Top 8 Python Testing Frameworks in 2023
By Shreya Bose & Aakash Makwana, Community Contributors - September 2, 2023
As per StackOverflow, Python is one of the most used programming languages in the world, whose popularity has increased over the last decade, as seen in the graph below. Python is a user-friendly language. Test cases are therefore relatively easy to write. Thus, effective, easy-to-use Python testing frameworks are in high demand.
This article lists eight python testing frameworks that are most suitable for quick and comprehensive automation of test cases written in Python. Before we begin.
What is a Python Testing Framework?
Python testing framework is a dynamic framework based on Python, which is well known for its ease of use in web development and test automation. These transitions over time also result in the need for excellent testing tools. There are several frameworks and libraries in Python that support automated testing.
Top Python Testing Frameworks
Here’s a list of top Python Testing Frameworks:
- Behave Framework
- Lettuce Framework
- Robot Framework
- Pytest Framework
- TestProject Framework
- PyUnit (Unittest) Framework
- Testify Framework
- Doctest Framework
Let’s go through the detailed of most preferred Python Testing Frameworks and understand their benefits and limitations that will help decide when to use which:
1. Behave Framework
All test scripts are written in a simple language and then attached to the code during execution. Code behavior is determined by relevant specs. Behave allows once-defined steps to be reused by other use case scenarios.
Prerequisites for Behave:
Behave can be used by anyone who possesses basic knowledge of Python. Before installing Behave, do the following:
- Install any version of Python over 2.7.14
- Install a Python package manager or pip
- Install Pycharm or a similar IDE.
Key Benefits of Behave:
- In Behave, system behavior is defined by semi-formal language and involves the use of a domain vocabulary that ensures behavior remains constant in the organization.
- There are building blocks available for the execution of a large variety of test cases.
- Facilitates effective coordination of development teams’ work on diverse modules with some similar features.
- All specs are in a similar format, offering managers better clarity on the output of developers and QAs.
Disadvantages of Behave:
- Works optimally only for black-box testing.
- Not the best fit for a unit or integration testing, as the verbosity inherent in these tests, can lead to complications in test scenarios.
BrowserStack provides integration with Behave to facilitate simpler test automation using Python & Selenium. To know more, view the documentation for running Selenium tests using Behave with Browserstack.
2. Lettuce Framework
Lettuce is also extremely effective when it comes to automating behavior-driven test structures. Its functioning is based on Cucumber and Python. Primarily, Lettuce is good for making the common tasks of a BDD structure easier to accomplish.
Prerequisites for Lettuce:
Before installing Lettuce, do the following:
- Install Python 2.7.14 or above
- Install Pycharm or an equally capable IDE
- Install the Python package manager
Key Benefits of Lettuce:
- Enables developers to program more than one scenario and describe its characteristics in a simple, natural language
- Enables, much like Behave, productive coordination due to specs being defined in a similar format
Disadvantages of Lettuce:
- Lettuce requires a highly refined system of communication between QAs, developers, and stakeholders in order to be truly functional as a Python testing framework. There is no room for ambiguity here.
As is the case with Behave, BrowserStack provides integration with Lettuce to facilitate simpler test automation using Python. View the relevant documentation here to learn more about automated testing with Lettuce and Python.
3. Robot Framework
This framework is largely suitable for acceptance testing. While it has been developed with Python, it is also capable of running on IronPython (.net based) as well as Jython (Java-based). The Robot Framework is compatible with Windows, macOS, or Linux.
Prerequisites for Robot Framework:
Before installing Robot Framework, do the following:
- Install Python 2.7.14 or above
- Install the Python package manager (pip)
- Download a development framework such as the Pycharm Community Edition
Key Benefits of Robot Framework:
- RF is based on keyword-driven testing, it makes automation simpler by helping testers easily create readable test cases
- Enables easy usage of test data syntax
- Supports all operating systems (macOS, Windows, Linux) and all application types such as web and mobile apps.
- Easy-to-understand report data
- Its many APIs and rich ecosystem make it highly extensible and easy to integrate with third-party tools.
- It is equipped with numerous generic tools and test libraries, each of which can be individually used in separate projects.
- Good community support.
Disadvantages of Robot Framework:
- There is no default parallel test support. However, you can achieve this with Selenium Grid.
- While it is largely convenient to use, some complications may arise when creating customized HTML reports. However, it does allow for the presentation of xUnit formatted short reports.
- Requires a bit longer learning curve for beginners who need to learn to work within the RF’s predefined methodology.
- Creating generic keywords takes longer than writing coded tests.
- Report customization is quite difficult.
4. Pytest Framework
Prerequisites for Pytest Framework:
- Install Python version 3.5 or above.
Key Benefits of Pytest Framework:
- Provides a compact and simple test suite.
- Highly extensible with many plugins available, such as the Pytest HTML plugin, which can be added to your project to print HTML reports with a single command-line option.
- It has large community support.
- It helps to cover all parameter combinations without rewriting test cases.
Disadvantages of Pytest Framework:
Pytest’s proprietary routines prevent compatibility. This means that though the test cases can be easily written in this framework, you won’t be able to use those in other testing frameworks.
5. TestProject Framework
TestProject is an open-source automation framework. It provides cloud and local HTML reports and easy test automation development. Supports the Pytest and Unittest frameworks and all required dependencies as part of a single executable cross-platform agent file.
Prerequisites for TestProject Framework:
- Install Python version 3.6 or above.
Key Benefits of TestProject Framework:
- Free automated reports in HTML/PDF format
- Easy access to execution history via RESTful API
- Always updated with the latest Selenium/Appium driver version
- Provides a single SDK for Android, iOS, web, and general testing
- Integrated test reporting capabilities
- Cross-platform support for all operating systems
- Extensive community and support.
Disadvantages of TestProject Framework:
- Requires support for parallel testing as only one test runs at a time.
- Team collaboration tools are limited in offline mode, and tests must be stored on a shared network drive/git when working in the field.
6. PyUnit (Unittest) Framework
PyUnit (also known as Unittest) is a JUnit-inspired unit testing framework. This is the default Python testing framework that comes with the Python package by default. Therefore, most developers start testing with this.
Key Benefits of PyUnit Framework:
- It doesn’t require the installation of additional modules as it is part of the standard Python library.
- Provides simple, clean and quick test case execution.
- Fast generation of test reports, including XML and Unittest SML reports.
Disadvantages of Unittest Framework:
- Test code supports abstractions that can obscure intent and make it difficult to interpret.
- Requires a lot of ‘boilerplate’ code.
- Uses camelCase naming convention.
7. Testify Framework
Testify is a unit testing framework that is also used for integration and system testing. It aims to replace the popular Unittest and Nose frameworks and add advanced additions to them.
Key Benefits of Testify Framework:
- Easy to understand
- Simple, lucid syntax
- Enables test discovery
- Offers extensive plugins for additional functionality
Disadvantages of Testify Framework:
- Difficult to achieve parallel testing.
- Although it is easy, it doesn’t have good documentation, so beginners might need to struggle for a bit.
8. Doctest Framework
Doctest is a module in the standard library of the Python programming language that makes it easy to generate tests based on the output of the standard Python interpreter shell.
This output is cut and pasted into the docstring. It searches for interactive Python sessions to check if they are working properly.
Key Benefits of Doctest Framework:
- Since it is standard equipment, it is easy to start without installation.
- Extensive code documentation with up-to-date interactive examples
Disadvantages of Doctest Framework:
- Compares only printed output, the test fails if there are discrepancies.
- It cannot be parameterized as it is static in nature.
- Test Discovery, Test Fixtures, and Test Runner features are not supported.
Comparison Table of all these 8 Python Testing Frameworks
|Framework||Benefits||Limitations||When to use|
|Behave Framework||Behave is used for BDD in Python|
|Lettuce Framework||Lettuce is used for BDD in Python|
|Robot Framework||Robot is used for Acceptance Testing and Keyword-driven Testing|
|Pytest Framework||Pytest supports different levels of testing such as unit testing, functional testing, and API tests.|
|TestProject Framework||TestProject is used for end to end testing of web and mobile applications.|
|PyUnit (Unittest) Framework||PyUnit is used for Unit Testing in Python|
|Testify||Testify is used for Unit Testing in Python|
|Doctest||Doctest is used to generate tests easily based on output from the standard Python interpreter shell, cut and pasted into docstrings. It is used to perform regression testing by verifying that interactive examples from a test file or a test object work as expected.|
Choosing the right Python testing framework undoubtedly takes a bit of research and judgment. Attention must be paid to script quality, simplicity of test case definition, and techniques used to run modules. The frameworks described above rank highly on these factors and are widely used by developers because of their many advantages. Nonetheless, it is advisable to try testing on each framework and gauge results for oneself so that the automation testing process is not hindered in any way.