Training course · GitHub Copilot · Test Automation & QA

GitHub Copilot for Test Automation and QA

Use GitHub Copilot professionally: completions, chat, agent mode, the coding agent for issues and PRs, code review and enterprise policies.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“GitHub Copilot for Test Automation and QA” is a hands-on course for roles such as Test automation engineers and SDETs, QA engineers and test managers and Developers responsible for test suites. Rather than learning GitHub Copilot in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Test Automation & QA.

GitHub Copilot has grown from simple code completion into a broad AI platform that supports the entire development lifecycle. In the editor you work with inline suggestions, next edit suggestions and Copilot Chat; in agent mode Copilot handles tasks across multiple files and runs commands. On GitHub itself, the coding agent picks up assigned issues and delivers pull requests. In this two-day course you will learn how to use each of these building blocks deliberately and fit them cleanly into your existing development process.

Beyond hands-on editor work, the course covers control and quality. Custom instructions and prompt files give Copilot the project knowledge it needs, MCP adds further tools, and Copilot code review complements human reviewers. For those responsible for teams and organisations, we look at policies at organisation and enterprise level, content exclusions, model availability and usage reporting. Together, these lay the groundwork for a secure, transparent and genuinely effective Copilot rollout across the entire company.

Quality assurance is under pressure: release cycles are getting shorter, applications more complex, and AI-assisted coding produces more code in less time – all of which needs testing. Test automation engineers and QA professionals therefore benefit particularly from AI assistants and agents. These tools derive test cases from user stories and acceptance criteria, write Playwright or Cypress tests with robust selectors, generate API tests from OpenAPI specifications, analyse flaky tests and build test data generators. At the same time the role is shifting: QA becomes the function that systematically safeguards AI-generated code too. This course teaches you to use AI for test design, automation and failure analysis and to adapt your test strategy to the new pace of development.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Test automation engineers and SDETs
  • QA engineers and test managers
  • Developers responsible for test suites
  • Business testers moving into automation

Typical challenges we address

  • Unstable end-to-end tests with timing issues and fragile selectors
  • High effort for test case creation, maintenance and test data
  • Gaps in coverage of APIs, edge cases and failure scenarios
  • Growing volumes of AI-generated code that need targeted safeguarding

What you will be able to do

  • You use Copilot completions, chat and agent mode purposefully for your everyday development tasks.
  • You capture project knowledge in custom instructions and prompt files to improve output quality.
  • You delegate suitable issues to the coding agent and review its pull requests properly.
  • You integrate Copilot code review sensibly into your existing review process.
  • Derive test cases systematically from requirements with AI and automate them in Playwright, Cypress or API tests.
  • Analyse flaky tests and coverage gaps with AI support and fix them for good.
  • Adapt your test strategy to AI-assisted development and safeguard generated code in a targeted way.

Course outline

Day 1

Module 1 Copilot in the editor: completions and chat
  • Accepting, rejecting and steering inline and next edit suggestions efficiently
  • Using Copilot Chat for explanations, debugging and test generation
  • Setting context precisely with files, selections, workspace and chat variables
  • Model selection and differences between VS Code, Visual Studio and JetBrains IDEs
Module 2 Agent mode and extensions
  • Planning multi-step tasks in agent mode and executing them under control
  • Feeding terminal commands, test runs and error output into the agent loop
  • Connecting MCP servers for additional tools and data sources
  • Reviewing changes, accepting them partially or rolling them back
Module 3 Project knowledge through custom instructions
  • Writing repository-wide instructions on architecture, style and testing
  • Creating path-specific instructions and reusable prompt files
  • Combining personal and organisation-wide guidance sensibly
  • Refining instructions continuously based on output quality
Module 4 Coding agent and code review on GitHub
  • Writing issues the coding agent can implement reliably
  • Reviewing, commenting on and iterating over the agent’s pull requests
  • Using Copilot code review as an additional check in the PR process
  • Configuring the agent’s development environment with setup steps and Actions

Day 2

Module 5 Administration, policies and security
  • Licence assignment and policies at organisation and enterprise level
  • Content exclusions, model availability and public code filters
  • Using audit logs and usage metrics to steer adoption
  • Clarifying security risks, secrets and accountability for AI-generated code
Module 6 Practice lab: Test Automation & QA
  • Deriving test cases from requirements
  • Writing Playwright and Cypress tests
  • Analysing flaky tests
  • API tests from specifications
  • Generating test data
  • Coverage and mutation testing
Module 7 Data protection, quality & transfer to daily work
  • Test data should be synthetic or effectively anonymised, as production data containing personal information must not be used unprotected in test environments or prompts (GDPR). Credentials for test systems belong in secret stores rather than test code, and in regulated sectors test results must be traceable and documented, for example as evidence under NIS2 or the Cyber Resilience Act.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Deriving test cases from requirements

The assistant turns user stories and acceptance criteria into structured test cases with equivalence classes, boundary values and negative scenarios, optionally as Gherkin scenarios for behaviour-driven development.

Writing Playwright and Cypress tests

The AI writes end-to-end tests with role-based selectors, page objects or fixtures and proper waiting instead of fixed delays, and runs them iteratively until they are stable.

Analysing flaky tests

The agent examines test history, traces, screenshots and logs, identifies causes such as race conditions, test interdependencies or unstable test data, and proposes lasting fixes.

API tests from specifications

OpenAPI descriptions are turned into API tests for status codes, schemas, authentication and error cases, plus contract tests that surface interface changes between teams early.

Generating test data

The AI builds generators for realistic synthetic test data, factories and seed scripts that deliberately cover edge cases without relying on real personal data. Privacy issues in test environments are avoided.

Coverage and mutation testing

Coverage reports and mutation testing results are analysed, untested paths identified and missing tests added in a targeted way to increase the real value of the suite.

Possible hands-on projects

Flaky test report

A script that merges JUnit XML results from multiple CI runs, detects unstable tests and produces a prioritised list with failure patterns.

Test data generator

A CLI that creates realistic synthetic records, including edge cases, from a database schema or JSON schema. Data volume and distribution are fully configurable.

E2E suite for a demo app

A Playwright test suite with page objects, visual comparisons and CI integration, built by an AI agent for an existing web application.

Prerequisites

You develop software regularly, work with Git and GitHub issues and pull requests, and use a modern IDE. Experience in software testing or test automation and a working knowledge of at least one programming language such as JavaScript, TypeScript, Python or Java.

Technical requirements

Your own laptop with VS Code or a JetBrains IDE, the Copilot extension installed and a GitHub account with an active Copilot licence; the coding agent requires a suitable plan.

Data protection & compliance

Test data should be synthetic or effectively anonymised, as production data containing personal information must not be used unprotected in test environments or prompts (GDPR). Credentials for test systems belong in secret stores rather than test code, and in regulated sectors test results must be traceable and documented, for example as evidence under NIS2 or the Cyber Resilience Act.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Stuttgart Request
  • Tue, 19 January 2027 – Wed, 20 January 2027 Berlin Request
  • Thu, 28 January 2027 – Fri, 29 January 2027 Online Request
  • Wed, 3 February 2027 – Thu, 4 February 2027 Munich Request
  • Thu, 11 February 2027 – Fri, 12 February 2027 Online Request

Frequently asked questions

What is the difference between agent mode and the coding agent?

Agent mode runs inside your IDE: you work interactively with Copilot, see every step and approve commands. The coding agent, by contrast, works autonomously on GitHub once you assign it an issue, uses its own execution environment and opens a pull request that you then review just as you would a contribution from a colleague.

How can we make sure Copilot complies with our company policies?

At organisation and enterprise level you can enable or block specific features, models and extensions. Content exclusions keep sensitive files out of the context, and audit logs make usage traceable. During the course we show how to align these controls with data protection, information security requirements and your internal coding standards.

Is the course worthwhile if we already use Copilot?

Yes, especially then. Many teams rely on completions alone and barely touch chat, agent mode, custom instructions or the coding agent. The course shows how to use these features in a structured way, improve results with project knowledge and avoid common pitfalls. Experienced users benefit most from the sections on review and governance.

Do I need programming skills for this course?

A working knowledge of a programming language helps, since you should be able to read, understand and adapt generated tests. Business testers with little coding experience still benefit greatly, because the AI explains code and supports the move into automation. The exercises are designed to accommodate different levels of prior knowledge.

Will AI replace manual and exploratory QA?

No. AI automates recurring tasks such as test case creation, scripting and failure analysis, freeing up time for exploratory testing, risk assessment and acceptance. With AI-generated code in particular, the critical judgement of experienced QA professionals becomes more important, not less.

How much does “GitHub Copilot for Test Automation and QA” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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