Published: 27 Jan 2023
Python Unit Testing: Key Benefits, Features, and Frameworks
Last Updated: 31 Jan 2023
Content 1. An Overview of Python Unit Testing 2. Why is Python Unit Testing Performed? 3. Key Features of a Reliable Python Unit Test 4. Top Python Unit Testing Frameworks to Look for in 2023 5. Conclusion 6. How can TestingXperts Help with?
Python unit tests are scripted code-level tests written in Python to verify a small “unit” of functionality. Unit tests in general are often designed to provide a broad testing coverage of the web application, including any weird corner cases and some tests that should not work. Interaction is minimal with resources such as networks and databases. The code relying on these resources for Python unit testing is typically tested through functional, automated acceptance, and/or integration tests.
Python unit testing focuses on checking whether the software is behaving correctly, which means checking that the mapping between the inputs and the outputs is all done correctly. Unit testing helps in the early identification of bugs so that they aren’t propagated to higher levels of the software system.
By ensuring that all the components work well individually, it’s easier to solve integration problems.
With a good amount of test cases, if future modifications to the source code will cause problems, it’s easier to locate the issue.
Unit tests provide documentation on how the method or class under test works by testing the correct mapping between input and output.
Unit tests are mostly executed automatically, which means they must be fast. Developers intend to skip slow unit tests because they don’t provide instant feedback.
Unit tests are standalone. They test the individual unit and do not depend on anything external (like a file or a network resource).
Unit tests are executed repeatedly, and the result must be consistent over time.
Unit tests fail when there’s a bug in the system or application under test. The environment or the order of execution of the tests shouldn’t matter.
The name of the test should provide relevant information about the test itself.
Let’s go through a list of some of the best Python Unit Testing Frameworks and understand their key features:
• Single Agent executable includes all the 3rd party libraries needed to execute and develop test automation for mobile and web tests.
• Screenshot-based automatic reports in HTML/PDF format.
• Execution history accessible through RESTful API.
• Always updated with the latest and stable Selenium/Appium driver versions.Common SDK for Web, Android, and iOS-based testing.
• Built-in test runner and reporting capabilities.
• Cross-platform assistance for Mac, Windows, Linux, and Docker.
• Extensive support from the community through forums, blogs, and live chat.
• 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.
• Doctest is a simple, easy-to-use unit testing framework.
• The testing script goes in docstring with a small function at the bottom of the file that runs it.
• Lets testers test the code by running examples included in the documentation and verifying that they return the desired results.
• After parsing the help text, Doctest searches for examples, runs them, and compares the output against the expected value.
• Docstrings serve as tangible examples and tests of the functions they document.
• Supports environmental functions, fixtures, configuration settings, etc.
• Often scenario comes with a multitude of data sets. Behave can run tests on each line that does not have headings that appears in the data tables.
• PyCharm integration.
• Documentation support for the tool is impeccable.
• With the help of Behave, even non-technical people can create test cases or test scenarios.
• Nose 2 supports all the latest Python versions.
• Nose 2 loads all the tests first, then starts the execution.
• One of the biggest features of Nose2 is that it does not require a custom importer. All it needs is __import__().
• Supports the same kind and level of fixtures as Unittest.
• Supports all test generators in test classes, unit tests, test functions, and in Case subclasses.
• Config files can do all the configuration.
• Nose2 can invade the test running process with nose2.collector.collector(). This is a test runner hidden inside each test case. Even though it runs tests as normal Nose2, it can run its test runner and tests.
• Used for unit testing, integration testing, and system testing.
• For those familiar with Unittest, Testify is very easy to start with.
• Has extensive plugins.
• Similar to Nose2, Testify enables test discovery.
• Simple syntax to fixture method.
• Provides a compact and simple test suite.
• Highly extensible with many plugins, such as the Pytest HTML plugin, which can be added to a project to print HTML reports with a single command-line option.
• It has significant community support.
• It helps to cover all parameter combinations without rewriting test cases.
Python is an easy-to-use language with various features and community support, making it an excellent choice for test automation by testers. Thus, the automation testing framework developed in Python is considered a good choice for test automation. Python testing framework makes the test automation process easy and fast, which helps testers to test and release software faster. It ultimately enables businesses to achieve quality software quickly and deliver a great CX. Businesses should therefore leverage automation testing from a next-gen QA and software testing services provider to get superior products and reach the market early.
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