The power of GitHub Copilot for testers

AI in test automation

GitHub Copilot is a large language model trained on a massive volume of code – far more than any tester or developer will ever encounter. But does that make Copilot a good testing assistant? And what should you consider as the pilot in command?

In this one-day hands-on training, you’ll discover what Copilot can and cannot do in test automation. You’ll learn how to configure, guide, and use the AI as a complement to your existing testing tools and frameworks. We cover the theory behind large language models, efficient prompting of relevant test cases, writing unit and integration tests, and analyzing test results with Copilot.

The goal is to optimize your collaboration with Copilot. You’ll learn how Copilot accelerates repetitive test tasks, improves test coverage, and helps you work in a more structured way – without losing human oversight. You’ll also discover the limitations: when Copilot is trustworthy and when critical human judgment remains essential.

Our trainers, experienced test and DevOps engineers, work daily with Copilot in Azure projects. They share realistic use cases, concrete examples, and best practices so that after this training, you’ll not only understand what’s possible but also where the pitfalls are – and how to responsibly apply AI in test automation.

What you’ll learn

After a brief introduction to align on goals and experience, you’ll work through six blocks. Each block includes one or more hands-on workshops and is tailored to the group’s experience and needs.

• Fundamentals
  • Large Language Models
  • How coding copilots work
  • GitHub Copilot vs alternatives
  • GitHub Copilot configuration
  • Workshop: repository instructions
• Core features & best practices
  • Autofill and code generation
  • Chat (commands, context, personas, prompting)
  • Ask, Edit, Agent
• Use cases
  • Testing
  • Workshop: generating test code
  • ReqNRoll
  • Debugging & refactoring
  • Commit messages
  • Transpiling
  • Workshop: security testing
• In-depth
  • Additional features
  • GitHub-only features
  • Extensions (Marketplace, MCP)
  • Workshop: testing in Agent Mode
  • Common errors
• Mature usage
  • Workshop: test frameworks
  • Code quality, compliance, and security
  • Copilot in the software development lifecycle
• Future and adoption
  • The impact of GitHub Copilot
  • The future of GitHub Copilot and AI-assisted software engineering
  • Workshop: adoption plan

Test smarter, not harder

After this training, you’ll know how to use GitHub Copilot effectively in test automation. You’ll understand how AI supports repetitive testing tasks, helps identify risks, and improves software quality — without compromising on control or reliability.