Top 8 Python Testing Frameworks

This article will let you know about the Top 8 Python Testing Frameworks.

Python is the preferred programming language (though not that far from the all-time-favorite Java that came second with 32 percent).

Through the years, Python has indeed been growing in popularity among developers and testers. It is the fastest-growing programming language there (as we can see from the StackOverflow Trends Chart below) We all know the meaning of that… Popularity equals relevance!

In the course in this post, my intention is to show you what a powerful Python programming language can be, and also which Python testing frameworks will be best suited for your specific project requirements.

What’s So Great About Python for Test Automation?

Python is an open-source programming language that was first introduced in 1991 to create a natural simple, user-friendly beautiful, and less cluttered programming language. When it comes to testing, these qualities are crucial, particularly when you have manual testers within the team (that do not necessarily have any programming experience) that are now moving to automation and are able to benefit from Python’s speedy learning curve to write scripts in a short time.

There are a variety of reasons why Python’s popularity has been increasing in the area of test automation and the reason it’s regarded among the top alternatives for automation of tests. There are many reasons to see the Zen of Python (19 guiding principles that guide the design philosophy of Python) It is a great choice for beginners but powerful enough for professionals It’s functional and object-oriented, it comes with a vast library of test tools as well as a global community, and more!

The Right Python Testing Frameworks For You

With the growing usage of Python and Python-based tools, the demand for testing frameworks that are based on Python is growing as well. It can be quite confusing to know which one to choose of the many tools available since each has its own pros and pros. In reality, each company or project has its own needs and restrictions, so we must take into consideration each one when choosing one that is the most appropriate for us. I suggest reading these excellent articles to help you choose the appropriate tool for your needs:

1. Selecting The Right Test Automation Tool
2. Selecting the Best Tool – Research Process
3. Selecting the Right Functional Testing Tools

Of course, why are pro and con lists exist in the first place, other than to help us gain additional information about the tools we love, all in one location! Let’s dive into the most popular eight Python testing frameworks available and look at their strengths and weaknesses to be a step closer to choosing the best Python testing frameworks that meet your requirements.

Advantages And Disadvantages of Top 8 Python Testing Frameworks

1. Robot Framework
2. Pytest
3. TestProject
4. PyUnit (Unittest)
5. Nose2
6. Behave
7. Lettuce
8. Testify
9. Conclusion

1. Robot Framework (RF)

Robot Framework (RF) is an open-source test automation python testing framework that are used for acceptance testing and testing-driven acceptance (ATDD) and robot process automation (RPA). The core of the framework is written in Python and can be used using Jython (Java application of Python) as well as IronPython (Python for the .NET framework). To run it, you’ll require installing Python versions 2.7.14 or greater.

Advantages

• Based on the Keyword driven testing (KDT) approach, making it easy to develop test cases that can be read by humans (no need for coding expertise).
• is compatible with every operating system (Windows, Linux, or MacOS) and all apps (web mobile, desktop, and mobile applications).
• It provides clear and easy-to-use HTML reports (including images).
• Rich ecosystem with number of APIs, making it an extremely flexible framework that allows you integration with other third-party software.
• supports If/Else syntax, starting with RF v4.0
• Excellent Community support as well as online sources.

Disadvantages

• Parallel testing isn’t implemented in the default however it is accomplished using Selenium Grid or Pabot (a Parallel Executor that works with RF).
• Both for good and bad, it requires users to adhere to a certain method, the initial learning curve may be longer than the normal for newbies.
• Making generic keywords can take more time than just creating coded tests
• It is not easy to modify your reports.

The bottom line is: If you’re looking to establish a framework that relies on keywords that allow manual testers and business analysts to create automated testing, RF is the solution for you. It offers an array of extensions and libraries that are simple to use. But, be careful if you’re seeking to develop complex scenarios, then you’ll have to create certain modifications that aren’t included in the framework.

2. Pytest

Pytest is an open-source Python testing frameworks that could be one of the most popular Python testing frameworks on the market. Pytest can be used for functional testing, unit testing, API testing, and more as well. To run it, you’ll require Python Version 3.5 or greater.

Pros

• permits simple and compact testing suites.
• is highly extensible the use of plugins such as the following: pytest-randomly, pytest django, pytest.
• You can also include the an html pytest plugin to your application to print HTML reports by using just one command-line option.
• It is possible to run tests in parallel with a Pytest plugin called pytestxdist. It is possible to learn more here.
• is a huge community.
• Fixtures support HTML0 and allow you cover all parameters without having to rewrite tests, and provide a wonderful method of managing information between the stages.

Cons

• Compatibility isn’t the most important thing for Pytest even though you can create test cases using Pytest but you won’t be able to utilize them in other testing frameworks due to the use of its unique routines.

The bottom line: If you’re looking to develop small, unit tests and compact tests that allow for complex scenarios, this fully-featured framework is the one for you.

3. TestProject

TestProject is a 100% free complete automation framework that includes both local and cloud-based HTML reports. Utilizing TestProject allows you to easily build test automation for web, mobile, or general purposes using the Python open-source SDK. It is compatible with Python version 3.6 or greater and is compatible with the Pytest as well as the Unittest frameworks. TestProject contains all dependencies needed to run the single executable for any platform.

Pros

• One Agent executable that contains all 3rd parties libraries required to run and create test automation for mobile, web , and general tests.
• FREE Automatic reports in HTML/PDF format (including screenshots).
• Execution history available through an RESTful API.
• Always up-to-date with latest stable Selenium/Appium driver version.
• One SDK that works for Web, Android, iOS and generic tests.
• Test runner built-in and capability to report.
• Support for cross-platforms such as Mac, Windows, Linux and Docker.
• Huge community, and help including blog, forum and live chat built-in.

Cons

• The agent runs tests one at a given time, which means to run parallel tests, you’ll have to make use of the Docker agent.
• Collaboration features in the team that are available in the hybrid cloud can be limited when working in offline mode. This means that, unlike the seamless collaboration that is available in the hybrid cloud, when working locally – using an “on-prem” option, you’ll need to set up the collaboration yourself and save your tests to the shared network drive/git.

The bottom line: If you’re in search of an all-encompassing framework that will cover all automation tasks from beginning to end, TestProject is certainly the best choice that is the perfect choice for teams with different abilities, from novices to seasoned automation experts.

4. PyUnit (Unittest)

PyUnit (Unittest) PyUnit (Unittest) is a unit-testing Python Testing Frameworks for Python that was in the spirit of JUnit. The default Python tests framework is included when you install the Python package and, consequently, the one that developers typically begin their tests using.

Pros

• Since it’s included in the Python library, there are no additional modules to install It is included straight out of the box in it. Python application.
• offers a an easy and flexible test case execution.

Cons

• The intention behind the test program can sometimes be unclear since it relies on abstraction.
• A lot in boilerplate programming is needed.
• Since it is based on Junit, the camelCase method of naming is used in place of Python’s snake_case name convention.

The bottom point: If you’re in the market for basic unit testing and are comfortable with xUnit you’ll be able to start with PyUnit which will likely be the most suitable one for you. There are no extra dependencies.

5. Nose2

Nose2 is the successor of Nose and is similar to PyUnit (Unittest) however with additional plugins. Nose2 enhances PyUnit beyond its functions with numerous plugins that offer support for tests, test execution fixtures, decorators parameterization, and more.

Pros

• Easy to start by extending to the PyUnit (Unittest) framework which is included from the box with Python libraries.
• It comes with a wide range of plug-ins built-in that will speed up your testing and make it more efficient.
• Allows parallel testing with the MP plugin.
• Automatically collects test results provided you adhere to a few simple rules to organize your library and tests.

Cons

• The lack of comprehensive documentation that could hold you back when you are just starting out.
• It is not maintained actively in comparison to other Python Testing Frameworks.

The bottom line: If you’re already using the PyUnit program, you may like to give Nose2 another try and see the ways that it could extend PyUnit.

6. Behave

Behave is among the most well-known Python BDD (behavior-driven development) testing frameworks. While it’s not officially a part of Cucumber, it is a part of the Cucumber initiative, Behave works exactly like Cucumber frameworks.

Pros

• Allows you to write tests in a readable language which allows for an easy collaboration between teams that have similar capabilities.
• A vast amount of documentation and support is available to assist in getting started.
• fully supports Gherkin language, so creating feature files do take no expertise.
• Has Django along with Flask Integrations.

Cons

• There isn’t option to run parallel operations.
• Only for testing in black boxes.

Bottom note: If your company is using the BDD method, and you already have BDD experience (such as Cucumber, SpecFlow, etc) and you are in the market for black-box testing, you must definitely look into Behave. It is also worth exploring different Python BDD frameworks such as Pytest-added Lettuce (to be discussed in the following paragraphs), Radish, and more in this Python BDD Python testing frameworks comparison article. If you require more than just black-box testing, then take a look at other alternatives.

7. Lettuce

Lettuce is yet another Python BDD framework and it is an extension of Cucumber. It requires Python 2.7.14 or greater.

Pros

• supports Gherkin language, which allows even members of teams that are not technical to create tests with ease with native language.
• Like Behave It is utilized predominantly for black-box testing however it can also be used for other kinds of testing. For example, Lettuce can test various behavior of databases and servers as well as interactions.

Cons

• isn’t as rich in advantages offered by other Python Testing Frameworks making it better suited for smaller projects.
• The documentation and support for it don’t seem to be kept up to date.
• It requires a dedicated communications among all the stakeholders in the project, including developers management, QA and dev to ensure that the implementation goes smoothly.

The bottom line: If you are working on a smaller BDD initiative, Lettuce is a great choice for simple, natural language testing among everyone in the team.

8. Testify

Testify was designed to replace the popular Unittest along with the Nose frameworks. It also has more enhanced features compared to the traditional Unittest.

Pros

• Useful to test unit functionality Integration testing, unit testing, and testing systems.
• For those familiar with Unittest Testify is simple to begin with.
• Has extensive plugins.
• Like Nose2, Testify enables test discovery.
• Simple syntax for fix method.

Cons

• The lack of extensive documentation, so beginners may have to look for the right sources.
• Parallel testing isn’t difficult to attain.

The bottom line is: If you’ve had previous experience using Unittest you’ll find it fairly simple for you to adapt your tests that you have already created to use Testify and it’s certainly worth looking into.

Conclusion

We’re at the conclusion of this Python testing frameworks comparison list and now you can select the one that best is most suitable for your needs.

Are you in search of more of a BDD approach? Is it unit testing that you want to achieve or more functional testing? Does your team have any technical or programming experience or does it consist of novices? These questions and many more are the ones you have to consider when making a choice. Each of these frameworks offers benefits and drawbacks and there isn’t a way to tell if it is either good or evil, just appropriate. It is suitable for your personal requirements and needs for your product.

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