AI-Assisted Mainframe Modernization: Progress Does Not Mean Production-Ready

https://ift.tt/EShka7l

If you ask most companies where they are on their mainframe modernization journey, they will tell you that they are “about 80 percent converted.” They’ll tell you that the dashboards are green, the new Java compiles, and the tests are passing. However, the people who have actually been keeping these mainframes running for 20 years (the same ones who are hoping to retire soon) know there is more to it than that.

AI has made the first stretch of a migration dramatically faster, but the first stretch is the easiest. Working on large legacy estates has taught us that the gap between that level of “progress” and what it takes to be “production-ready” is where most of the complexity and risk live. The hard part is proving the new system behaves exactly like the old one, under real load, with real data, across every job and integration the business depends on.

Misleading Metrics

One of the biggest reasons for this gap is misleading metrics. Three signals show up in almost every status report that measure something different than what leadership thinks they measure.

“Percent of code converted.” Lines translated measures output, not correctness. A COBOL program can be fully converted and still, for example, round a currency calculation differently, mishandle a packed-decimal field, or sort records in a different order. None of this would show up as an error, but at some point the misalignment will create confusion and introduce risk. This is why it’s also important to track percent verified. A program counts as verified when it arrives compiled and tested end to end, with every dependency it touches mapped, and with a documented list of what hasn’t been checked yet.

“It compiles.” Compiling proves valid syntax, but it doesn’t necessarily indicate matching behavior. Decades-old COBOL is full of non-standard constructs and undocumented workarounds, and code that’s mapped construct by construct won’t surface those quirks. The standard to hold is higher: Does it run?

 This is a behavior check rather than just a syntax check.

“All tests pass.” This metric is one of the most dangerous because it sounds like validation. When the AI that writes the code also writes the tests, the tests mostly confirm the AI’s own reading of the system. If the AI misses a rule, the tests miss it, too. Green tests can simply mean the code agrees with itself. It’s important to determine who wrote the tests and what the tests do and don’t cover. This needs to be done by humans who know the system and can put the tests into context.

Mind the Gaps

So, where are the gaps? They are not in the COBOL, which is where AI tools typically look. Instead, the gaps reside in the job control language that decides what runs, in what order, against which datasets. They’re in the batch schedules that encode timing dependencies, like the end-of-day job that must finish before reconciliation starts. They’re in CICS transaction configurations, DB2 bindings, and copybooks shared across hundreds of programs to create coupling that never appears in a single source file. And they’re in the formats nobody wrote down, as well as the brains of those system experts set to retire.

An AI tool that reads source files as text, a few at a time, can’t see most of this. Research on long-context models adds another wrinkle: Models reason most reliably about information at the start and end of their input, and less reliably about what’s buried in the middle. This means that an AI tool processing an enormous COBOL program will be less right about the middle, which is often where the business logic lives.

It’s like looking into a room with a flashlight vs. switching on the overhead light. That whole-room view is what most programs skip, and it’s a big reason Gartner predicts more than 70% of mainframe exit projects initiated this year will fail to deliver their intended benefits.

What It Takes to Be Production-Ready

To move from iterative progress to production-ready depends on understanding the whole system before changing any of it and on validating against reality before declaring that anything (much less full-on mainframe modernization) is done, including:

1. Behavioral parity against real data. Run the old and new systems side by side on production-representative inputs and compare outputs field by field. A practical start is a “golden dataset,” which comprises a few weeks of real inputs and the outputs the old system produced.

2. Validation beyond unit tests. It’s important to perform integration tests, batch-window runs, and end-to-end scenarios that exercise jobs, schedules and interfaces, not just functions.

3. Expert sign-off on extracted business rules. Before anything is rebuilt, the people who know the system must confirm the rules the tool says it found. It’s the most cost-effective way to catch a misunderstanding.

4. A tool that reports its own gaps. Anything working at this scale will hit something it can’t resolve. The question is whether it tells you or reports the job as done. An honest list of open items beats a perfect-looking dashboard.

5. A mapped blast radius. Before a change merges, you should know every program, job, and downstream consumer it touches. If nobody can determine all possible dependencies, you’re not ready.

AI is changing what’s possible in mainframe modernization. The speed gains are important, but speed matters only if it gets you to a system you can trust in production. The teams that succeed will be the ones who prioritize understanding and validation over blind faith in “green.”

The post AI-Assisted Mainframe Modernization: Progress Does Not Mean Production-Ready appeared first on SD Times.

Tech Developers