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?

An Overview of Python Unit Testing

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.

Why is Python Unit Testing Performed?

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.

Other advantages of Python unit testing include the following:

Simplifying integration:

By ensuring that all the components work well individually, it’s easier to solve integration problems.

Minimizing code regression:

With a good amount of test cases, if future modifications to the source code will cause problems, it’s easier to locate the issue.

Providing documentation:

Unit tests provide documentation on how the method or class under test works by testing the correct mapping between input and output.

How to perform Python Unit Testing?

Unit testing in Python is a software testing methodology where individual units or components of a software application are tested in isolation to ensure their correctness. A unit can be a function, method, class, or module. Unit testing aims to identify defects early in the development process and provide a way to verify that each component of the software performs as expected.

Key concepts in unit testing:

Test Case: A test case is a self-contained unit of testing that focuses on a specific aspect of the code. It includes the input data, the action being tested, and the expected output.

Assertions: Assertions are statements that verify the expected behavior of the code under test. They are used to compare the actual output with the desired output and raise an error if they don’t match.

Test Fixture: A test fixture is the preparation and cleanup work necessary to perform a test. It can include setting up initial conditions, such as creating objects, setting variables, and preparing the environment.

Test Suite: A test suite is a collection of grouped test cases. It allows you to run multiple tests together, making it easier to manage and execute tests for a specific part of the code.

Test Runner: A test runner is a tool or framework that automates the execution of test cases and provides reports on the test results. In Python, the built-in unittest module provides a test runner for executing tests.

Mocking and Stubs: Mocking and stubbing are techniques used to replace parts of the code that are external dependencies (such as databases or web services) with simulated or controlled implementations. This helps isolate the unit being tested from its dependencies.

Isolation: Unit tests should be isolated to ensure that failures in one test don’t affect others. This is achieved by resetting the state between tests and avoiding shared resources.

Coverage: Test coverage measures the percentage of code executed during testing. It helps identify areas of the code that are not adequately tested.

Key Features of a Reliable Python Unit Test

Promote Rapid Testing:

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.

Ensure Isolation:

Unit tests are standalone. They test the individual unit and do not depend on anything external (like a file or a network resource).

Repeatable:

Unit tests are executed repeatedly, and the result must be consistent over time.

Enable Reliability:

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.

Accurate Naming:

The name of the test should provide relevant information about the test itself.

Top Python Unit Testing Frameworks to Look for in 2023

Let’s go through a list of some of the best Python Unit Testing Frameworks and understand their key features:

TestProject

• 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.

• 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.

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.

Doctest

• 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.

Behave

• 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

• 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.

Testify

• 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.

PyTest

• 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.

Conclusion

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.

How can TestingXperts Help

TestingXperts (Tx) is one of the 5 largest pure-play software testing services providers globally. Tx has been chosen as a trusted QA partner by Fortune clients and ensures superior testing outcomes for its global clientele. We have rich expertise in enabling end-to-end testing services for global clients across various industry domains like healthcare, telecom, BFSI, retail & eCommerce, etc.

Our wide range of QA testing services includes:

Test Advisory:

TestingXperts’ Test Advisory Services provide you with specialist advice to help you achieve overall product quality, reduced release cycle time, faster time-to-market, and significant reductions in QA costs. Our Test advisory services will help you move to the next maturity level in QA.

DevOps Consulting Services:

Our DevOps experts will analyze your business needs and help you achieve a strategic DevOps adoption roadmap. Our wide range of DevOps services like DevOps consulting, DevOps CI/CD implementation, Enterprise DevOps Transformation, IAC consulting and Implementation, and Test Automation in DevOps will help you achieve faster and quality releases.

Test Automation:

We have in-house test automation experts with rich expertise in industry-leading test automation tools. We can help you reduce regression time and cost with our end-to-end test automation services with our in-house test automation accelerator Tx-Automate.

Functional Testing:

Tx has years of experience providing functional testing services for clients across domains. Our wide range of available testing services includes automation, manual, regression, and mobile application testing. We have in-house mobile test labs with numerous mobile devices.

Non-Functional Testing:

Tx provides a wide range of non-functional testing services such as performance and load testing, security testing, usability testing, and accessibility testing. We also have an in-house performance testing accelerator, ‘Tx-PEARS,’ which helps with all non-functional testing requirements, including continuous monitoring of your infrastructure in production as well as in lower environments.

Next-gen Testing:

Our wide range of next-gen testing services includes AI testing, RPA testing using UiPath Test Suite, Digital testing, IoT testing to name a few.

The post Python Unit Testing: Key Benefits, Features, and Frameworks first appeared on TestingXperts.

Contents 1. Python testing framework overview 2. Why do testers prefer Python testing frameworks for test automation? 3. Python testing framework building blocks 4. Comprehensive list of Python testing frameworks 5. Why Selenium with Python is a good choice for developing a Python framework? 6. What are the best practices for Python Unit Testing Frameworks? 7. Basic testing types in Python 8. Conclusion

Today’s businesses need faster software feature releases to get high-quality products and wish to market early without compromising software quality. The faster release of new features or bug fixes in existing features necessitates robust end-to-end software testing to ensure successful deployments. While small applications or software can be handled through manual testing, large and complex applications need dedicated resources and technologies such as java or python testing framework, automation testing tools, etc. to ensure maximum test coverage in less time to get faster and quality releases.

To perform test automation, businesses should leverage robust test automation frameworks developed using different programming languages. Python is one such important language used to develop robust test automation frameworks. Testers widely use Python testing frameworks to get effective testing outcomes.

Python testing framework overview

Python testing framework is a dynamic test automation framework developed using Python programming language, which is considered easy to use for web development and test automation. Since Python is an open-source programming language, many frameworks are based on python, making it hard for testers to determine which test automation framework to choose from the variety of available options.

Why do testers prefer Python testing frameworks for test automation?

• Ease of usage and less verbose

• Comes with active community support

• Contains a vast library of frameworks

• Supports parallel testing

• Easy integration with CI/CD tools

• Supports several IDEs

• Based on The Zen of Python

Python testing framework building blocks

• Decorators

• Context managers

• Higher-order functions

• Introspection

• Advanced Python object concepts (magic methods, descriptors, etc.)

• Generators

• Iterator protocol

Comprehensive list of Python testing frameworks

Django:

It offers some distinguishing features like ORM, URL routing, and database schema migration, making Django highly scalable and versatile. Django offers ready-made components for rapid development, and the framework is embedded with enhanced security features.

Doctest:

It is a simple yet powerful python testing framework in which tests can be written in line with the code. Testers use Doctest to perform regression testing by confirming if the interactive examples from a test file or object work and perform as expected. As a result, the documented Python statements are converted into test cases.

Pyramid:

It is an open-source framework that can run well with small and large applications. Pyramid is a highly valued Python framework due to its extensive scalability, flexibility, and testability.

Flask:

This Python framework provides tools to facilitate the web development process. Flask is relatively easy to learn and understand and provides a great environment to build functional applications. Flask has a modular design, making it adaptable for various development processes. This platform provides integrated support for unit testing and has Google app engine compatibility.

CherryPy:

This object-oriented web framework provides a production-ready WSGI thread-polled web server. Python developers use this framework to create portable database-driven web apps. CherryPy has inbuilt tools for static content, encoding, caching, and testing.

Robot Python Testing Framework:

It is a test automation framework used for acceptance testing, acceptance test-driven development (ATDD), and Robotic Process Automation (RPA). It is based on the Keyword Driven Testing approach; thus, it allows testers to create test cases easily without any coding experience. It supports all OS and applications (web, mobile, and desktop).

Pytest:

It is a web automation testing framework that supports unit testing, functional testing, API testing, etc. Pytest supports the Test Driven Development approach and parallel test execution through the pytest-xdist plugin. Pytest is compatible with Unittest, PyUnit, and other Python testing frameworks.

PyUnit:

It is a default Python test framework part of the Python standard library. Unit test is a derivative of the xUnit framework and has some similarities. It comes with an easy installation and configuration option, thus making UnitTest Framework one of the most preferred Python Unit testing frameworks.

TestProject:

It is a free and open-source complete automation testing framework. Testers can quickly develop test cases for mobile, web, or generic automation using this framework. It is a single-agent executable and comes with third-party libraries needed to develop and execute test automation for web, mobile and generic. It generates free automatic reports in PDF and HTML formats.

Lettuce:

This Python behavior-driven development framework is based on Cucumber. It supports the Gherkin language, making it easier for non-technical testers to create automated tests.

Gauge:

This open-source framework was developed by the same people who created Selenium. It allows cross-browser functionality testing and can be easily integrated with DevOps CI/CD pipeline. It supports all major plugins, languages, and command lines.

Why Selenium with Python is a good choice for developing a Python framework?

Selenium is an open-source software testing tool that supports various operating systems and browsers like Chrome, Firefox, Safari, etc. It allows scripting in multiple languages like Java, Python, Ruby, C#, etc. Selenium is a tool suite with three components: Selenium IDE, Selenium WebDriver, and Selenium Grid. By leveraging the Selenium tool, testers can perform various tests, such as smoke, sanity, UI, regression, and more. Python language is used with Selenium to perform testing. Python API helps connect with the browser through Selenium. Python Selenium combination can be used to write functional/acceptance tests using Selenium WebDriver.

Reasons why Selenium and Python testing frameworks are preferred:

• Python is easy to code which makes test script creation extremely easy for testers and is used along with Selenium

• A large number of testers/programmers are well aware and familiar with Python language; therefore, they prefer Python with Selenium for test automation

• The binding of Python and Selenium offers a simple API to write functional tests by utilizing Selenium WebDriver

• Python is comparatively faster than other languages; thus, it makes Selenium automation testing very fast

• Active community support and open-source availability of Python language is another reason that makes it suitable with Selenium

What are the best practices for Python Unit Testing Frameworks?

• Prioritize writing test cases for sections with minimum dependencies on external resources

• Tests should be logical, simple, and independent of other tests

• Each unit test should be named precisely and documented appropriately

• Testers should create and run unit tests that cover exceptions

Basic testing types in Python

Unit Tests:

This tests specific methods and logic in the code. Unit tests are the most granular type of test with the goal of verifying the internal flow of the method, as well as making sure edge cases are being handled.

Feature Tests:

This tests the functionality of the component. A collection of unit tests may or may not represent a Feature test. The goal is to verify that the component meets the requirements given for it.

Integration Tests:

This tests the entire application end-to-end. The goal is to guarantee the stability of the application. When new code is added, integration tests should still pass with minimal effort.

Performance Tests:

This tests the efficiency of a piece of code, and the size of the code can range from a method to the whole application.

Conclusion

Python is an easy-to-use language with various features and community support, making it a great 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 in less time 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.

The post Python Testing Framework – An Informative Guide first appeared on TestingXperts.

Today’s digital businesses need high-quality software in less time to deliver a great customer experience (CX). Therefore, before the software is released into the market, it goes through rigorous testing phases, which consumes a lot of time. But, for businesses to reach the market early, the software testing cycles need to be shortened, and it becomes possible adopting automation testing.

What is automation testing using Python?

Automation testing is a software testing method in which automated tools, frameworks, and scripts are used for testing the software. In this software testing technique, reusable test scripts are developed to test the app functionality, thus reducing the overall regression time and helping with faster software releases. Leveraging test automation reduces regression time and quality releases within the testing life cycle.

Various programming languages are used for creating automated test scripts, including Python, C#, Ruby, Java, etc. But, Python is a simple-to-use programming language, easy to learn, and has more resemblance to the English language. Moreover, Python is an open-source language with an approachable syntax that’s easy to read and understand relative to other programming languages. It is more preferred by testers for automated software testing, while other programming languages can also be used for automation testing.

What are the best programming languages preferred for automation testing?

1. Python:

It is an open-source programming language for test automation of web and desktop applications, machine learning, network servers, media tools, and test automation. It is an easy-to-use language and comes with vast library support.

2. Java:

This is one of the most popular programming languages for test automation. It works on the WORA (Write Once, Run Anywhere) principle, making cross-platform testing very easy. It comes with complete test frameworks, packages, and information sources.

3. C#:

It is an object-oriented programming language used to develop web and desktop applications. It is also one of the most widely used programming languages for test automation. It is best suited for windows, android, and iOS applications/platforms.

4. Ruby:

It is an open-source programming language; this language is slowly gaining attention from testers worldwide. This language is known for its simplicity and productivity and is easy to use, making it one of the easy-to-use programming languages.

5. Java Script:

It is another commonly used programming language for test automation and front-end development. One of the major reasons why testers prefer Java Script is that it supports shift-left methodologies where developers are involved in test code development.

But, Python is the most preferred language among all the above mentioned.

Python’s importance for automated software testing

1. This language has many built-in testing frameworks, such as Pytest, Robot, etc., which ensures faster  debugging and the creation of faster workflows

2. It is an interpreted language and implements the code line by line, which makes the debugging easy

3. As it is a cross-platform language, it can run on different platforms like Windows, Linux, and Mac

4. It can also be easily implemented with other programming languages such as C, C++, JAVA, etc.

How to configure using Python?

1. Quickly download and install Python on the OS

2. Install Selenium libraries in Python

3. Download and install Pycharm editor

4. Create one new project and write the Selenium test scripts

5. Run the test scripts and finally validate them

Why do testers prefer automated software testing with Python?

1. Easy to use and less verbose:

Python is comparatively easy to learn and use language. It is best suggested for beginners who want to learn software programming languages.

2. Active community support:

Python comes with active community support that offers technical help at no cost, making it the most preferred language for test automation.

3. Comes with a set of tools, frameworks, and libraries:

Python comes with a vast library of frameworks. PyUnit or Unittest is a default framework in Python for test automation. Apart from this, Python supports various other frameworks for test automation, such as PyTest, Behave, Robot, Lettuce, Nose2, Testify, etc.

4. Allows parallel testing:

Python supports parallel testing across browsers and operating systems for test automation, making it one of the most preferred languages for automation testing by testers.

5. Easily integrates with CI/CD tools:

The Python ecosystem contains tools that can be easily integrated with CI/CD tools like Jenkins, which is one of the added advantages of Python.

6. Supports a wide range of IDEs:

Python comes with a wide range of IDEs for test automation, and one of the most preferred IDE for test automation is PyCharm.

7. The Zen of Python is ideal for programming:

According to Wikipedia, The Zen of Python is a collection of 19 “guiding principles” for writing computer programs that influence the design of the Python programming language. These guidelines contain basic rules that testers can make automated test scripts very efficient and conduct test automation seamlessly.

Python testing frameworks for automation testing

1. Robot Framework:

It is an open-source test automation framework used for user acceptance testing, acceptance test-driven development (ATDD), and

2. Pytest:

It is an open-source web automation testing framework that supports unit testing, functional testing, API testing, etc. Pytest can be used for simple as well as complex functional test cases. It supports the Test Driven Development approach and supports parallel test execution through the pytest-xdist plugin. Pytest is compatible with other Python testing frameworks such as Unittest or Pyunit.

3. Unittest or PyUnit:

It is a default Python testing framework part of the Python standard library. Unittest is a derivative of xUnit framework and has some similarities with it. It comes with an easy installation and Configuration option, making UnitTest Framework one of the most preferred Python Unit testing frameworks.

4. TestProject:

It is an open-source complete automation testing framework. Testers can easily develop test cases for mobile, web, or generic automation using this test framework. It is a single-agent executable framework and comes with third-party libraries that are needed to develop and execute test automation for web, mobile and generic. It generates free automatic reports in PDF and HTML formats

5. Nose2:

It is a Python-based unit testing framework that can be used to run DocTests and UnitTests. Nose2 is a Domain-Specific Language (DSL) for writing functional tests and has an inbuilt plugin ecosystem that makes testing simple and faster.

6. Behave:

It is one of the most widely used Python behavior-driven development testing frameworks. This framework allows testers to write test cases in an easy and understandable language and has good documentation support.

7. Testify:

Testify is a replacement for Unittest and Nose. It has more advanced features than Unittest. This framework can be used for unit testing, system testing, and integration testing.

8. Lettuce:

It is a Python behavior-driven development framework based on Cucumber. It supports the Gherkin language. Testers with a non-technical background can easily use this language to create automated tests.

Conclusion

In earlier days, the testers were expected to know complex programming languages to perform software testing. Today, various programming languages are available and by leveraging them today’s testers can execute automated tests with much ease.

Python is one such programming language for automated software testing, which is easy to learn and use. It makes automated software testing easier, even for testers that have just started their journey in software testing. If you are looking for Automation testing using Python, speak to our experts and we can help.

The post An Overview of Automation Testing Using Python first appeared on TestingXperts.