# Legacy code modernization

> We use coding agents to document, test and migrate legacy systems in small, verified steps, from COBOL and old Java to aging .NET and monoliths.

- Page: https://www.ninalabs.ai/services/modernization/
- Publisher: Nina Labs AI LLC
- Updated: 2026-10-07

## Definition

AI-assisted modernization uses coding agents to read, document, test and rewrite legacy code. Engineers set the target architecture and verify each step against the old system.

## What you get

- An inventory of applications, dependencies and data flows
- Generated documentation of the business rules in the code, reviewed by your experts
- Characterization tests that pin the current behavior before any change
- An incremental migration plan with a rollback path for each step
- Migrated modules with equivalence tests against the old system
- A cutover plan and a runbook for the new system

## Typical use cases

- **Mainframe and COBOL:** Business rules extracted from COBOL and moved to Java, C# or a modern runtime, one batch job or transaction at a time.
- **Framework end of life:** Upgrades from end-of-life Java, .NET Framework, AngularJS or Python 2 code to supported versions.
- **Monolith decomposition:** Clear service boundaries found in a monolith and extracted one at a time behind the existing interface.
- **Database migration:** Stored procedures and schemas moved to a new database, with data checks on every table.

## Read the code first

The hardest part of a legacy system is usually the knowledge, not the code. Rules sit in code paths that nobody has touched in years. Agents read the whole code base and write down what each program does, which data it touches and which rules it applies. Your experts review that document. Most teams have never had one.

## Pin the behavior

Before we change anything, we write characterization tests. These tests record what the system does today, including the odd cases. They are the safety net for every later step.

## Migrate in slices

We replace the system one piece at a time behind its existing interface. Each slice is small enough to review, test and roll back. The old and the new code run side by side until the outputs match.

## Keep the team in charge

Your architects choose the target design. Agents do the volume work: reading, documenting, writing tests and translating code. Engineers review every change.

## Stack we use

Claude Code, OpenAI Codex, AWS Transform, GitHub Copilot, OpenRewrite, Characterization testing

## What we measure

- Share of the code base covered by characterization tests
- Modules migrated and verified as equivalent
- Incidents caused by the migration
- Run cost of the old system compared with the new one

## Frequently asked questions

### Can an agent rewrite the whole system at once?

It can try, but you cannot verify the result. We migrate in small slices that each have tests, a comparison against the old system and a rollback path.

### Our original developers are gone. Is that a problem?

It is the common case. Agents read the code and write down the business rules they find. Your domain experts check that list. It is often the first complete description of the system in years.

### How do you prove the new code does the same thing?

We run the old and the new code on the same inputs and compare the outputs, field by field, before each slice goes live.

## Related services

- [AI software factory](https://www.ninalabs.ai/services/software-factory/)
- [AI strategy and operating model](https://www.ninalabs.ai/services/strategy/)

## Contact

Talk to an engineer through the [contact form](https://www.ninalabs.ai/contact/) or by email at contact@ninalabs.ai.