Gain insights into writing and executing tests with pytest. Learn about core concepts, organizing tests, assertions, fixtures, and achieving high test coverage to enhance software quality.

pytest.tar.gz

Pytest Basic

Pytest Only

Pytest param

Pytest Cov

Pytest xdist

Pytest Coverage

Flask app

Pytest Basic-copy

Pytest debugger

selenium

test

Welcome to the pytest course! This course is designed to equip you with the knowledge and skills needed to effectively write and execute tests using pytest, a powerful testing framework for Python. Testing is crucial for software quality, and pytest is an essential tool in achieving that. 

Throughout the course, you’ll gain a solid foundation in pytest fundamentals and its core concepts, features, and capabilities. You’ll learn how to write and organize tests, use powerful assertions, manage fixtures, and leverage the extensive plugin ecosystem. Additionally, you’ll discover techniques for achieving high test coverage, writing effective test cases, and improving code quality.

By the end of the course, you’ll be able to write well-structured and effective tests using pytest, seamlessly integrate testing into your development workflow, and ensure high test coverage. This knowledge will not only make you a more proficient developer but also enhance your career prospects in the software development industry.

Mastering Unit Testing with Pytest

To ensure consistent random number generation across different runs.

To measure the model's accuracy on different subsets of the data.


To compare the model's performance against different baseline models.

What is the purpose of using a seed value when testing machine learning models?

What is the purpose of using a seed value when testing machine learning models?

To assess the model's performance on unseen data

To train the model on multiple subsets of the data and evaluate its performance

To validate the model's compatibility with different programming languages

To test the model's robustness against adversarial attacks

What is the purpose of cross-validation when testing machine learning models?

What is the purpose of cross-validation when testing machine learning models?

Load testing focuses on testing performance under normal loads, while performance testing tests the application's limits.

Load testing is used for web applications, while performance testing is used for mobile applications.

Load testing is automated, while performance testing is manual.

Load testing measures response time, while performance testing measures server load.

What is the main difference between load testing and performance testing?

What is the main difference between load testing and performance testing?

Verifying the performance of complex queries

Testing the database management system's installation process

Validating the accuracy of business logic implemented in database triggers

When testing a database, which is _not_ a typical objective?

When testing a database, which is not a typical objective?

A graphical user interface for test case design

The main scripting language used for Selenium testing

An interface to interact with web browsers programmatically

In Selenium, what does WebDriver refer to?

In Selenium, what does WebDriver refer to?

Test your grasp on the different types of testing we learned in this chapter.

Introduction

Pytest Framework

Advanced Unit Testing Techniques

Test-Driven Development with Flask

Different Types of Testing

Best Practices

Concluding Remarks

Automated Testing using Python and Selenium

Appendix

Test the Endpoints of the Blog API Using Pytest

Quiz on Different Types of Testing