AI-Driven Legacy System Modernisation — A Practical Approach 2026

How to use AI for analysis, refactoring and migration of legacy code. From COBOL conversion to automatic documentation of legacy systems. Real case studies and tools.

Why Legacy Systems Survive — And Why That’s Not Bad¶

In the Czech enterprise environment, thousands of applications older than 15 years are running. COBOL in banks, Visual Basic in insurance companies, PHP 5 in e-commerce. These systems work — they generate revenue, process transactions, serve customers. The problem isn’t that they exist. The problem is that it’s impossible to change them fast enough.

Traditional modernization — rewriting from scratch — is statistically the riskiest IT project. According to the Standish Group, 72% of large rewrite projects exceed budget or fail completely. And for good reason: legacy systems contain decades of business knowledge encoded in thousands of conditions, exceptions, and workarounds that nobody documented.

AI changes the rules. Not by automatically rewriting legacy systems — that’s marketing fantasy. But by dramatically accelerating every phase of modernization: analysis, documentation, refactoring, testing, and migration. Tools like Amazon Q Code Transformation, GitHub Copilot, and specialized platforms (Bloop, Sourcegraph Cody) can analyze millions of lines of code and provide contextual understanding that would take a team of analysts months.

In this article, we’ll go through a practical framework for AI-driven modernization, specific tools, and real results from enterprise projects.

Phase 1: AI-Powered Legacy Code Analysis¶

Before you can modernize anything, you need to understand what you have. And with legacy systems, that’s the hardest step. Documentation is outdated (if it exists at all), original developers have left, and code is an organically grown structure without clear architecture.

Automatic documentation: LLM models can analyze code and generate documentation at the function, module, and entire subsystem level. Sourcegraph Cody and GitHub Copilot Chat can answer questions like “What does this function do?” or “Where is customer address validated?” across the entire repository.

Dependency mapping: Tools like Lattix, Structure101, or open-source Depends can visualize dependencies in code. The AI layer adds semantic understanding — not just “module A calls module B,” but “module A needs module B for insurance premium calculation.”

Business rules extraction: This is the holy grail. Legacy code contains business rules that nobody explicitly documented. AI can identify conditions, exceptions, and edge cases and generate readable documentation of business logic. Example: from 500 lines of COBOL code extract “if client is older than 65 and has insurance type B, apply 15% discount, but maximum 2000 CZK.”

Dead code detection: Legacy systems typically have 20-40% dead code — functions that are never called, features that were never completed. AI analysis identifies dead code with higher accuracy than static tools because it understands runtime context (logs, traces).

Practical tip: start with analysis of one module, not the entire system. Proof of concept on 10,000 lines of COBOL will tell you more than a roadmap for 2 million lines.

Phase 2: Automatic Refactoring and Conversion¶

After analysis comes transformation. AI offers two approaches here:

Approach 1: Incremental refactoring — instead of rewrite, gradually modernize existing code. AI suggests refactoring steps: extract methods, eliminate duplicates, simplify conditions. Tools: Sourcery (Python), IntelliJ AI Assistant, JetBrains AI. Advantage: low risk, each step is testable. Disadvantage: slower progress.

Approach 2: Language conversion — automatic conversion between languages. Amazon Q Code Transformation converts Java 8/11 to Java 17, AWS Mainframe Modernization converts COBOL to Java. IBM watsonx Code Assistant for Z handles COBOL-to-Java conversion with claimed 80% code accuracy.

The reality is more sobering: automatic conversion produces functional, but not idiomatic code. Converted Java code from COBOL looks like COBOL written in Java — technically correct, but unmaintainable. You need a second refactoring phase where AI (or humans) transform the code to idiomatic style of the target language.

Workflow that works in practice:

1. Automatic conversion (AI) → generates functional code
2. Test suite generation (AI) → verifies behavior preservation
3. Idiomatic refactoring (AI + human) → code looks native
4. Code review (human) → verifies business logic
5. Integration testing (automated) → verifies compatibility

This pipeline shortens modernization from months to weeks for individual modules.

Phase 3: Strangler Fig Pattern with AI¶

The safest modernization strategy is the Strangler Fig Pattern — gradually replacing parts of the legacy system with new implementations while the old system runs. AI accelerates this pattern in several ways:

API wrapper generation: AI analyzes the legacy system interface and automatically generates REST/gRPC API wrappers. The legacy system becomes a “backend” behind a modern API — new applications communicate through API, legacy clients gradually migrate.

Test harness generation: Before replacing a module, you need tests that verify the new implementation behaves the same. AI generates contract tests, integration tests, and property-based tests from analysis of existing behavior.

Data migration automation: Legacy database schema is often normalized in a “creative” way. AI analyzes data patterns, proposes new schema, and generates migration scripts including data transformation logic.

Feature flag orchestration: Gradual rollout of new modules requires feature flags. AI can analyze traffic patterns and suggest optimal rollout strategy — which modules to migrate first for maximum impact with minimum risk.

Case study: Czech insurance company migrated claims processing module (180K lines of COBOL) to Java microservices in 4 months instead of planned 12. AI generated 70% of test suite, 60% of API wrappers, and 50% of data migration code. Human team focused on business logic and edge cases.

Tools for AI-Driven Modernization — 2026 Overview¶

The tool market is growing rapidly. Here are the categories and leaders:

Code understanding & documentation:

• Sourcegraph Cody — code intelligence across entire repository, context-aware Q&A
• GitHub Copilot Chat — integrated into IDE, understands project context
• Bloop — semantic code search, natural language → code navigation

Code transformation:

• Amazon Q Code Transformation — Java version upgrades, language migrations
• IBM watsonx Code Assistant for Z — COBOL-to-Java, mainframe modernization
• Moderne — automated code refactoring using OpenRewrite recipes + AI

Testing:

• Diffblue Cover — automatic unit test generation for Java
• Qodo (CodiumAI) — AI test generation with focus on edge cases
• Launchable — ML-based test selection (runs only relevant tests)

End-to-end platforms:

• AWS Mainframe Modernization — complete platform for mainframe migration
• Google Cloud Dual Run — parallel running of legacy + modern version with output comparison
• Micro Focus (OpenText) — enterprise modernization suite

Selection depends on your legacy stack and target platform. For mainframe COBOL → Java: AWS or IBM. For Java upgrade: Amazon Q. For general refactoring: Moderne + Copilot.

Risks and Limits of AI Modernization¶

AI isn’t a silver bullet. Critical limits you need to know:

Business context loss: AI understands code syntactically and semantically, but doesn’t understand why code was written a certain way. A workaround for a bug from 2008 that nobody documented might be “optimized” away by AI. Solution: business analyst must validate every transformation.

Hallucination in code generation: LLMs can generate code that looks correct but has subtle bugs. In legacy modernization, this is especially dangerous — the difference between “almost correct” and “correct” could be a million-dollar financial transaction.

Regression testing coverage: AI-generated tests typically cover 60-80% of behavior. The remaining 20-40% are edge cases, race conditions, and implicit behavior that require human expertise.

Regulatory compliance: In regulated industries (banks, healthcare), you must prove that the modernized system is equivalent to the original. Automated conversion complicates audit trails. Solution: detailed logging of every transformation, dual-run validation.

Vendor lock-in: Cloud-native modernization tools (AWS, Azure) naturally steer code toward vendor-specific services. Consider whether target architecture should be cloud-agnostic.

Rule: AI does 80% of the work, but the last 20% requires senior engineering talent. Don’t replace people — redirect them to high-value work.

Conclusion¶

Modernization with AI: Evolution, Not Revolution¶

AI-driven modernization of legacy systems isn’t about rewriting everything at once. It’s about gradual, measurable, safe transformation with AI as an accelerator for every phase.

Start with analysis of one module. Measure how much time AI saves. Expand to other modules. After 6 months, you’ll have data for a business case for systematic modernization.

Legacy systems won’t disappear overnight. But with AI, they can evolve at a speed that wasn’t previously possible.