Discussion on A Live Intro to Python Testing by Andrew Knight | Testμ 2023

:rocket: Want to explore Python testing hands-on? Join Andrew Knight for “A Live Intro to Python Testing.”

Skip the slides – get ready for live coding!

Learn to set up pytest and Playwright for the unit, API, and UI tests. Master Python automation and gather resources for further learning.

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If you have already registered and up for the session, feel free to post your questions in the thread below :point_down:

Here are some of the answered questions asked during the session!

What are the best practices for writing test cases in Python?

Andrew: Functional testing involves creating distinct and descriptive test names, confirming results through assertions, and encompassing diverse scenarios for thorough Python testing coverage.

What are the common challenges you face when performing automated testing in Python?

Andrew: Frequent obstacles involve managing intricate dependencies, proficiently handling test data, and maintaining uniform, isolated test environments during Python’s automated testing processes.

What are the advantages of using testing frameworks like unittest compared to writing scripts from scratch?

Andrew: Frameworks such as unittest offer integrated assertion methods, test discovery, and setup/teardown tools, simplifying test development and upkeep in contrast to building tests from the ground up. This results in more structured and effective testing suites.

In terms of extensibility and customization, how do testing frameworks like pytest enable developers to create tailored testing experiences?

Andrew: Pytest and similar testing frameworks empower developers to design personalized testing workflows through adaptable plugins, fixtures, and hooks, creating custom testing experiences that suit specific project requirements.

Shown below are the unanswered questions from the session!

What are some common challenges you face while automating tests in Python?

What are the best practices for writing test cases in Python?

What limitation you have seen in pytest compared to Jasmine or TestNG ?

Does Python work with most of the testing frameworks like Cucumber or Playwright?

Can Python be used for API testing? Which framework is commonly used for API testing in Python?

Can pytest be used to write an automation script?

What sets Python apart from other programming languages, and why is it currently the most popular choice for automation?

How to start with Python automation? What are the best tools to start with?

Can we use pytest for end-to-end integration testing?

Hi there,

If you couldn’t catch the session live, don’t worry! You can watch the recording here:

Additionally, we’ve got you covered with a detailed session blog:

Automating tests in Python is a common practice due to the language’s readability and extensive libraries. However, in my experience, there are several challenges that testers often encounter:

  1. Selecting Appropriate Testing Frameworks
  2. Managing Test Dependencies
  3. Handling Asynchronous Code
  4. Maintaining Test Data
  5. Dealing with Flaky Tests
  6. Scaling Test Automation
  7. Integrating with Continuous Integration (CI) Systems
  8. Keeping Tests Updated
  9. Writing Clear and Concise Test Cases

While automating tests in Python is highly beneficial, being aware of and prepared for these challenges can help ensure a smoother and more effective testing process. Continuous learning and staying updated on the latest testing tools and practices in the Python community is also crucial for overcoming these challenges.

Writing test cases in Python is a fundamental aspect of ensuring your code is robust and works as expected. Below are best practices I’ve consistently relied upon in my experience:

  1. Understand and Leverage Testing Frameworks
  2. Write Clear and Descriptive Test Names
  3. Keep Tests Independent and Isolated
  4. Use Mocks and Stubs Wisely
  5. Implement Setup and Teardown Procedures
  6. Write Both Positive and Negative Test Cases
  7. Practice Test-Driven Development (TDD)
  8. Maintain Test Documentation
  9. Regularly Review and Refactor Tests
  10. Automate and Integrate

Adhering to these best practices will guide you in writing test cases that are clear, maintainable, and effective in verifying your Python code’s functionality and reliability. Remember, the quality of your tests is indicative of the quality of your application, so invest the necessary time and effort into crafting thoughtful and thorough test cases.

Throughout my career, I’ve extensively used Pytest for Python projects, Jasmine for JavaScript-based applications, and TestNG for Java environments. Each of these frameworks has its strengths, but today, I’ll share some limitations I’ve experienced with Pytest when compared to Jasmine and TestNG.

  1. Language Specificity
  2. Behavior-Driven Development (BDD) Support
  3. Parallel Test Execution
  4. Assertion Syntax
  5. Configuration and Setup
  6. Integration with Development Tools
  7. Community and Ecosystem

While Pytest is a powerful and flexible testing framework for Python, it has limitations when compared to Jasmine and TestNG, especially in terms of language specificity, BDD support, parallel execution capabilities, and integration with development tools. Understanding these limitations is crucial when selecting a testing framework that best fits your project’s needs and constraints.

Drawing from my extensive experience working with Python and various testing frameworks, I can confidently affirm that Python is indeed compatible with a wide range of testing frameworks, including but not limited to Cucumber and Playwright.

Python, known for its simplicity and readability, offers a plethora of testing frameworks that cater to different testing needs and requirements. These frameworks provide support for aspects like unit testing, behavior testing, and acceptance testing, among others.

Speaking of Cucumber, while it is predominantly associated with Ruby, there is a Python variant known as Behave. Behave operates under the same principles as Cucumber, allowing developers to write tests in natural language descriptions. I have personally utilized Behave in multiple projects and found it to be an effective tool for behavior-driven development (BDD). It allows for the clear documentation of test cases in plain language, making it accessible for non-programmers as well.

Playwright, on the other hand, is a relatively newer framework designed for end-to-end testing of web applications. Although it was initially built for Node.js, the Playwright team has extended support for Python. In my professional journey, I have leveraged Playwright with Python to automate the testing of web applications across various browsers. Its ability to capture screenshots, record videos, and emulate devices makes it a powerful tool for testing modern web applications.

In addition to Behave and Playwright, Python supports a multitude of other testing frameworks. For instance, PyTest is one of the most popular ones, known for its no-boilerplate philosophy and ease of use. I have often recommended and used PyTest for projects that require a robust framework to handle complex testing scenarios due to its flexibility and extensive plugin support.

In conclusion, based on my hands-on experience, Python not only works with most testing frameworks but also provides a conducive environment for seamless testing. Its extensive library support, coupled with a vibrant community, makes it a preferred choice for developers and testers alike when working with various testing frameworks, including Cucumber (Behave) and Playwright. Whether you are looking to perform unit testing, integration testing, or end-to-end testing, Python has got you covered with a framework that suits your needs.

Python is a strong choice for API testing, a practice I’ve relied on throughout my career due to Python’s simplicity and array of testing frameworks.

For API testing in Python, the Requests library is a common and favored tool in my toolkit. It’s user-friendly, supports various HTTP methods, and simplifies sending HTTP requests, making it ideal for testing RESTful APIs. With Requests, you can easily craft and send requests to API endpoints, then analyze the responses, including status codes, headers, and body content.

Another valuable framework is pytest. Although not exclusively for API testing, I often use pytest alongside Requests for this purpose. It offers a simple syntax and detailed failure reports, aiding in both writing and debugging tests.

While Postman isn’t a Python library, it’s a notable tool for API testing. I’ve successfully used Python scripts within Postman to write tests and automate the process, combining the strengths of both.

In summary, Python, with frameworks like Requests and pytest, is not only suitable but excels in API testing. These tools have proven invaluable in my work, ensuring robust and reliable APIs. For developers and testers alike, Python’s straightforwardness and powerful testing frameworks make it a top pick for API testing.