium test automation process readable and simple. The best benefit of adopting a BDD tool like Lettuce is that since tests are created from business requirements, non-technical individuals may also take part in the process. Python programmers and testers are more likely to choose pytest-bdd because it supports the bulk of the capabilities offered by other BDD tools.
3. Robot Framework
Robot is a different popular Python testing framework that is utilized for ATDD, RPA, and Selenium test automation (Acceptance Test Driven Development). It is a Python testing framework that is free and expandable. Because it is simple to interface with other tools, it is perfect for developing adaptable automated testing solutions.
The Python testing framework is independent of operating systems and applications, meaning the same implementation works on Windows, Linux, and macOS. Since the Robot framework uses human-readable keywords, its syntax is simple to comprehend. Python-based Selenium libraries can expand the capabilities of the Robot framework. It has a robust ecosystem made up of tools and libraries created for different projects.
Acceptance testing is appropriate for this method. Although it was created in Python, it can also function on IronPython (a.net-based Python) and Jython (Java-based). Linux, macOS, and Windows are all compatible with the Robot Framework.
Prerequisites For Robot Framework:
Do the following before installing Robot Framework:
- Set up Python 2.7.14 or a later version.
- Install the package manager for Python (pip)
- Install a development framework, like Pycharm Community Edition, on your computer.
Principal Advantages of Robot Framework
- RF is built on keyword-driven testing, which simplifies automation by assisting testers in producing understandable test cases.
- enables simple test data syntax usage
- supports all application kinds, including online and mobile apps, across all operating systems (macOS, Windows, Linux).
- Easily comprehensible report data
- It is highly extensible and simple to integrate with third-party tools thanks to its numerous APIs and robust ecosystem.
- It comes with a tonne of general tools and test libraries, they can use independently all of which in different projects.
- excellent community backing
Negative aspects of the robot framework
- There is no default support for parallel tests. However, Selenium Grid can help you accomplish this.
- While producing customized HTML reports can be challenging, it is easy to use. It does, however, permit the presentation of brief reports in xUnit format.
- For beginners who must learn to work within the RF’s predefined method require a slightly longer learning curve.
- Generic keyword generation takes more time than coding tests.
- Customizing reports is quite challenging.
Is Robot Your Favorite Python Testing Framework?
When compared to PyUnit (or unittest) and PyTest, the Robot framework is simpler to use because of its extensive library set and simple syntax. If we’re looking for the best Python automation framework for ATDD, we should pick Robot.
4. Pytest Framework
One of the most widely used Python testing frameworks is called Pytest. It is a testing framework that is open-source. Unit testing, functional testing, and API tests are all supported by Pytest.
Pytest Framework Prerequisites:
Unit testing is the main use of the free and open-source Python web automation library PyTest. In 2020, this specific Python testing framework will be scalable since it can create both straightforward automated tests and intricate functional tests for libraries and apps. With PyTest, getting started is simple. It works with PyPy 3 and Python 3.5+.
Because less boilerplate code is needed and there is no obligation to incorporate tests in substantial test classes, test suites created with PyTest are smaller. There is no set test case nomenclature in PyTest, in contrast to the default Python testing framework 2020, which requires tests to begin with test_ (or end with _test). This is so that test modules and functions can be automatically discovered, thanks to PyTest’s built-in functionality.
Principal Advantages of Pytest Framework
- offers a limited and straightforward test suite.
- The Pytest HTML plugin, for example, is very extendable and can be added to your project to produce HTML reports with only one command-line argument.
- The community supports it.
- Covering all parameter combinations without having to write new test cases is beneficial.
Pitfalls of the Pytest Framework
Compatibility is prohibited by Pytest’s proprietary routines. This implies that even though test cases can be written quickly in this framework, they cannot be used in other testing frameworks.
Nose2, a Python unit testing framework replaced the Nose framework. It is also referred to as an extension of unittest because it is based on the unittest. By offering plugins for test execution, enabling the automatic discovery of test cases, and gathering documentation, Nose2 enhances the PyUnit framework’s support.
With all these new features, testing with PyUnit is made simple and straightforward. Decorators, fixtures, parameterized testing, modules, classes, and test discovery are all supported by Nose2’s plugin ecosystem. A DSL (Domain-Specific Language), often known as a functional test, is used in Nose2 to write tests with pricey, nested features. The official website of Such provides additional information about Such in Nose2.
Benefits of Nose2
The Nose2 Python Testing Framework has the following benefits:
- Nose2 enhances the existing unittest framework, making it simple to install and get going.
- Nose2 makes unittest as helpful as PyTest with capabilities like test discovery, fixtures, parameterized testing, etc.
- Nose2 scripts offer a wide range of command-line arguments and comprehensive configuration using config files.
- Nose2’s mp plugin allows tests to run concurrently across several processes. If your tests are heavily CPU or IO bound, this expedites test execution.
- Many Nose2 plugins are available to speed up the Selenium test automation process.
Issues with Nose2
The following are a few of the Nose2 Python Testing Framework’s minor drawbacks:
- Nose2’s documentation is not as comprehensive as that of the PyTest framework.
- For programmers who desire to use Nose2 in their applications, the deprecation of the nose framework, which was supported on Python 2.4 (and above), could be a deterrent.
- Is Nose2 Your Favorite Python Testing Framework?
- It is worthwhile to try Nose2 as it extends unittest if your team members are currently using the unittest framework and do not have any plans to switch to any other framework (such as Lettuce, Behave, PyTest, etc.).
Nose2 can be used for automated browser testing scenarios where test scenarios run on various platform & browser combinations because it also supports parallelized testing.
6. PyUnit (Unittest) Framework
Framework for unit tests A unit testing framework modelled after JUnit is PyUnit, also referred to as Unittest. This is the standard Python testing framework that is included by default with the Python package. As a result, here is where most developers begin their testing.
Since introducing Python version 2.1, t has been shipped as a component of the unittest module.
Many developers who are starting with Selenium test automation prefer the unittest Python testing framework because it is included out of the box. Test cases are organised according to a specific nomenclature, where tests begin with test or end with test.
The assertion methods, SetUp, and TearDown procedures are all found in the base class called TestCase. It is possible to create custom test runners by grouping several testcases into a class called TestSuite.
What Conditions Must Be Met?
The Python testing framework includes the PyUnit framework out of the box, therefore installing extra packages is unnecessary.
The PyTest framework has a variety of benefits, the most significant of which are listed below:
- It is suitable for use with many Python testing frameworks, including unittest (or PyUnit).
- PyTest can create complicated functional test cases besides simple tests.
- Existing implementations that use other Python testing frameworks can be more easily ported to PyTest.
- Both open-source and TDD (Test Driven Development) projects can use PyTest.
- Because of its support for parameterization, PyTest is essential for developing efficient test cases (and test suites). With the use of parameterization, test cases may be run with many input settings, resulting in less code duplication.
- PyTest tests have very little boilerplate code, making them simple to understand.
- Because of its support for fixtures and classes, common test objects are accessible throughout the lifetime of a module, class, function, or session.
- The Python testing framework already has a robust plugin architecture, and PyTest is extendable. With PyTest, there are currently over 315 external plugins.
- The pytest-xdist plugin facilitates the running of tests in parallel.
- In PyTest, assertions give specific information about the failure circumstances.
Cons Of PyTest As A Testing Framework For Python
- Because PyTest employs its own unique development procedures, it is incompatible with other Selenium Python testing frameworks. The only way to move an existing PyTest implementation to another Python testing framework is to rewrite the implementation completely.
The PyUnit Framework Has The Following Significant Drawbacks:
- Testcase names continue to follow Java’s camelCase naming pattern.
- For test implementation, a lot of boilerplate code is utilized.
- The output of color is not supported.
Is PyUnit Your Best Option For A Python Automation Framework?
Most developers begin their journey toward automating testing with Python with PyUnit because it is the Selenium Python testing framework that is shipped by default with Python (also known as unittest). The support for too much abstraction and the abundance of boilerplate code significantly hampered using the Python Testing Framework for large projects.
Cloud-based testing platforms like LambdaTest offers online Selenium Grid infrastructure allows you to perform Python testing on 3000+ real browsers and operating systems. The major advantage of using a reliable online Selenium Grid infrastructure like LambdaTest is that it doesn’t require additional software (or plugin) installations on your machine. The browsers on VMs in LambdaTest have pre-installed versions of Adobe Flash, Adobe Shockwave, Adobe Reader, Microsoft Silverlight, etc., making it easy to test rich internet applications.
In this article, we looked at a few of the well-liked Python testing frameworks that Selenium can interact with. Each of these frameworks has a unique set of benefits and drawbacks. Try BDD tools like Behave or Lettuce if you want to involve non-technical members in the test creation process. In conclusion, selecting the best Python testing framework only depends on the project requirements and the team’s expertise.