Test Automation with Python

Python is one of the programming languages that doesn’t need any introduction! It is one of the most preferred languages when it comes to projects that involve Artificial intelligence (AI), Machine Learning (ML), and more.

However, when it comes to Test Automation there are several options available to developers and automation engineers in terms of programming languages. Automation testing is the execution of tests using software or tools rather than a human. It is one of the best techniques to boost Software testing’s efficiency, test coverage, and execution speed.

Today we will learn about some of the benefits of Automated testing, how Automation testing with python works, and why Python is the preferred choice for automation testing along with some of its testing frameworks. So without waiting further, let’s dive into it.

Table of Contents:

• What is Automation Testing with python?

• Benefits of Automation testing
  

• Python’s importance for Automated software testing

• Why do testers prefer automated software testing with python?

• Testing Frameworks for Automation testing with Python

• Conclusion

What is Automation testing with Python?

Automation testing with python simply terms the testing of applications and software using Automated tools, frameworks, and scripts using Python as the programming language. There are several programming languages like C#, Ruby, Java, etc. that can all be used to write automated test scripts.

However, Python is more similar to the English language, easier to learn and use compared to other programming languages, and an open-source language with an approachable syntax that is simple to read and grasp. Thus, when it comes to automated software testing, testers favor and prefer to use Python.

Benefits of Automation testing

• Saves Time: With Test Automation the tests can be run repeatedly with no additional cost and are also faster resulting in saving a lot of time.

• Faster Feedback: Automation testing provides another big edge with faster feedback which results in quicker bug fixes and reduced downtime.

• Scalability: Test Automation can scale in a matter of minutes. It simply requires adding more test executors to the test automation solution.

• Accuracy: With test automation, we can eliminate the reliance on a manual tester and thereby consistently produce the desired results.

Python’s importance for Test Automation

• Provides many built-in testing frameworks, such as Pytest, Robot, etc., and ensures faster debugging and the creation of faster workflows.
• An interpreted language that implements the code line by line, which makes debugging easy.
• As it is also a cross-platform language, it can run on different platforms like Windows, Linux, and Mac.
• It can also be easily implemented with other programming languages such as C, C++, JAVA, etc.

Why do testers prefer automated software testing with Python

• Less verbose and simple to use: Python is a relatively simple language to learn and use. As it is a scripting language, the automation engineer need not bother about running a compiler to turn code into an executable.

• Active support of the community: Python is the most popular language for test automation because of its vibrant community support, which provides professional assistance without charge.

• Parallel testing: Python is one of the most popular languages for automated testing among testers since it facilitates parallel testing across operating systems and browsers.

• Set of tools, frameworks, and libraries: Python has a big set of frameworks built in. The standard framework for test automation is PyUnit or Unittest. In addition, Python supports various alternative test automation frameworks, including PyTest, Behave, Robot, Lettuce, Nose2, Testify, etc.

• Integrates quickly with CI/CD tools: One of the other benefits of Python is its ecosystem which provides tools that can be quickly linked with CI/CD solutions like Jenkins.

• Supports a variety of IDEs: Python offers a wide variety of IDEs for test automation. One of the most popular IDEs for test automation in Python is PyCharm.

Testing Frameworks for Automation testing with Python

Python’s wide-ranging support for test automation frameworks is one of several factors contributing to its popularity. There are numerous Python frameworks available in addition to the unittest (or PyUnit), which is the standard Python testing framework.

It is now vital to select the best Python automation testing framework that meets your needs due to the growing number of available options. The best Python testing frameworks for automated testing are:

PyTest

PyTest is an open-source Python web automation tool, designed mainly for unit testing, Because it can be used to create both straightforward automated tests and intricate functional tests for libraries and apps, this particular Python testing framework is highly scalable

It does not require a lot of boilerplate code and there is no requirement for tests to be included in big test classes. It has built-in features that support the auto-discovery of test modules and functions. If you’re creating intricate functional tests, PyTest is a better option than the standard framework, unittest. Fixtures, Classes, and Parameterization can be quite helpful for creating automated tests.

Pros of PyTest:

• It is compatible for use with many Python testing frameworks, including unittest (or PyUnit).
• PyTest can be used to create complicated functional test cases in addition to simple tests.
• PyTest tests have very little boilerplate code, making them simple to understand.
• The Python testing framework already has a robust plugin architecture, and it is extendable. With PyTest, there are currently more than 315 external plugins.

Cons of PyTest:

• As PyTest uses its unique development routines, it is not compatible with other Selenium Python testing frameworks.
• The only way to move an existing PyTest implementation to another Python testing framework is to completely rewrite the implementation.

Behave

Behave is one of the popular BDD (Business Driven Development) testing frameworks for Python. It uses the Gherkin programming language to produce scenario and feature files. Gherkin’s use of an easy-to-read language for test case development makes it possible for team members who lack the technical expertise to write tests, supporting Business Driven Development.

The use of a BDD framework facilitates improved teamwork because engineers, managers, quality analysts, business development managers, and others work more closely together. It is quite simple to get started with a behavior-driven Python testing framework like Behave if you are familiar with existing behavior-driven development (BDD) frameworks like SpecFlow, Cucumber, Cucumber-JVM, etc.

Pros of Behave:

• It is relatively simple and easy to level up with Behave if the team has previous expertise with any existing BDD framework, such as SpecFlow, Cucumber, etc.
• Easy setup and cleanup are made possible by the availability of environmental functions, configuration settings, fixtures, etc.
• It offers integration with web frameworks like Django and Flask.
• It makes it easier to create fresh test cases as tests can include a variety of scenario steps, scenario outcomes, feature files, and other things.

Cons of Behave:

• Lack of integrated support for parallel test execution, one of the key prerequisites for automated browser testing.
• It can only be used for Black-box testing

Robot

Robot is a general open-source automation framework. Because it is open and extendable, it may be combined with almost any other tool to produce robust and adaptable automation solutions.

Since the Robot framework uses human-readable keywords, its syntax is simple to comprehend. Python-based Selenium libraries can be used to expand the capabilities of the Robot framework. It has a robust ecosystem made up of tools and libraries created for different projects.

Pros of Robot:

• The test data syntax is simple to use, making it simple to get started.
• For testing various applications, Robot offers a robust ecosystem of libraries, including Appium for mobile automation, database libraries for DB testing, etc.
• It features a good built-in reporting system because, after each build, an HTML log is produced.

Cons of Robot:

• There is no built-in functionality for running tests in parallel.
• It’s challenging to make customized HTML reports.

PyUnit

PyUnit (sometimes known as unittest) is the default Python testing framework and is a part of the Python standard library. The JUnit framework served as an inspiration for the Python Testing Framework. It is a component of the unittest module, which has been included since Python 2.1 was released.

Many developers who are starting with Selenium test automation prefer the unittest Python testing framework because it is included out of the box. Test cases are organized according to certain terminology, where tests begin with test_ or finish with _test.

Pros of PyUnit:

• Installation of any extra packages is not necessary.
• The output of the default execution is clear and straightforward.
• It provides a number of command-line parameters that aid in producing output that is more thorough.
• It simply takes a few milliseconds to generate a test report.

Cons of PyUnit:

• Testcase names continue to follow Java’s camelCase naming pattern.
• For test implementation, a lot of boilerplate code is utilized.
• No option exists for color output.

Lettuce

Lettuce which is a Python testing framework hosted on the open-source website GitHub makes BDD testing easier. It takes cucumber as its source of inspiration (that was designed for Ruby).

Lettuce’s album names are derived from the color green. Kryptonite is the name of Lettuce’s most recent version, which is 0.2.23. Lettuce was created to make testing amusing and simple.

Pros of Lettuce:

• Lettuce also uses the Gherkin language to create scenarios and feature files.
• Lettuce will be easy for you to use if you’ve already used a BDD testing tool.
• It is simple to create new test cases because feature files and scenario files need to be updated on a regular basis.

Cons of Lettuce:

• It is mostly employed for black-box testing.
• Perfect execution utilizing a BDD testing tool like Lettuce demands prompt communication between all relevant project stakeholders.

Nose2

Nose2, a Python unit testing framework is also referred to as an extension of unittest because it is based on the unittest. By offering plugins for test execution, enabling the automatic discovery of test cases, and gathering documentation, Nose2 enhances the PyUnit framework’s support.

Decorators, fixtures, parameterized testing, modules, classes, and test discovery are supported by Nose2’s plugin ecosystem. With all these new features, testing with PyUnit is made straightforward.

Pros of Nose2:

• Nose2 enhances the existing unittest framework, making it simple to install and get going.
• Nose2 scripts offer a wide range of command-line arguments and comprehensive configuration using config files.
• Nose2’s mp plugin allows tests to run concurrently across several processes. This speeds up the test execution if the tests are considerably CPU- or IO-bound.

Cons of Nose2:

• Nose2’s documentation is not as comprehensive as that of the PyTest framework.
• For programmers who desire to use Nose2 in their applications, the deprecation of the nose framework, which was supported on Python 2.4 (and above), could be a possible big obstruction.

Testify

Testify is another well-liked Python testing framework that is seen as a successor to the nose and unittest frameworks. The tests created for the unittest will only need minor modifications to function with Testify because the framework is modeled after the unittest.

Automated unit, integration, and system testing are all possible using Testify. It is currently at version 0.11.0 and includes an expandable plugin system that offers helpful reporting functionality. Testify also offers improved test discovery and class-level SetUp & TearDown fixture methods that are executed once for the whole collection of test methods, just like the Nose2 framework does.

Pros of Testify:

• Since Testify is built on the unittest framework, it is really simple to get started with it.
• The ecosystem of plugins for the framework is expandable.
• Additionally, it offers testing tools for integration, profiling, and code coverage.
• Similar to the Nose2 framework, Testify has improved test discovery features that make locating tests very simple.

Cons of Testify:

• Similar to Nose2, Testify has a problem with its documentation that could be a hindrance if you’re just starting started with the framework.
• According to a few popular discussion sites, parallel testing with Testify may be difficult.

References

• https://www.geeksforgeeks.org/automated-software-testing-with-python/
• https://www.softwaretestinghelp.com/python-testing-frameworks/
• https://www.javatpoint.com/automate-software-testing-with-python
• https://automationpanda.com/2020/11/09/learning-python-test-automation/

Conclusion

In this article, we’ve learned about the use of Python in Automated testing along with some of the well-known Python automation testing frameworks. Each of these frameworks has a unique set of benefits and drawbacks.

In conclusion, automation testing provides a lot of benefits with Python, and selecting the best Python testing framework only going to depend on the project requirements and the team’s expertise.