Five excellent Python testing frameworks

In this article, readers will find a real-life comparison of the top 5 Python frameworks for test automation. Discover all their pros and cons.

After being named the best programming language of 2018, Python continues to rise in the rankings and is currently ranked third, behind Java and C, according to the Index published by Tiobe . Python-based test automation frameworks are also growing in popularity as the use of this language increases. Apparently, developers and testers get a little confused when it comes to choosing the best framework for their projects. While choosing one, you should judge a lot of things, the scripting quality of the framework, the simplicity of the test cases and the technique of running the modules and finding out their weaknesses. This is my attempt to help you compare the top five Python frameworks for test automation in 2019, and their pros and cons compared to other frameworks. So, you can choose the ideal Python test automation framework as per your requirement.

Robot Framework

Robot Framework is mainly used for acceptance test-driven development and acceptance testing. It is one of the top Python testing frameworks. Although it was developed in Python, it also runs on .net-based IronPython and Java-based Jython. Robot as a Python framework is compatible with all platforms – Windows, MacOS or Linux.

Prerequisites

• First of all, you can use Robot Framework (RF) only if you have Python 2.7.14 or higher installed. Although Python 3.6.4 is also used, the code snippets provided on the official RF blog will ensure that appropriate comments are added, including all required changes.
• You will also need to install "pip" or the Python package manager.
• Finally, a development framework must be downloaded. PyCharm Community Edition is a popular framework among developers. However, because the code snippet is not IDE dependent, you can use any IDE you have used before.

Advantages and Disadvantages of Robot

Let’s take a look at the advantages and disadvantages of Robot as a test automation framework compared to other Python frameworks:

Advantages

• Use keyword-driven testing approach, which simplifies the automation process by helping testers easily create readable test cases.
• Test data syntax can be easily used.
• It consists of common tools and test libraries, with a large ecosystem where individual elements can be used in separate projects.
• The framework is highly extensible as it has many APIs.
• Robot framework helps you run parallel tests through Selenium Grid; however, this functionality is not built-in.

Disadvantages

• Robot framework is tricky though when it comes to creating custom HTML reports. However, you can still use the Robot framework to render short reports in xUnit format.
• Another flaw of the Robot framework is the lack of parallel testing.

Is Robot the best Python testing framework for you?

If you are a beginner in the field of automation and have less development experience, then using Robot as the top Python testing framework is better than Pytest or Pyunit is easier to use as it has rich built-in 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 that involves Python code.

pytest

Pytest is used for all kinds of software testing and is another top Python testing framework for test automation. The tool is open source and easy to learn, and can be used by QA teams, development teams, individual practice groups, and open source projects. Most projects on the internet, including big names like Dropbox and Mozilla, have switched from unittest (Pyunit) to Pytest due to its useful features like "Assertion Rewriting". Let’s take a deeper look at what makes this Python framework special.

Prerequisites

Pytest doesn’t require anything complicated other than a working knowledge of Python. All you need is a work desktop that has:

• Command Line Interface
• Python Package Manager
• Development IDE

Advantages and Disadvantages of Pytest

Advantages

• In the Python testing community, before Pytest existed, developers contained their tests in large categories. However, Pytest brought a revolution as it made it possible to write test suites in a more compact way than before.
• Other testing tools require developers or testers to use a debugger or inspect logs and detect the source of specific values. Pytest helps you write test cases, enables you to store all values ​​in the test case, and notifies you which value failed and which value was asserted.
• Tests are easier to write and understand because they don’t require as much boilerplate code.
• A fixture is a function that can be used by adding parameters to the test function. Their job is to return values. In Pytest you can make them modular by using a fixture. Using multiple fixtures helps you cover all parameter combinations without having to rewrite test cases.
• The developers of Pytest have released some useful plugins to make the framework extensible. For example, pytest-xdist can be used to perform parallel tests without using different test runners. Unit tests can also be parameterized without copying any code.
• Provide developers with certain special routines to make test case writing simpler and less error-prone. The code also becomes shorter and easier to understand.

Disadvantages

The fact that Pytest uses special routines means you have to compromise on compatibility. You will be able to write test cases conveniently but will not be able to use these test cases with any other testing framework.

Is Pytest the best Python testing framework for you?

Well, you have to start by learning a full-fledged language, but once you master it, you will get all the features, e.g. Static code analysis, support for multiple IDEs, and most importantly, writing effective test cases. It is better than unittest for writing functional test cases and developing complex frameworks, but if your goal is to develop a simple framework, its advantages are somewhat similar to Robot framework.

Unit Test (PyUnit)

Unittest, or PyUnit, is Python’s own standard test automation framework for unit testing. It is heavily inspired by JUnit. Assertion methods and all cleanup and setup routines are provided by the base class TestCase. The name of every method in a TestCase subclass begins with "test". This allows them to be run as test cases. You can use the load method and the TestSuite class to group and load tests. You can use them together to build a custom test runner. Like using JUnit for Selenium testing, unittest also has the ability to use and generate XML reports unittest-sml-reporting.

Prerequisites

There is no such prerequisite because unittest comes with Python by default. To use it, you need standard knowledge of Python frameworks, and if you want to install additional modules, you need to install pip as well as an IDE for development.

Advantages and disadvantages of PyUnit

Advantages

As part of the Python standard library, using Unittest has several advantages:

• Development One does not need to install any additional modules as it comes included.
• Unittest is a derivative of xUnit and works similarly to other xUnit frameworks. People without a strong Python background often find it comfortable to work with.
• You can run individual test cases in a simpler way. All you need to do is specify the name on the terminal. The output is also concise, giving the framework flexibility in executing test cases.
• Test reports are generated within milliseconds.

Disadvantages

• Normally, snake_case is used to name Python code. However, since this framework takes a lot of inspiration from Junit, the traditional camelCase naming method still exists. This can be very confusing.
• The intent of test code sometimes becomes unclear because it supports abstraction so much.
• Requires a lot of boilerplate code.

Is PyUnit the best Python testing framework for you?

In my personal opinion and the opinions of other Python developers, Pytest introduces certain idioms that allow testers Write better automation code in a very compact way. Although unittest appears as the default test automation framework, its working principles and naming convention are slightly different from standard Python code, and it requires too much boilerplate code, making it a less popular Python test automation framework.

Behave

We all know about behavior-driven development, the latest agile-based software development approach that encourages collaboration among developers, business actors, and quality analysts. Behave is another top Python testing framework that allows teams to perform BDD testing without any complications. The nature of this framework is very similar to SpecFlow and Cucumber for automated testing. Test cases are written in a simple and readable language and then fixed into the code during execution. Behaviors are designed from behavioral specifications, and these steps are then reused by other test scenarios.

Prerequisites

Anyone with basic knowledge of Python should be able to use Behave. Let’s take a look at the prerequisites:

• Before installing Behave, you must have any version of Python above 2.7.14 installed.
• Using Behave requires the Python package manager or pip.
• 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

As with all other behaviors in a driven testing framework, opinions on the advantages of Behave vary from person to person. Let's look at the common pros and cons of using Behave:

Pros

• System behavior is expressed by a semi-formal language and domain vocabulary, making behavior consistent across the organization.
• Development teams working on different modules with similar functionality are properly coordinated.
• Building blocks are ready to execute various test cases.
• Reasoning and thinking are reflected in the details, resulting in better product specifications.
• Due to the similar format of the specification, stakeholders or managers have better clarity on the output for QA and developers.

Disadvantages

The only disadvantage is that it only works for black box testing.

Is Behave the best Python testing framework for you?

Well, as we said, Behave (Python framework) is only suitable for black box testing. Web testing is a good example because use cases can be described in plain language. However, Behave is not a good choice for integration testing or unit testing, as the verbosity will only complicate complex test scenarios. Both developers and testers recommend pytest-bdd. It is an alternative to Behave as it uses all the advantages in Pytest and implements it to test behavior-driven scenarios.

Lettuce

Lettuce is another easy-to-use behavior-driven automation tool based on Cucumber and Python. The main goal of Lettuce is to focus on common tasks of behavior-driven development and make the process easier and fun.

Prerequisites

You need at least Python 2.7.14 and an IDE installed. You can use Pycharm or any other IDE of your choice. Additionally, in order to run tests, you will need to install the Python package manager.

Advantages and Disadvantages of Lettuce

Advantages

• Well, just like any other BDD testing framework, Lettuce enables developers to create multiple scenarios and natural language description function.
• Since the format of the specifications is similar, the development and QA teams are properly coordinated.
• For black-box testing, Lettuce is useful for running behavior-driven test cases.

Disadvantages

There is only one disadvantage to using Lettuce as a Python framework. To successfully execute behavior-driven testing, communication between the development team, QA, and stakeholders is necessary. Absence or miscommunication can make the process ambiguous and any team can ask questions.

Is Lettuce the best Python testing framework for you?

According to developers and automation testers, Cucumber is more useful when performing BDD testing. But if we talk about Python developers and QA, there is no one like 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.

Summary!

In the above article, we discussed the top five Python frameworks for test automation in 2019 based on different testing procedures. Pytest, Robot framework, and unittest are used for functional and unit testing, while Lettuce and Behave are only suitable for behavior-driven testing.

From the described features, we can conclude that for functional testing, Pytest is the best. However, if you are new to Python-based automated testing, the Robot framework is a great tool to get started. Although the functionality is limited, it allows you to easily lead on the track. For Python-based BDD testing, Lettuce and Behave are equally good, but if you already have experience with Pytest, it is better to use pytest-bdd.

I hope my article can help you learn from the top Python testing frameworks Make the right choice for your Python web automation needs. Happy testing!

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