Testing teams are under pressure to move faster, improve coverage, create better test data, and validate quality earlier in the delivery lifecycle. AI can help turn that pressure into practical progress.

In this session, we will explore how GitHub Copilot and modern engineering practices can help quality teams create meaningful test cases, generate realistic test data, write Playwright tests, create JMeter scripts, and automate Azure Load Testing through GitHub Actions. Along the way, we will build a functional load testing workflow from the ground up, connecting the pieces needed to run tests as part of a repeatable delivery process.

The session connects real testing scenarios with core concepts from the Azure Load Testing and GitHub Actions learning path, including workflow automation, secrets, service authentication, JMeter-based load tests, YAML configuration, fail criteria, auto-stop rules, and pipeline-based performance validation.

Attendees will see how AI can act as a practical testing partner, helping teams move from manual testing ideas and scattered scripts to automated, cloud-ready quality checks. The focus is not just generating test code, but using AI to build a stronger testing workflow, from functional validation to performance testing to CI/CD automation.

Key Takeaways
- Use AI to create stronger test coverage. Learn how AI can help generate test cases, identify edge cases, create test data, and turn requirements into practical testing scenarios.

- Create modern UI and performance tests faster. See how AI can help write Playwright tests for browser automation and JMeter scripts for load and performance testing.

- Build load testing into the delivery pipeline. Learn how GitHub Actions, Azure authentication, YAML configuration, fail criteria, and auto-stop rules come together to deploy and run a functional load testing workflow.