python automated testing framework and tutorial

python automated testing framework and Tutorials: The Future of Quality Assurance

In today’s fast-paced digital age, software development and deployment speed have become paramount. However, this doesn’t mean we can compromise on quality. Enter Python automation testing, an efficient and powerful solution that guarantees both speed and quality. This article dives deep into the world of Python automation testing tutorials, making the learning curve smoother for developers and quality assurance professionals.

1. What is Python Automation Testing?

Automation testing is the process of using software tools, scripts, and techniques to perform testing tasks without human intervention. Python, known for its simplicity and readability, is a favorite among developers for automation testing. Its vast library support, versatility, and community backing make it an ideal choice for test automation.

2. Why Choose Python for Automation Testing?

a. Simplicity: Python’s syntax is clear and concise, making test scripts more maintainable. b. Library Support: With frameworks like Selenium, PyTest, and Robot Framework, Python offers powerful tools for web testing, API testing, and more. c. Community: Python boasts a massive community, ensuring that any roadblocks you face will have a solution online. d. Cross-platform: Python scripts can run on various platforms, ensuring consistent testing environments.

3. Python Automation Testing Tools & Frameworks:

a. Selenium with Python: Ideal for web applications, Selenium provides capabilities to automate browsers and web app testing. b. PyTest: A more Pythonic approach to testing, PyTest offers fixtures and parameterized testing. c. Robot Framework: A keyword-driven approach that’s ideal for acceptance testing.

4. Tips for Effective Python Automation Testing:

a. Prioritize tests: Not every test needs to be automated. Focus on repetitive and high-risk tests. b. Maintain your scripts: As your application evolves, so should your test scripts. c. Parallelize tests: Speed up testing by running multiple tests concurrently. d. Continuous Integration: Integrate with tools like Jenkins or Travis CI to automate the testing process in your development pipeline.

5. Accessing Python Automation Testing Tutorials:

Dive into detailed tutorials, from beginner to advanced levels:

a. Official Documentation: Python’s official documentation and that of tools like Selenium and PyTest. b. Online Courses: Websites like Udemy, Coursera, and Pluralsight offer in-depth courses. c. YouTube: A plethora of channels dedicate content to Python automation testing. d. Blogs: Personal and corporate blogs often provide unique insights and real-world examples.

Python Automation Testing Tools

Python, being a versatile programming language, offers a plethora of tools for automation testing. Below are some of the most popular ones:

1. Selenium: A browser automation tool, primarily used for automating web applications for testing purposes. With Python bindings, you can utilize Selenium for browser automation with ease.
2. PyTest: A mature framework for Python, PyTest simplifies the process of writing simple as well as scalable test cases.
3. Robot Framework: A keyword-driven testing framework, it’s extensible and can be integrated with Selenium for web testing.
4. unittest (or PyUnit): Python’s standard library offers ‘unittest’ as its testing framework.
5. nose2: Successor to the now-inactive ‘nose’, it extends unittest to make testing easier.
6. Tox: It is an automation tool used for setting up virtual environments for tests.
7. Behave: A behavior-driven development (BDD) tool, it enables writing tests in a natural language style.
8. Lettuce: Similar to ‘Behave’, it is also used for BDD.
9. Locust: An open-source load testing tool, it lets you write tests in pure Python.
10. TestComplete: Though not limited to Python, it offers PythonScript for scripting tests.

Python Automated Testing Frameworks – Explained

Of all the tools mentioned, some prominent ones are full-fledged testing frameworks. Let’s delve into details of a couple of them:

1. PyTest

• Introduction: PyTest is one of the most popular testing frameworks in the Python community. It is known for its advanced assertion introspection, meaning that when tests fail, PyTest provides detailed information about the failure, making debugging easier.
• Features:
  • Fixtures: PyTest fixtures are a powerful feature for setting up and tearing down prerequisites for tests.
  • Parameterization: Allows one to define multiple sets of arguments for a test. Thus, you can run the same test logic with different input values.
  • Plugins: PyTest supports plugins, and there are many available in the ecosystem. You can also easily write your own.
  • Skip Tests: PyTest allows conditional test execution, which means you can skip certain tests based on specific conditions.
  • Parallel Execution: With the pytest-xdist plugin, tests can be executed in parallel, reducing overall test execution time.

2. Robot Framework

• Introduction: Robot Framework is a generic test automation framework that follows a keyword-driven testing approach. This makes it easier for non-programmers to write and understand test cases.
• Features:
  • Readable Syntax: Test cases are written using natural language keywords.
  • Extensible: Can be extended with Python and Java libraries.
  • Web Testing: Integration with Selenium WebDriver makes it perfect for web application testing.
  • Rich Reporting: Robot Framework has a built-in mechanism to generate detailed logs and reports post test execution.
  • Tabular Data: It allows using tabular test data, which can be easier to read and maintain.

Both PyTest and Robot Framework have their own merits. While PyTest is more Pythonic and offers better integration with the Python ecosystem, Robot Framework excels in cases where tests need to be understood by non-developers due to its natural language support. The choice between them largely depends on the specific requirements of a project.

Example for PyTest and unittest Implementation

The art of testing in Python has been simplified and enhanced with frameworks like PyTest and unittest. Beginners often start by searching for resources like the “Pytest tutorial w3schools” or the “Unittest python tutorial w3schools”. But let’s delve deeper into both these frameworks, highlighting their distinct features and capabilities.

PyTest: A Modern, Feature-Rich Framework

Introduction: PyTest is a modern testing framework that’s gained popularity for its simplicity and powerful features. It’s no wonder that “Pytest tutorial for beginners” is a trending search topic.

Key Features & Code Example:

• Simple Syntax: PyTest doesn’t require a lot of boilerplate code. Just create a function and use the assert statement.

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def test_example():
assert 2 + 3 == 5

• Rich Documentation: The “pytest documentation” is comprehensive, covering everything from basic to advanced use cases. You can also find supplementary resources from platforms like “pytest tutorialspoint” or download the “pytest tutorial pdf” for offline reading.
• Parameterized Testing: Allows running the same test function with multiple sets of arguments.

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import pytest

@pytest.mark.parametrize(“input,expected”, [(1, 2), (4, 5)])
def test_addition(input, expected):
assert input + 1 == expected

• Setup and Installation: Before diving in, ensure you “install pytest”. Use the command:

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pip install pytest

Searching for “pytest example” or “pytest tutorial python 3” will provide more insights and samples.

unittest: Python’s Built-In Testing Library

Introduction: unittest, as the name suggests, is the built-in testing library for Python. Beginners looking for a “unittest python tutorial” will be pleased to find that it’s an integral part of Python’s standard library.

Key Features & Code Example:

• Structure: unittest requires a more structured approach using classes.

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import unittest

class TestMathOperations(unittest.TestCase):

def test_addition(self):
    self.assertEqual(2 + 3, 5)

• Parameterized Testing: With the help of external packages, parameterized tests are possible.

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from parameterized import parameterized

class TestMath(unittest.TestCase):

@parameterized.expand([(1, 2), (4, 5)])
def test_addition(self, input, expected):
    self.assertEqual(input + 1, expected)

• Advanced Features: Advanced testers can leverage functionalities like “python unittest setupclass” for setup routines specific to the class.
• Resources: Beginners can benefit from the “Unittest python tutorial for beginners”, and more advanced users can explore “python unit test framework” specifics.

Pytest vs unittest: While searching “pytest vs unittest”, testers will find that PyTest offers a more pythonic approach with its simpler syntax. However, unittest is robust and a standard part of Python.

For those who are starting, “python testing tutorial for beginners” will provide a holistic view of testing in Python, covering both PyTest and unittest. Dive deeper by looking at “unittest python example” or explore “python unittest parameterized” to harness the power of parameterized tests.

In summary, whether you’re drawn towards the extensive capabilities of PyTest or the standardized structure of unittest, Python offers robust solutions for all your testing needs.

pytest example

MAIN-PIPELINE.yml

name: $(BuildID)

# Variable set to configure the pipeline, all of them are in libraries.
variables:

# Branches from this pipeline will be triggered
trigger:
  - develop
  - premaster
  - master

# Schedules defined to run daily
schedules:
  - cron: 0 20 * * 1,2,3,4,5
    branches:
      include:
        - develop
    always: true
  - cron: 0 21 * * 1,2,3,4,5
    branches:
      include:
        - premaster
    always: true
  - cron: 0 22 * * 1,2,3,4,5
    branches:
      include:
        - master
    always: true

resources:
  repositories:
    - repository: self
      type: git
      ref: master

jobs:
  # job to run CIO DevSecOps Standards tasks
  - job: Job_1
    pool:
      vmImage: ubuntu-20.04
    steps:
      - checkout: self

      # set the python version
      - task: UsePythonVersion@0
        displayName: Use Python 3.8
        inputs:
          versionSpec: 3.8

      # install all python dependencies for whole project
      - task: Bash@3
        displayName: Install Python dependencies
        inputs:
          targetType: inline
          script: echo "installing all dependencies..."

            pip install --upgrade pip

            find . -iname "requirements.txt" -path "./*" | xargs -n 1 pip install -r

      # task to update table libraries only for branh - environment where pipeline is running
      - task: Bash@3
        displayName: Update table libraries
        condition: and(succeeded(), in(variables['Build.Reason'], 'IndividualCI', 'BatchedCI', 'Manual', 'Schedule'))
        continueOnError: True
        inputs:
          targetType: inline
          script: python ./libraries/update_variable_group.py \

            --branch $(Build.SourceBranch) \

            --pat $(CLOUDFUNCTION_AZDEVOPS_PAT)
      - template: PYTHONunittest.yml
        parameters:
          path_to_check: terraform/PATHtotemplate
          display_name: TF

PYTHONunittest.yml

steps:
- bash: |
    pytest --cov-report html:reports/cov_html --cov-report xml:reports/coverage.xml --cov=./ --junitxml=reports/junit.xml
  workingDirectory: './${{parameters.path_to_check}}'
  displayName: '[Unit Test] for ${{parameters.display_name}}'
  enabled: ${{parameters.enabled}}

- task: PublishTestResults@2
  displayName: 'Publish Test Results reports/junit.xml'
  inputs:
    testResultsFiles: ${{parameters.path_to_check}}/reports/junit.xml
  continueOnError: true
  enabled: ${{parameters.enabled}}

- task: PublishCodeCoverageResults@1
  displayName: 'Publish code coverage from reports/coverage.xml'
  inputs:
    codeCoverageTool: Cobertura
    summaryFileLocation: ${{parameters.path_to_check}}/reports/coverage.xml
  continueOnError: true
  enabled: ${{parameters.enabled}}

Conclusion:

Python automation testing is redefining the quality assurance landscape. With the combination of speed and accuracy, it ensures that software products are reliable and market-ready. By leveraging Python’s capabilities, testers can step up their game, delivering excellence without compromising on development velocity. Dive into the world of Python automation testing tutorials today and embrace the future of software testing.