Python is a programming language with unflagging popularity. Every year, this language gains more and more supporters who are attracted to it, encouraged by its simplicity, comprehensibility, and universality. Discovering new ways to use Python encourages creators and enthusiasts to develop new ways to test the created software. Everyone is also striving to make the testing process more and more reliable and consume less time and financial resources.
A testing framework is as much a set of instructions and standards used to design and develop test cases. Inside a testing framework, you will find multiple procedures for various tests. Frameworks help QA specialists find bugs and errors more effectively.
Unit testing focuses on individual code units in isolation from the other parts of the system. Integration testing focuses on how the different parts of code work together and find the conflicts between them. Integration tests and unit tests are used at distinct stages of the development process and have other goals to reach.
Functional testing checks the system against the functional requirements and specifications. After performing the functional tests, you ensure that the application meets the requirements or specifications to the highest degree. Non-functional testing is performed to verify all the aspects not tested in functional tests. Non-functional tests check whether the system behavior meets the requirements or not. To be sure your application is ready to release, you need to perform both functional and non-functional tests.
Python test frameworks are popular because the creators and supportive community constantly develop them. Many test frameworks are being improved to save time and business resources, so they can easily adjust to modern requirements. An incredible advantage of test frameworks in Python is the possibility of combining multiple frameworks. You can prepare a complex testing environment that will check various test scenarios to find even the deepest hidden bugs.
Below are the top Python testing frameworks recommended for use in diverse cases. Take a quick look at each framework and know the advantages and disadvantages of all the presented tools.
Pytest is one of the most popular testing frameworks due to its simplicity. Pytest distinguishes itself with incredibly clean and short test commands. You only need to call an asset keyword to validate a piece of code in this (recognized as the best) Python testing framework. A massive benefit of Pytest is a parallel testing feature that saves a lot of time. Using Pytest, you can perform both integration and unit test cases.
Unittest is a testing framework in Python that comes as a part of a Python package, so it doesn’t need to be installed additionally. Unittest supports object-oriented programming. If you plan to structure your testing, unittest can wrap your test cases in dedicated classes and then execute them in an event loop. Unlike pytest, unittest requires you to write more than a single keyword – this Python testing framework needs a lot of boilerplate code.
Robot is an open-source testing framework in Python. It is also known as a tool to test automation. Using Robot, you can perform acceptance testing to make sure your software meets the defined criteria. You can also use this Python testing framework to handle end-to-end tests and regression tests. Robot framework can perform tests of mobile, web, desktop apps, and more on any operating system.
Selenium is a Python testing framework to perform test cases on UI. The massive popularity of Selenium among software developers and testers and strong community support make Selenium the best Python testing framework for automation cases. Selenium is commonly used to handle cross-platform and cross-desktop test execution. The IDE extension provides record-and-play testing in Chrome and Firefox browsers.
Nose2 is a Python testing framework that can perform unit tests, integration tests, and parametrized tests. Nose2 functionality is extremely similar to Pytest, but many features are inherited straight from unittest. This framework is excellent for running tests created in Selenium or already mentioned unittest. What is characteristic of the Nose2 framework? Multiple plugins that can make test execution fast and simple. Unfortunately, Nose2 is not actively maintained.
Doctest is an advanced testing framework in Python that enables code documentation during the testing procedure. Doctest is a part of the standard Python library, so you don’t need to install an external library to perform tests. The intuitive library and validation of expected output make Doctest one of the best Python testing frameworks for unit testing. But it would be too beautiful if the Doctest had only advantages. This Python testing framework doesn’t support test discovery, test runner features, and text fixtures.
Last but not least, Behave framework is a tool for behavior-driven development (BDD). Understanding Behave framework is easy even for non-technical people because this Python testing framework uses a natural style language. That’s why Behave is appreciated by both business teams and development teams. You can use Behave framework only for black box testing. Unfortunately, parallel testing is not available with Behave.
Each project in your organization has unique needs that restrictions that determine the use of specific testing frameworks in Python. You can choose from many tools rich in features helping you test the most critical parts of your solution. Every Python testing framework has advantages and disadvantages to use in particular cases, so you need to choose wisely.
If you need a tool for simple tests, you will probably use pytest. If you want to have a plain test code structure, you should choose unittest. As you can see, plenty of Python testing frameworks exist, so you should focus on finding a tool that will be technically suited but won’t consume more resources, such as time or finances, than you assumed at the beginning.
What brings us the future in Python testing frameworks? Python doesn’t seem to be losing its popularity. As such, testing tools will undoubtedly be subject to continuous development. What improvements can we expect in the subsequent versions of the top Python frameworks?
The use of AI in testing is not high but still evolving. Artificial Intelligence in software testing tools focuses on simplifying the software development life cycle. AI can find its place in automating the most mundane and tedious tasks in development and testing processes. In any place where the existing automation tools meet their limitations, AI-based tools can improve and speed up the testing process.
Testing is an integral process of the CI/CD pipeline in DevOps. If we don’t include automated testing as a unit, integration, and system tests, the CI/CD process will be hard to complete and ineffective. Because the need to conduct tests exists at many stages of this process, Python testing frameworks will simplify and speed up catching errors that could affect the subsequent failure of the finished product.
Which of the Python testing tools is the best? It depends. On what? There are many aspects you need to consider before you decide on a single testing framework or a combination of more than one tool.
Don’t look only at the particular framework’s popularity or other businesses’ opinions. Consider the project’s specificity, the company’s resources, and the skill level of the developers and testers. Deep research inside and outside your organization will help you choose the Python testing framework optimal in terms of time, budget, complexity, and accuracy.
Do you struggle with finding the perfect testing framework and still have no success choosing the right one? Sometimes you will not find the ideal solution, but the testing phase needs to be performed. Releasing untested software is much worse than using tools that only partially meet your expectations. You can lose too much by entrusting the customer with a product requiring frequent and costly corrections.
Remember that many Python testing frameworks, such as Robot, Pytest, or Selenium, are constantly supported and developed by the community. There are many possibilities to adapt the solutions existing on the market to the needs of the project you work on with your team.