API Performance Testing With K6, Github Copilot, and ChatGPT

About this course

Explore the frontier of REST API performance testing in this course, designed to empower you with the knowledge to use Grafana K6 for REST API testing enriched with the support of AI assistants. This course covers everything from the basics of K6 to advanced testing techniques, including how to effectively use GitHub Copilot and ChatGPT to improve your automated testing skills with Grafana K6.

Start with a warm welcome and an intriguing introduction to AI and Large Language Models. Delve into the course structure and absorb the crucial points guiding your learning path. Your adventure gets more interesting as you explore the integration of AI assistants in software engineering and testing using the capabilities of ChatGPT and GitHub Copilot. With the basics in hand, you’ll examine the source code structure, laying the groundwork for comprehensive performance testing. As you progress, the course introduces you to performance testing with K6, starting with installation and your first script execution. Each step, from executing commands on the K6 Command Line with test options flags, is enhanced by AI assistance. The curriculum expands to cover essential tools and concepts like Docker, using software for testing, and using Postman for API interactions. You'll navigate HTTP requests with and without AI support, understand K6 checks and metrics, and learn how to visualize K6 results for insightful analysis. Further refinement of your skills is achieved through lessons on thresholds, tags, and custom tags, each benefiting from AI integration. The course then shifts focus to practical application, guiding you through debugging K6 scripts, adding test data, and mastering data correlation—all with the invaluable support of AI assistants. Advanced topics introduce handling insecure requests, managing HTTP redirects, executing parallel requests, and defining custom metrics. Each module showcases the power of AI to simplify complex tasks. Groups, test lifecycle management, and environmental variables are also d

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