Training course · GitHub Copilot · Java & Spring

GitHub Copilot for Java and Spring Developers

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 Java and Spring Developers” is a hands-on course for roles such as Java back-end developers using Spring Boot or Jakarta EE, Teams modernising legacy Java applications and Software architects for enterprise and microservice landscapes. Rather than learning GitHub Copilot in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Java & Spring.

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.

Java and Spring carry business-critical systems in many organisations: banking back ends, ERP integrations, public-sector applications and large microservice estates. These systems have often grown over many years, and modernisation never stops. AI coding assistants and agents change the heaviest routine work in particular: major Spring Boot upgrades including the javax-to-jakarta switch, JUnit and Mockito tests for poorly covered code, refactorings towards records and modern language features, and analysis of stack traces and Hibernate issues. Clear guardrails are essential: build tool, module structure, architecture rules and test strategy all need to be in context. This course shows you how to use agents productively and in a controlled way in Maven or Gradle projects.

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?

  • Java back-end developers using Spring Boot or Jakarta EE
  • Teams modernising legacy Java applications
  • Software architects for enterprise and microservice landscapes
  • Developers in banking, insurance and the public sector
  • Tech leads responsible for upgrades and technical debt

Typical challenges we address

  • Laborious framework and JDK upgrades with numerous breaking changes
  • Large legacy codebases with low test coverage and tight coupling
  • Hard-to-analyse performance issues in JPA, Hibernate and transactions
  • Heavy boilerplate in configuration, mapping and interface code

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.
  • Use AI agents for Spring Boot upgrades, refactorings and test generation in Maven and Gradle projects.
  • Diagnose Hibernate, performance and production issues faster with AI support.
  • Embed architecture rules and quality standards so that generated Java code adheres to them reliably.

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: Java & Spring
  • Driving a Spring Boot upgrade
  • Generating JUnit and Mockito tests
  • Refactoring legacy code
  • Analysing JPA and Hibernate issues
  • REST APIs and interfaces
  • Making sense of stack traces and production errors
Module 7 Data protection, quality & transfer to daily work
  • Java systems often run in regulated sectors: agree with information security and data protection which code may be sent to external models, and take requirements such as DORA, NIS2 or national banking IT rules into account. Generated code and new Maven dependencies should be checked for licences and known vulnerabilities and recorded in an SBOM.
  • 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

Driving a Spring Boot upgrade

The agent analyses dependencies, applies OpenRewrite recipes, switches javax packages to jakarta, adapts deprecated configuration and security setups and iterates over build and tests until the application is green again.

Generating JUnit and Mockito tests

Services and controllers get unit tests with JUnit and Mockito plus slice tests using WebMvcTest or DataJpaTest. The AI deliberately raises coverage on critical paths and documents test assumptions.

Refactoring legacy code

Large classes are split up, DTOs turned into records, null checks replaced with Optional and streams used where they help. Existing tests safeguard behaviour at every step.

Analysing JPA and Hibernate issues

The AI reads SQL logs and stack traces, spots N+1 queries, LazyInitializationExceptions and poor fetch strategies, and proposes entity graphs, projections or index changes. Each optimisation is backed by SQL logs.

REST APIs and interfaces

OpenAPI specifications are turned into controllers, DTOs, MapStruct mappers and Bean Validation, complete with consistent error handling via ControllerAdvice and integration tests. Existing package and naming conventions are respected.

Making sense of stack traces and production errors

Long stack traces, thread dumps and log excerpts are summarised, the likely root cause is narrowed down and a reproducing test case plus fix is proposed.

Possible hands-on projects

Spring Boot microservice

A REST service with Spring Data JPA, Flyway migrations, Bean Validation, Actuator endpoints and Testcontainers tests, developed entirely by an AI agent from a specification.

Dependency report for Maven and Gradle

A command-line tool that analyses build files, lists outdated and vulnerable libraries and describes upgrade paths along with expected breaking changes.

Architecture rule checks

An ArchUnit test suite that automatically enforces your project’s layering rules, package dependencies and naming conventions and runs in the CI pipeline.

Prerequisites

You develop software regularly, work with Git and GitHub issues and pull requests, and use a modern IDE. In-depth Java knowledge and practical experience with Spring Boot or Jakarta EE, Maven or Gradle and Git.

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

Java systems often run in regulated sectors: agree with information security and data protection which code may be sent to external models, and take requirements such as DORA, NIS2 or national banking IT rules into account. Generated code and new Maven dependencies should be checked for licences and known vulnerabilities and recorded in an SBOM.

Suggested dates

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

  • Mon, 23 November 2026 – Tue, 24 November 2026 Online Request
  • Tue, 1 December 2026 – Wed, 2 December 2026 Augsburg Request
  • Tue, 15 December 2026 – Wed, 16 December 2026 Online Request
  • Tue, 12 January 2027 – Wed, 13 January 2027 Kaiserslautern Request
  • Thu, 28 January 2027 – Fri, 29 January 2027 Online Request
  • Wed, 10 February 2027 – Thu, 11 February 2027 Bremen 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.

Can an AI agent carry out a major Spring Boot upgrade on its own?

An agent can take on much of the mechanical work – package switches, configuration changes and iteratively fixing compile and test failures – especially when combined with OpenRewrite. Architecture decisions, functional sign-off and testing in realistic environments remain the team’s job. On the course you practise this workflow on a sample project.

Is AI-assisted coding allowed in regulated environments?

In principle yes, but with clear rules. What matters is the choice of provider and operating model, contractual commitments on data use, approval processes and traceable reviews. We discuss how banks, insurers and public authorities typically approach this and which questions you should settle with your information security team.

How much does “GitHub Copilot for Java and Spring Developers” 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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