Imagine this scenario: you’re a developer in 2025 tasked with modernizing a mainframe system processing millions of ATM transactions daily. You’re facing COBOL, a programming language that has persisted for over six decades. The sheer age of this technology can be daunting.
However, COBOL isn’t disappearing anytime soon. In fact, it remains essential, powering some of the world’s most critical systems within banks, insurance companies, and government agencies. The challenge? Finding developers proficient in COBOL is increasingly difficult; a significant portion – 200 billion lines – still runs these vital applications.
Fortunately, we now possess powerful tools to address this issue: GitHub Copilot and advanced AI agents are offering unprecedented support for maintaining and evolving these legacy systems. They provide a pathway to extend the lifespan of crucial infrastructure.
Meeting the Developer Modernizing COBOL with AI
I recently spoke with Julia Kordick, Microsoft Global Black Belt, who is actively modernizing legacy systems using AI techniques. Remarkably, she hasn’t learned COBOL herself! Her approach demonstrates a shift in how we handle these challenges.
Julia’s expertise lies in designing intelligent solutions, and she partnered with individuals possessing decades of domain knowledge about the legacy systems. This collaborative partnership is key to success; instead of requiring developers to become COBOL experts, they leverage AI alongside existing specialists.
When this whole idea of generative AI appeared, we were thinking about how we can actually use AI to solve this problem that has not been really solved yet.
Julia Kordick, Microsoft Global Black Belt
A Three-Step Framework for AI-Powered Legacy Modernization
Julia and her team at Microsoft have developed a systematic framework applicable to any legacy modernization project, not just COBOL. This approach leverages GitHub Copilot to streamline the process and maximize efficiency.
Step 1: Code Preparation – Reverse Engineering
A significant obstacle with legacy systems is often a lack of understanding of what the code actually does. Organizations rely on these systems but struggle to interpret their functionality.
This is where GitHub Copilot proves invaluable as an analytical tool. Instead of lengthy and expensive consultant reviews, AI can efficiently:
- Extract business logic from legacy files.
- Document the code in markdown for human review and comprehension.
- Automatically identify call chains and dependencies within the system.
- Clean up extraneous comments and historical logs, improving clarity.
- Add clarifying comments where necessary to enhance understanding of complex sections.
Essentially, GitHub Copilot generates a comprehensive analysis that significantly reduces the initial investment in understanding the legacy codebase.
For example, GitHub Copilot might generate documentation like this:
# Business Logic Analysis Generated by GitHub Copilot
## File Inventory
## Business Purpose
Customer accountStep 2: Translation and Abstraction
Following reverse engineering, the next step involves translating the COBOL code into a more modern format. However, this doesn’t always necessitate a complete rewrite in languages like Java or Python. Often, it’s about creating an abstraction layer that enables new systems to interact with the legacy code without needing deep COBOL expertise.
GitHub Copilot facilitates this process by suggesting translations and generating boilerplate code for common tasks, thereby accelerating development. Furthermore, it helps identify opportunities to refactor the existing COBOL code into more manageable and modular components, simplifying maintenance and future extensions. Consequently, the transition becomes less disruptive and more efficient.
Step 3: Testing and Validation
Rigorous testing is paramount in any modernization effort, particularly with legacy systems, as even minor bugs can have significant consequences. Therefore, a robust validation process is essential.
AI also plays an important role here; by analyzing test results and identifying patterns, AI models can anticipate potential bug locations, allowing developers to focus their testing efforts effectively. Additionally, GitHub Copilot assists in generating unit tests for translated components, ensuring code quality and reliability throughout the modernization process.
The convergence of tools like GitHub Copilot with domain expertise is creating exciting new avenues for legacy system modernization. It’s not about replacing developers; instead, it’s about empowering them to overcome previously insurmountable challenges and extend the lifespan and value of critical business applications. This approach ensures that vital systems continue to operate effectively while embracing modern development practices.
Summary Table of Key Steps
| Step | Description |
|---|---|
| 1 | Code Preparation (Reverse Engineering) – Using AI to understand existing legacy code. |
| 2 | Translation and Abstraction – Creating a modern interface for interacting with the original codebase. |
| 3 | Testing & Validation – Ensuring that changes don’t break the system and improve reliability. |
Source: Read the original article here.
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