Jmeter Performance testing framework done with Chat GPT

This is a very basic material on how to create a testing framework with Chat GPT and Jmeter.

In this concise and focused course, I’ll guide you through the entire process of setting up a performance testing framework, step by step. Here’s what you’ll learn:

1. Creating a Test Plan: You’ll start by understanding the fundamentals of JMeter and how to create an effective test plan. We’ll cover the key components and configurations needed to structure your tests accurately.
2. Adding Users: Learn how to simulate real user behavior by adding users to your test scenarios. We’ll explore various user profiles and demonstrate how to configure JMeter for different user loads.
3. Adding Test Data: Discover the importance of realistic test data and how to incorporate it into your performance tests. We’ll show you how to efficiently manage and manipulate data for thorough testing.
4. Adding Randomness: Understand the significance of randomness in performance testing and how to introduce it into your test scenarios. You’ll learn techniques to create dynamic and unpredictable test conditions.
5. Interpreting Results: Dive into result analysis and learn how to extract valuable insights from your test runs. We’ll cover metrics, graphs, and best practices for interpreting JMeter results effectively.
6. Integration with CI/CD and GitHub: Take your performance testing framework to the next level by integrating it seamlessly with your CI/CD pipeline and GitHub. Automate your testing process and ensure continuous performance monitoring.

By the end of this course, you’ll have a basic grasp of building a basic JMeter performance testing framework and integrating it into your CI/CD workflow. You’ll be equipped with the skills needed to continue your journey to meaningful performance tests, identify bottlenecks, and optimize your applications for peak performance.