Training course · GitHub Copilot · Legacy Modernisation

GitHub Copilot for Legacy Modernisation

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 Legacy Modernisation” is a hands-on course for roles such as Developers maintaining COBOL, PL/I or RPG systems, Java and .NET teams with ageing monoliths and outdated frameworks and Software and enterprise architects in modernisation programmes. Rather than learning GitHub Copilot in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Legacy Modernisation.

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.

Many business-critical systems still run on mainframe COBOL, ageing Java EE applications or .NET Framework solutions whose original developers moved on long ago. AI coding assistants and agents make these codebases far more approachable: they explain programme logic, reconstruct business rules, generate characterisation tests and support stepwise migration to modern languages, frameworks and cloud platforms. Successful modernisation is still engineering work, though, because automated translation without understanding simply produces new legacy code. In this course you learn to apply AI along proven patterns such as strangler fig, branch by abstraction and anti-corruption layers, lock down existing behaviour with tests before every change and base modernisation decisions on a solid analysis of what you already have.

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?

  • Developers maintaining COBOL, PL/I or RPG systems
  • Java and .NET teams with ageing monoliths and outdated frameworks
  • Software and enterprise architects in modernisation programmes
  • IT managers and project leads responsible for migrations
  • Application modernisation and cloud migration consultants

Typical challenges we address

  • Business rules are buried undocumented in code and the original developers’ knowledge is gone
  • Missing automated tests make every change risky
  • Outdated frameworks, unpatched libraries and proprietary runtimes
  • Big-bang migrations fail on scope, data migration and parallel operation

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 to understand legacy systems, document business rules and make dependencies visible.
  • Protect existing behaviour with generated characterisation tests before migrating or refactoring code.
  • Plan and steer incremental modernisation using proven patterns instead of risky big-bang cutovers.

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: Legacy Modernisation
  • Extracting business rules
  • Mapping dependencies
  • Generating characterisation tests
  • Framework upgrades
  • Translating and refactoring code
  • Designing a strangler fig architecture
Module 7 Data protection, quality & transfer to daily work
  • Legacy systems often process customer, financial or insurance data: you learn to sanitise code and test data before using AI, clarify licensing and copyright questions around translated code and address the requirements of the GDPR, DORA and sector regulators for changes to critical systems.
  • 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

Extracting business rules

Analyse COBOL paragraphs, copybooks or Java service classes, document the embedded calculation and decision logic as readable business rules and validate them together with domain experts.

Mapping dependencies

Reconstruct call graphs, JCL job chains, database access and interfaces from source code and visualise them as diagrams to find sensible seams for stepwise replacement.

Generating characterisation tests

Capture current behaviour with golden master and approval tests, derive anonymised test data from production patterns and build a safety net before any migration begins.

Framework upgrades

Plan migrations from Java EE to Jakarta EE or Spring Boot and from .NET Framework to modern .NET, identify API breaks and apply consistent changes across many files.

Translating and refactoring code

Turn COBOL modules into idiomatic Java or C# rather than translating line by line, map data types such as packed decimals correctly and verify the result against the original with comparison tests.

Designing a strangler fig architecture

Design facades, routing layers and anti-corruption layers through which new services take over legacy functions step by step, including data synchronisation and fallback options. Each stage remains independently deployable.

Possible hands-on projects

Copybook to schema converter

A tool that reads COBOL copybooks and generates JSON schemas, Java records or SQL tables, correctly mapping decimal and REDEFINES fields.

Legacy knowledge portal

A small web app that makes analysed programmes, business rules and dependencies searchable and answers questions about the legacy system with source references.

Comparison test harness

A test harness that sends identical inputs to the old and new system, compares outputs field by field and logs discrepancies clearly.

Prerequisites

You develop software regularly, work with Git and GitHub issues and pull requests, and use a modern IDE. You have programming experience in at least one language of the legacy or target platform, such as COBOL, Java or C#, plus a basic grasp of software architecture.

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

Legacy systems often process customer, financial or insurance data: you learn to sanitise code and test data before using AI, clarify licensing and copyright questions around translated code and address the requirements of the GDPR, DORA and sector regulators for changes to critical systems.

Suggested dates

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

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Mon, 7 December 2026 – Tue, 8 December 2026 Stuttgart Request
  • Wed, 20 January 2027 – Thu, 21 January 2027 Online Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Berlin Request
  • Tue, 9 February 2027 – Wed, 10 February 2027 Online Request
  • Tue, 23 February 2027 – Wed, 24 February 2027 Munich 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 AI automatically translate a COBOL system into Java?

AI translates individual modules far faster than manual work, but a pure one-to-one translation often yields code that is hard to maintain. A better approach is to understand and test the business rules first and design the target architecture deliberately. In the course you combine AI-assisted translation with comparison tests and refactoring.

How do we handle very large codebases?

Large legacy systems do not fit entirely into a model’s context. What works is systematic inventory, per-module summaries, dependency graphs and targeted retrieval of relevant code. Agents then work module by module with a clearly defined brief. You learn to build this structure and turn the results into input for architecture decisions.

How much does “GitHub Copilot for Legacy Modernisation” 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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