Top 7 Testing Frameworks for Python
Whatever the size of a project, a tiny open-source script, or a big enterprise service, the goal is to be sure that it works as expected and by design. A minor defect is a typo in a word or a sentence; the worst – is affected business revenue because of miscalculation or even downtime caused by unexpected behavior. Testing activities come to eliminate and prevent such mistakes in time.
As the most common practice, manual testing is good, but it fails to guarantee accuracy and coverage. Something might be skipped or untested by human mistakes or pure attention. Test automation does the job better, quicker, with higher accuracy, and can be checked even on the coverage percentage.
Python provides different testing frameworks for developers to work on test automation. As the programming language, Python is commonly used in different scale projects and various fields, tests are necessary and can be written for any complex project. Testing frameworks allow covering the code with different kinds of tests, including texts, localization, and even external services usage. Such tests can be run in various scenarios, such as code reviews, code change pushes, deployments, and others.
The Python language has a built-in testing framework PyUnit. Furthermore, the community maintains various third-party solutions, for example, PyTest, Behave, Lettuce, Test Project, Nose2, Testify, Robot, Selenium, and others. This article gives an overview of some of the best testing frameworks for Python.
Why are testing frameworks with Python so valuable?
Software testing is crucial as it helps validate software behavior and detect errors at early steps, which helps reduce costs, resources, and time investments. While some companies still do manual testing only, the recommendation is to move towards automating as a first thing, in addition to manual testing. It is well-known that manual testing is time-consuming and runs the risk of human error. Therefore, it fails to guarantee accuracy.
Testing frameworks for Python increases development efficiency and save human time. As already mentioned, Python has a built-in unit testing framework PyUnit. Therefore, a developer can write unit tests with Python from the beginning of a project.
Python testing frameworks help reduce engineering efforts and increase productivity. If the tests are run automatically, as part of the built-in process, developers can keep working on features and perfecting code.
Here is a summary of the most prominent benefits of testing with testing frameworks:
- Faster feedback. With tests, new feature feedback is fast. Tests reduce feedback cycles and speed up product validation. Tests help uncover problems or flaws early in the development phase, increasing team efficiency.
- Time-saving team. Teams validate new features quicker by simply triggering running tests. It enhances communication with marketing, design, and product owners who rely on test results. These departments can readily check the automated test logs.
- Reduced business costs. A business will save money by adopting an automated test environment to test a product. Creating an automated testing environment involves time and resources. It requires a solid environment setup. On the other hand, unhappy customers or unsustainable products cost much more than these investments. Imagine a financial service with a bug that causes wrong calculations for customers. A devastation error that tests can prevent.
- More tests. Manual testing restricts the number of checks. Automation lets write additional tests for the automated test suite, which increases a test coverage of a product, resulting in higher-quality software. Automated testing allows engineering to develop complicated use case tests. Unattended long tests can be run.
- Test Suite Reusability. Creating an automated test suite is an effort. After defining a testing suite, it is easy to reuse tests for multiple use cases or projects. The automated test suite can easily be connected to another project. The knowledge of how to set up an automated test suite and related tools like a CI pipeline may be simply replicated for a new project.
- Quicker time-to-market. Test automation may continuously test and certify new functionalities, which lowers feedback and testing cycles and speeds up product launches.
- Insights. Automated testing provides better insights than manual testing when some tests fail. Automated software testing shows memory, data tables, file contents, and other internal application states. Such information helps engineers with root-causing.
What is Unit Testing?
Unit testing is one type of software testing to validate individual units of software code, for example, a function or any other independent unit, use, or operating procedure. The goal of validation is to determine the correctness of its functionality and usage in the software product. For example, does the function calculate and return data by design for different scenarios? In short, unit testing ensures the code is correct but does not test interaction. In addition, a coverage percentage allows evaluation of the amount of possible untested gaps in the code base.
There are two fundamentally different approaches to creating unit tests. Create test cases before the code base or after for already existing ones.
Usually, unit testing is ready before any other type of testing, including integration testing. Developers or Quality Assurance engineers work with unit test cases to test a section of the code, validate its correctness, test the function and procedure, fix any bugs early to save costs, make necessary changes quickly, and help reuse code.
With the Python programming language, developers can access different automated Python testing frameworks, built-in and third-party ones. Most of the frameworks are free of cost; therefore, an engineer will use Python programming language to create new code and automated testing scripts to validate the correct functionality of the code. The created unit tests code base can be utilized over and over for many projects (no need to create new testing code every time), allowing to spend more time programming new features or maintaining software.
Best Python Testing Frameworks
As mentioned above, Python allows using several different testing frameworks, allowing engineers to cover all types of software with different tests. Because of being a scripting programming language, Python is widely used for automation testing suites.
This article gives high-level information about the most commonly used Python testing frameworks. That will help understand the main perks, how simple the testing can be, techniques to run the modules, and limitations to help with the decision-making in determining which one will be more suitable for different needs.
Pytest is a unit testing framework for writing small tests, but it can also support complex functional testing for apps and libraries.
Pytest is one of the most popular third-party testing frameworks that try to solve some of the problems with unit tests. In short, Pytest is an ecosystem for testing Python code with less code, quickly performing complex tasks with several commands and plugins. It can run existing tests, even those written with unit tests, right out of the box.
Advantages: Pytest automatically discovers test modules and functions, provides effective CLI to control options to run or skip, offers a sizeable third-party plugin ecosystem and different fixtures, and works with a unit test framework. Pytest also can generate reporting. Its top features are:
- You can get more information about failing assertions.
- The tools can be used to test both APIs and complex databases.
- It can be connected to GitHub's issue tracker.
- Modular fixtures for different resources.
- Able to run unit tests
- Over 800+ plugins are available.
- Very safe architecture
- Pytest is not compatible with other frameworks.
- PyTest has unique routines for writing tests, which makes it hard to use with other frameworks. To make that happen, you have to rewrite all of the code.
PyUnit (also known as Unittest) supports fixtures, test cases, test suites, and a test runner for automated code testing. In PyUnit, you can organize test cases into suites with the same fixtures. PyUnit (Unittest) was created based on JUnit. Therefore, both have similarities. PyUnit is a base for some other Python testing frameworks, for example, Django Tests. PyUnit (Unittest) supports:
- Test automation frameworks.
- Sharing shutdown and setup code used for testing.
- Aggregating tests into collections.
- The independence that tests sometimes requires from the reporting framework.
PyUnit (Unittest) also supports the essential concepts of an object-oriented language, such as:
- Test fixture
- Preparing test cases
- Preparing test suites for group execution
- Test runner for executing tests
Advantages: PyUnit helps detect bugs early in the development cycle, write better programs, is compatible with other Python testing frameworks, has fewer bugs, and is easy to modify. More advantages include:
- Use a file path to name each test module.
- More information can be added to tests.
- Quick lookup files of tests-modules and packages
- The SetUp method can be used to separate the code for setting up a test case so that the test framework can call it for each test.
- A boilerplate code is required.
- It can be hard to figure out what snake case (Python) and camelCase (JUnit) mean.
- The test code can be hard to understand.
Behave is one of the top Python test frameworks allowing the team to execute BDD (behavior-driven development) testing without complications. Test cases are written in a readable language and later stuck to the code during execution. The behavior specs design the behavior. Other test scenarios then reuse the steps.
Advantages: Behave BDD system uses semi-formal language and domain vocabulary, which keeps the behavior consistent. It allows developer teams to work on different test modules while being coordinated. Behave also possesses ready-to-use building blocks for all test cases. It also provides reasoning and thinking features. Key features include:
- Support of environmental functions, fixtures, configuration settings, and others.
- Behave can run tests on every line in data tables that does not have a heading.
- Support for the tool's documentation is perfect for PyCharm integration.
- Even people who are not tech-savvy can make test cases or test scenarios with the help of Behave.
- Behave testing framework only works with black box testing.
- Even though PyCharm integration exists, the PyCharm community does not support it.
- Parallel test execution is a part of test automation that must be done, but Behave does not support that. At the same time, third-party software integration makes it possible.
Lettuce is another simple and easy-to-use Python BDD (behavior-driven development) automation tool based on Cucumber. The main objective of Lettuce is to focus on the everyday tasks of behavior-driven development, making the process simpler and entertaining.
- Lettuce allows developers to work on more than one case.
- Lettuce uses simple and plain text descriptions allowing developers to write tests anyone can understand.
- Lettuce BDD Python framework requires excellent communication between developer teams, Quality Assurance Engineers, and stakeholders.
- If there are any gaps, the result can be confusing. Same as Behave, Lettuce is limited to black box testing.
TestProject is an automated testing tool for web, API, and mobile apps that allow Quality Assurance engineers to test web, Android, or iOS apps. This framework is community-powered. That allows users to extend the framework using add-ons shared by others on the platform.
- The test project is effective but easy to use, with minimal time to learn how to operate.
- It works best when the entire team s the ability to work together on the platform. This tool has a full library of shared add-ons, allowing you to create your own add-on.
- With TestProject, you can create new tests or import existing ones to your current platform, and it allows you to move from Android and iOS and work on any platform (Windows, Linux, or macOS).
- Test Project also creates free, automatic reports in HTML/PDF format has easy access to exec history with RESTful API, and is always updated with the latest Selenium/Appium driver versions.
- TestProject does not include the cross-browser cloud.
- In comparison to other similar solutions, Test Project is much slower.
Nose 2 (successor of Nose) is a framework that extends Unittest (PyUnit). Nose2 supports more configurations and provides more tools than PyUnit.
Advantages: Nose2 can make testing simple and accessible using its ample collective tests. It also supports multiple functions for writing. Nose2 can run doc tests, Unittest, and extend PyUnit, using its plugins for support, testing, discovery, decorators, execution, fixtures, and parameterization. Key features include:
- Nose2 gives you more options for testing than PyUnittest does.
- Nose 2 works with all the latest versions of Python.
- Nose 2 first loads all of the tests, and then it starts to run.
- One of the best things about Nose2 is that you do not need a custom importer to use it. All you have to do is import ().
- supports the same type and level of fixtures as Unittest
- Supports all test generators in test classes, unit test Tests, test functions, and Case subclasses.
- Config files can be used to do all of the setting up.
- With nose2.collector.collector, Nose2 can get into the test-running process (). Each test case has a test runner hidden inside of it. It runs tests just like normal Nose2, but it can also run its own test runner and tests.
- Requires Python 3; Python 2 is no longer supported.
- When the class method is called, it takes much work to finish tests.
- It is also hard to figure out why sessions are open and where to close them.
Testify is another unit testing framework for Python that can also be used for integration and system testing. It is modeled after PyUnit (unit test) with an extension of features such as naming conventions, a better test runner visually, and a more decorative approach to fixture methods.
- Testify is easy to get started.
- It also has extensive plugins for additional functionality.
- It enables test discovery, works with simple and easy syntax, and is suitable for integration, unit, and system testing.
- In comparison to other tools, Testify lacks extensive documentation.
- It also has difficulty achieving parallel testing.
Python is a valuable programming language on the market, and one of its advantages is having a variety of testing frameworks. This characteristic brings many options, including must-have unit testing frameworks, to the table. Suppose a company decides to go with covering a Python project with tests. That should be a must-have decision. In that case, review the various frameworks and make a choice. A combination of different frameworks can increase the quality of the test.
It is no secret that there are tons of testing framework tools out there, and this article pooled together some of the most reliable and commonly used tools for automated testing in Python.
Setting up automation can take much time, effort, and resources, even though it is a huge time-saver and cost-effective solution for the long term. It can take much work to implement and keep up. Nevertheless, worry not; we are here to help you!
Svitla Systems has vast Quality Assurance expertise and can add the best automation frameworks to your development pipeline to make your product with fewer bugs. Get in touch with us, and we will be happy to help you!
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