Top 5 Python Testing Frameworks

Python testing frameworks are used by many and their number is growing exponentially. According to a recent survey, Python is the most wanted language in the world.

In terms of the most loved programming python emerged as third, and it’s expected that soon the language will grab the first place.

In terms of software testing, Why is python testing frameworks are regarded so much?

• First of all, ease of coding and ease of reading is painless. Those who are scripting in python need not worry about running a compiler or the execution of the code.
• For instance, one of the most renowned Python unit testing frameworks, PyUnit helps save a lot of time by eradicating the need for the creation of test cases again and again.
• Another example would be that of Selenium. Selenium can be used to run parallel tests across a multitude of browsers and OS versions. Even though we can use a multitude of languages for Selenium the easiest one to use is Python.
• However, if you are using a Python testing framework it would be much easier. PyTest has a plugin called pytest-xdist. Pytest-xdist helps in distributing the test across multiple CPUs to speed up the execution.
• –looponfail: Helps to run tests repeatedly as a subprocess after each PyTest run until the file in your project changes.
• PIP (Preferred Installer Program) is the packet management module of python. The module offers a great deal of help in installing packages and libraries. In the case of testing, PIP searches for the required packages along with their dependencies.
• Python is an object-oriented programming language and it’s well-structured and helps you in choosing functions or classes based on your tasks very easily.
• When it comes to IDE Python’s PyCharm can be used for selenium test automation. Also, PyCharm has features such as code highlighting, code refactoring, debugging, etc., Python also lets you use editors such as IDLE, Sublime Text 3, Atom, Thonny, and Visual Studio Code, etc.
• Reporting is one of the major aspects of test automation. The report has to be visually detailed so that issues can be tracked and fixed effectively. Combining Selenium test automation align with PyTest through an HTML package provides visual reporting so that it can be compared saily with the software.
• Command-line in Python can be used to govern test automation workflow. Using the command line you can run tests through the command prompt.
• Python’s integration with CI/CD tools is impeccable. PyTest can be coupled with Jenkins or any other CI/CD tools for better test execution, creating code metrics, reporting, and tracking

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Top 5 Python testing Frameworks

#1) Robot Framework

Robot Framework is an open-source test automation framework that can be used for robotic process automation (RPA).

With RPA, you will be able to define the inputs and check whether the test automation framework is working as intended or not.

Features of Robot Framework

• The openness of the tools enables testers and developers to integrate it with many tools making it more efficient.
• Robot Framework is free to use without any license cost.
• The tool has a very easy syntax and uses keywords
• The capability of the Robot framework can be extended using Python libraries
• Robot Framework has huge community support and owing to the same they have a rich ecosystem
• Expandability is one of the core reasons behind the creation of the tool. This means the capability of the tool can be enhanced in many ways.
• It’s very user friendly to report data

Disadvantages of using Robot Framework

• Hard to maintain
• HTML customization is very less
• Running parallel tests is very hard
• Some issues found by the framework will be very difficult to debug
• strict indentation rules

#2) PyTest

Easy to write tests are the forte of PyTest. However, the tool can be used to scale tests for complex functionalities as well as using libraries. But keep in mind that you require Python 3.7+ or PyPy3 to operate PyTest.

Example:

# content of test_sample.py
def inc(x):
return x + 2
def test_answer():
assert inc(3) == 7
To execute it:
$ pytest
=========================== test session starts ============================

platform linux -- Python 3.x.y, pytest-7.x.y, pluggy-1.x.y
rootdir: /home/sweet/project
collected 1 item
test_sample.py F [100%]

================================= FAILURES =================================

_______________________________ test_answer ________________________________

def test_answer():
> assert inc(3) == 7
E assert 4 == 7
E + where 4 = inc(3)
test_sample.py:6: AssertionError
========================= short test summary info ==========================

FAILED test_sample.py::test_answer - assert 4 == 7

============================ 1 failed in 0.12s =============================

Features of PyTest

• You can grab detailed assert statements on failing ones
• Be it API tests or complex database testing the tools support both
• Can be integrated with the GitHub issue tracker
• Allows modular fixtures for managing any kind of test resources
• Can be used to run unit tests
• Over 800+ plugins are available
• Highly secure architecture

Disadvantages of using PyTest

• PyTest uses special routines to write tests which makes the tool very difficult to integrate with other frameworks. You have to rewrite the entire code for that to happen.

#3) PyUnit (Unittest)

PyUnit (Unittest) was created based on JUnit. So it’s obvious that both have similarities. PyUnit (Unittest) supports

• Test automation frameworks
• Sharing shutdown and setup code used for testing
• Aggregating tests into collections
• The independence that tests require sometimes from the reporting framework.

PyUnit (Unittest) also supports the important concepts of object-oriented language such as

• Test fixture
• Preparing test cases
• Preparing test suites for group execution
• Test runner that’s used to execute tests

Basic example:

import unittest
class TestStringMethods(unittest.TestCase):
def test_upper(self):
self.assertEqual('foo'.upper(), 'FOO')
def test_isupper(self):
self.assertTrue('FOO'.isupper())
self.assertFalse('Foo'.isupper())
def test_split(self):
s = 'hello world'
self.assertEqual(s.split(), ['hello', 'world'])
# check that s.split fails when the separator is not a string
with self.assertRaises(TypeError):
s.split(2)
if __name__ == '__main__':
unittest.main()

Advantages of PyUnit (Unittest)

• The unit test module can be operated using the command line

• Each test module can be specified using the file path
• Tests can be run with more details
• If the tests files are stored as modules and packages PyUnit (Unittest) can detect the tests very easily, much faster than any other tools in existence
• Test case set-up code can be factored out using setUp(), so that the test framework can call it for every test

Disadvantages of PyUnit (unittest)

• snake_case( python) and camelCase (JUnit) can be very confusing
• The intent of the test code can be unclear

#4) Behave

Behave is a BDD (behavior-driven development) tool that can be used to write tests in natural language. Owing to the same, test case written using Behave is easily readable. The tool used Gherkin language for feature file and scenarios creation

Advantages/features of 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

Cons of using Behave

• Even though PyCharm integration is there it’s not supported by the Pycharm community

• Suites best only for backbox testing
• Parallel test execution is one of the mandatory aspects of test automation. Behave does not support that. However, it’s possible using third-party software integration

#5) Nose 2

Nose 2 is a successor to Nose. This tool is simpler terms is PyUnitest with plugins. Nose2 is not meant for people who are new to Python testing frameworks. Since Nose2 is based on Unittest it’s better that you start from a standard Python library and then add Nose 2 on top to add more value

Advantages of Nose 2

• Nose2 provides more testing configuration than that of PyUnittest

# in test_fancy.py
from nose2.tools import params
@params("Sir Bedevere", "Miss Islington", "Duck")
def test_is_knight(value):
assert value.startswith('Sir')

Nose2 runs the tests like this

$ nose2 -v --pretty-assert
test_fancy.test_is_knight:1
'Sir Bedevere' ... ok
test_fancy.test_is_knight:2
'Miss Islington' ... FAIL
test_fancy.test_is_knight:3
'Duck' ... FAIL
======================================================================
FAIL: test_fancy.test_is_knight:2
'Miss Islington'
----------------------------------------------------------------------

Traceback (most recent call last):
File "/mnt/ebs/home/sirosen/tmp/test_fancy.py", line 6, in test_is_knight
assert value.startswith('Sir')
AssertionError
>>> assert value.startswith('Sir')
values:
value = 'Miss Islington'
value.startswith = <built-in method startswith of str object at 0x7f3c3172f430>
======================================================================

FAIL: test_fancy.test_is_knight:3
'Duck'
----------------------------------------------------------------------

Traceback (most recent call last):
File "/mnt/ebs/home/sirosen/tmp/test_fancy.py", line 6, in test_is_knight
assert value.startswith('Sir')
AssertionError
>>> assert value.startswith('Sir')
values:
value = 'Duck'
value.startswith = <built-in method startswith of str object at 0x7f3c3172d490>
----------------------------------------------------------------------

Ran 3 tests in 0.001s
FAILED (failures=2)

• Nose 2 supports all the latest Python versions
• Nose 2 loads all the tests first then start 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, unittest Test, test functions, and in Case subclasses
• All the configuration can be done by Config files.
• Nose2 has the ability to invade the test running process with nose2.collector.collector(). This is actually a test runner hidden inside each test case. Even though it runs tests as normal Nose2, it has the ability to run its own test runner and tests.

Disadvantages of Nose2

• Hard to finish tests when the class method is called upon
• Hard to trace out why sessions are open and where to close it

Other Framework of Python used in test automation

• UI automation
• selenium webdriver
• Toolium
• webdriver_manager
• gauge
• Playwright
• splinter
• Mailosaur
• wtframework
• Needle
• PyPOM
• pypom_form
• Golem
• Pylenium.io

Mobile Automation Frameworks

• Appium
• Airtest
• ATX
• robotframework-ioslibrary
• robotframework-androidlibrary
• robotframework-ioslibrary

Rest API Automation testing Frameworks

• gabbi
• Schemathesis
• siesta
• play_requests
• Rester
• pyresttest

Conclusion

We know for a fact that there are bazillion tools out there. What we have listed are the most robust and most-used tools in testing.

However, it’s always better to study the requirement extensively and then choose a tool that fits your budget.

Automation is indeed a painstaking process to set up. Even though it saves a lot of time, later on, maintaining it can be a hefty task. That’s the reason why we can help you

Testscenario has the power to bring the best automation framework to your development pipeline so that you get a product that has minimal errors. All you have to do is reach out to us. We are always ready to help.