5 Best Python Testing Frameworks
In this article, readers will find an honest comparison of the top 5 Python frameworks for test automation. Discover all their advantages and disadvantages.
Join the DZone community and get the full member experience.Join For Free
After being voted as the best programming language in the year 2018, Python still continues rising up the charts and currently ranks as the third best programming language just after Java and C, as per the index published by Tiobe. With the increasing use of this language, the popularity of test automation frameworks based on Python is increasing as well. Obviously, developers and testers will get a little bit confused when it comes to choosing the best framework for their project. While choosing one, you should judge a lot of things, the script quality of the framework, test case simplicity and the technique to run the modules and find out their weaknesses. This is my attempt to help you compare the top five Python frameworks for test automation in 2019, and their advantages and disadvantages over the other frameworks. So you could choose the ideal Python framework for test automation according to your needs.
Used mostly for development that is acceptance test-driven as well as for acceptance testing, the Robot Framework is one of the top Python test frameworks. Although it is developed using Python, it can also run on IronPython, which is .net-based and on Java-based Jython. Robot as a Python framework is compatible across all platforms—Windows, MacOS, or Linux.
- First of all, you will be able to use Robot Framework (RF), only when you have Python 2.7.14 or any version above it installed. Although, Python 3.6.4 is also used, code snippets provided in the official blog of RF will make sure the appropriate notes are added, consisting of all the changes required.
- You will also need to install “pip” or Python package manager.
- Finally, a development framework is a must to download. A popular framework among developers is the PyCharm Community Edition. However, since code snippets are not IDE-dependent, you can use any IDE, which you have worked on earlier.
Advantages and Disadvantages of Robot
Let’s take a look at the advantages and disadvantages of Robot as a test automation framework over other Python frameworks:
- Using a keyword-driven-test approach, it makes the automation process simpler by helping the testers easily create readable test cases.
- Test data syntax can be used easily.
- Consisting of generic tools and test libraries, it has a vast ecosystem where individual elements can be used in separate projects.
- The framework is highly extensible since it has many APIs.
- The Robot framework helps you run parallel tests via a Selenium Grid; however, this feature is not built in.
- The Robot framework although is tricky when it comes to creating customized HTML reports. However, you could still present xUnit formatted short reports by using the Robot framework.
- Another flaw of the Robot framework is the inadequacy of parallel testing.
Is Robot the Top Python Test Framework for You?
If you are a beginner in the automation domain and have less experience in development, using Robot as a top Python test framework is easier to use than Pytest or Pyunit, since it has rich built libraries and involves using an easier test-oriented DSL. However, if you want to develop a complex automation framework, it is better to switch to Pytest or any other framework involving Python code.
Used for all kinds of software testing, Pytest is another top Python test framework for test automation. Being open source and easy to learn, the tool can be used by QA teams, development teams, individual practice groups, and in open source projects. Because of its useful features, like “assert rewriting,” most projects on the internet, including big shots like Dropbox and Mozilla, have switched from unittest (Pyunit) to Pytest. Let’s take a deep dive and find out what’s so special about this Python framework.
Apart from a working knowledge in Python, Pytest does not need anything complex. All you need is a working desktop that has:
- A command line interface
- Python package manager
- IDE for development
Advantages and Disadvantages of Pytest
- In the Python testing community, before the arrival of Pytest, developers included their tests inside large classes. However, a revolution was brought by Pytest since it made it possible to write test suites in a very compact manner than before.
- Other testing tools require the developer, or tester, to use a debugger or check the logs and detect where a certain value is coming from. Pytest helps you write test cases in a way that gives you the ability to store all values inside the test cases and inform you which value failed and which value is asserted.
- The tests are easier to write and understand since the boilerplate code is not needed that much.
- Fixtures are functions you can use by adding an argument to your test function. Their job is to return values. In Pytest, you can make them modular by using one fixture from another. Using multiple fixtures helps you cover all the parameter combinations without rewriting test cases.
- Developers of Pytest released some useful plugins that make the framework extensible. For example,
pytest-xdistcan be used to execute parallel testing without using a different test runner. Unit tests can also be parameterized without duplicating any code.
- Provides developers with certain special routines that make test case writing simpler and less prone to errors. The code also becomes shorter and easily understandable.
The fact that special routines are used by Pytest means you have to compromise with compatibility. You will be able to conveniently write test cases but won’t be able to use those test cases with any other testing framework.
Is Pytest the Top Python Test Framework for You?
Well, you have to start by learning a full-fledged language, but once you get the hang of it, you will get all the features like static code analysis, support for multiple IDE, and, most importantly, writing effective test cases. For writing functional test cases and developing a complex framework, it is better than unittest but its advantage is somewhat similar to the Robot framework if your aim is to develop a simple framework.
Unittest, or PyUnit, is the standard test automation framework for unit testing that comes with Python. It’s highly inspired by JUnit. The assertion methods and all the cleanup and setup routines are provided by the base class TestCase. The name of each and every method in the subclass of TestCase starts with “test.” This allows them to run as test cases. You can use the load methods and TestSuite class to the group and load the tests. Together, you can use them to build customized test runners. Like Selenium testing with JUnit, unittest also has the ability to use
unittest-sml-reporting and generate XML reports.
There are no such prerequisites since unittest comes by default with Python. To use it, you will need standard knowledge of the Python framework and if you want to install additional modules, you will need pip installed along with an IDE for development.
Advantages and Disadvantages of PyUnit
Being part of the standard library of Python, there are several advantages of using Unittest:
- The developers are not required to install any additional module since it comes with the box.
- Unittest is xUnit’s derivative and its working principle is similar to other xUnit frameworks. People who do not have a strong background in Python generally find it comfortable to work.
- You can run individual test cases in a simpler manner. All you need to do is specify the names on the terminal. The output is concise as well, making the framework flexible when it comes to executing test cases.
- The test reports are generated within milliseconds.
snake_caseis used for naming Python codes. But, since this framework is inspired a lot from Junit, the traditional
camelCasenaming method persists. This can be quite confusing.
- The intent of the test code sometimes become unclear, since it supports abstraction too much.
- A huge amount of boilerplate code is required.
Is PyUnit the Top Python Test Framework for You?
As per my personal opinion and the opinion of other Python developers, Pytest introduced certain idioms, which allowed testers to write better automation code in a very compact manner. Although unittest comes as a default test automation framework, the fact that its working principle and naming conventions are a bit different than standard Python codes, and its requirement of too many boilerplate codes, make it a not so preferred Python test automation framework.
We are all aware of behavior driven development, the latest agile-based software development methodology that encourages developers, business participants, and quality analysts to collaborate among each other. Behave is another one of the top Python test frameworks that allows the team to execute BDD testing without any complications. The nature of this framework is quite similar to SpecFlow and Cucumber for automation testing. Test cases are written in a simple readable language and later stuck to the code during execution. The behavior is designed by the behavior specs and the steps are then reused by other test scenarios.
Anyone with basic knowledge in Python should be able to use Behave. Let’s take a look at the prerequisites:
- Before installing Behave, you have to install any version of Python above 2.7.14.
- Python package manager, or pip, is required to work with Behave.
- A development environment is the last and most important thing you need. You can use Pycharm, which is preferred by most developers, or any other IDE of your choice.
Advantages and Disadvantages of Behave
Like all other behavior in driven test frameworks, the opinion regarding Behave’s advantage varies from person to person. Let’s take a look at the common pros and cons of using Behave:
- System behavior is expressed by semi-formal language and a domain vocabulary that keeps the behavior consistent in the organization.
- Dev teams who are working on different modules with similar features are properly coordinated.
- Building blocks are always ready for executing all kinds of test cases.
- Reasoning and thinking are featured in details, resulting in better product specs.
- Stakeholders or managers have a better clarity regarding the output of QAs and devs because of the similar format of the specs.
The only disadvantage is that it works well only for black box testing.
Is Behave the Top Python Test Framework for You?
Well, as we said, Behave (Python framework) works best only for black box testing. Web testing is a great example since use cases can be described in plain language. However, for integration testing or unit testing, Behave is not a good choice since the verbosity will only cause complications for complex test scenarios. Developers, as well as testers, recommend
pytest-bdd. It is an alternative to Behave since it uses all that is good in Pytest and implements it for testing a behavior driven scenario.
Lettuce is another simple and easy to use behavior driven automation tool based on Cucumber and Python. The main objective of Lettuce is to focus on the common tasks of behavior driven development, making the process simpler and entertaining.
You will need, at minimum, Python 2.7.14 installed along with an IDE. You can use Pycharm or any other IDE of your choice. Also, for running tests, you will be required to install the Python package manager.
Advantages and Disadvantages of Lettuce
- Well, just like any other BDD testing framework, Lettuce enables developers to create more than one scenario and describe the features in the simple natural language.
- Dev and QA teams are properly coordinated since the specs are of similar format.
- For black box testing, Lettuce is quite useful for running behavior driven tests cases.
There is only one disadvantage of using Lettuce as a Python framework. For successful execution of behavior driven tests, communication is necessary between dev teams, QA, and stakeholders. Absence or communication gap will make the process ambiguous and questions can be raised from any team.
Is Lettuce the Top Python Test Framework for You?
According to developers and automation testers, Cucumber is more useful when it comes to executing BDD tests. However, if we are talking about Python developers and QA, there is no better replacement than
pytest-bdd. All the great features of Pytest, like compactness and easy to understand code, are implemented in this framework combined with the verbosity of behavior driven testing.
In the above article, we have discussed the top five Python frameworks for test automation in 2019, based on different testing procedures. While Pytest, Robot framework, and unittest are meant for functional and unit testing, Lettuce and Behave are best for behavior driven testing only.
From the features stated, we can conclude for functional testing, Pytest is the best. But if you are new to Python-based automation testing, the Robot framework is a great tool to get started. Although the features are limited, it will enable you to get ahead on the track easily. For Python-based BDD testing, Lettuce and Behave are equally good, but if you already have experience with Pytest, it’s better to use
I hope my article helps you make the right choice out of the top Python test frameworks for your Python web automation needs. Happy testing!
Published at DZone with permission of Arnab Roy. See the original article here.
Opinions expressed by DZone contributors are their own.
How to Use an Anti-Corruption Layer Pattern for Improved Microservices Communication
How To Use an Automatic Sequence Diagram Generator
Performance Comparison — Thread Pool vs. Virtual Threads (Project Loom) In Spring Boot Applications
An Overview of Cloud Cryptography