What is AI-assisted code migration?

AI-assisted code migration uses large language models to read a legacy codebase and produce upgraded dependencies, generated tests, or translated source in a target language, with a developer reviewing and validating every change.

The current tools are GitHub Copilot app modernization, generally available for Java and .NET since 23 September 2025, AWS Transform for mainframe, .NET, and VMware workloads, and IBM watsonx Code Assistant for Z for COBOL. Each ships assessment and human review as part of the workflow.

What does AI-assisted migration do well?

Four things: portfolio assessment, dependency and framework upgrades, characterization test generation, and first-draft translation of small, well-bounded modules with clear inputs and outputs.

Assessment is the underrated one. Running these tools across an estate produces a dependency graph, a dead-code inventory, and a complexity ranking in weeks rather than months. Teams routinely find that a meaningful share of what they planned to migrate has no live callers, which is the cheapest scope reduction available to any program.

Framework upgrades are the other reliable win. Moving a Java service across major versions or patching a dependency tree is mechanical, high-volume, and verified by an existing test suite. The model does the tedious part and the pipeline catches mistakes.

Where does AI-assisted migration break?

On implicit business logic, on code whose behavior depends on the runtime, and on quality that degrades quietly at volume. The last one causes the most damage because nothing fails at the time.

GitClear and GitKraken analyzed 623 million real code changes from 2023 through 2026. Against the 2021 baseline they found duplicated code blocks up 81 percent, copy-paste within commits up 41 percent, error-masking constructs up 47 percent, and refactoring down from a quarter of all changes to under a tenth. Cloned blocks carry 15 to 50 percent more defects. A separate study of over 300,000 AI-authored commits found more than 15 percent introduced at least one issue.

The specific failure mode in migration work: a model given a large legacy file will often reproduce a pattern rather than consolidate it, so a translated estate can come out functionally correct and structurally worse than the original. Watch for the error-masking pattern too, where a generated catch block swallows an exception the legacy code deliberately allowed to surface.

Implicit logic is the other hazard. Decades-old systems encode business rules in field lengths, sort orders, and job sequencing that no comment explains. A model translates the syntax faithfully and loses the intent.

What review gate do you need?

Characterization tests written against the legacy system before translation, the same static analysis and coverage thresholds you apply to hand-written code, plus a commit record showing which tool generated which change.

Characterization tests are the non-negotiable one. Capture the behavior of the existing system, including behavior nobody intended, then hold the migrated code to it. That is the only way to catch a silent semantic change in logic no one on staff fully understands.

Track duplication and change failure rate through the program alongside velocity. Rising throughput with rising duplication means you are accumulating debt faster than you are retiring it.

What to do next

Pick one bounded module with an existing test suite and run a translation pilot on it. Record duplication percentage, coverage, and change failure rate before and after. Those three numbers tell you where your team can trust the tooling, and they make the next scoping conversation an evidence-based one.

Read next: AI for Enterprise Application Modernization: Migrate, Automate, Govern

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