There’s a whole lot of pleasure proper now about AI enabling mainframe software modernization. Boards are paying consideration. CIOs are getting requested for a plan. AI is a real accelerator for COBOL modernization however to get outcomes, AI wants extra context that supply code alone can’t present.Right here’s what we’ve discovered working with 400+ enterprise prospects: mainframe modernization has two very totally different halves. The primary half is reverse engineering, understanding what your current methods really do. The second half is ahead engineering, constructing the brand new functions.
The primary half is the place mainframe tasks reside or die. Nonetheless, coding assistants are genuinely good at solely the second half. Give them a transparent, validated spec and so they’ll construct fashionable functions quick.
Now we have discovered that delivering profitable COBOL modernization requires an answer that may reverse engineer deterministically, produce validated and traceable specs, and assist these specs movement into any AI-powered coding assistant for the ahead engineering. A profitable modernization requires each reverse engineering and ahead engineering.
What a profitable mainframe modernization requires
Bounded, full context
Mainframe functions are large. Actually large. A single program can run tens of hundreds of strains, pulling in shared knowledge definitions from throughout the system, calling different packages, orchestrated by JCL that spans the complete panorama. Right now, AI can solely course of a restricted quantity of code at a time. Feed it one program and it could actually’t see the copybooks, the referred to as subroutines, the shared information, or the JCL that ties all the pieces collectively. It’ll produce output that appears affordable for the code it could actually see however miss dependencies it was by no means proven. In working with prospects, we resolve this by extracting all implicit dependencies deterministically first, then feeding AI bounded, full items with all the pieces it wants already resolved. That approach AI focuses on what it’s nice at (understanding enterprise logic, producing specs) as an alternative of guessing at connections it could actually’t see.
Platform-aware context
Right here’s one thing that surprises individuals: the identical COBOL supply code behaves in another way relying on the compiler and runtime. How numbers get rounded, how knowledge sits in reminiscence, how packages discuss to middleware. These aren’t within the supply code. They’re decided by the particular compiler and runtime setting the code was constructed for. Many years of hardware-software integration can’t be replicated by merely shifting code. We discovered that AI does its greatest work when platform-specific conduct has already been resolved. Feed AI clear, platform-aware enter, and it delivers. Feed it uncooked supply code, and it’ll generate output that appears proper however behaves in another way than the unique. In monetary methods, a rounding distinction isn’t a beauty difficulty. It’s a cloth error.
A traceable basis
When you’re in banking, insurance coverage, or authorities, your regulators will ask one query: are you able to show you didn’t miss something? AI by itself isn’t sufficient to extract enterprise logic and generate documentation that regulators will settle for. Regulatory compliance requires each output to have a proper, auditable connection again to the unique system. We discovered early that traceability doesn’t come from AI studying supply code. It comes from structuring the code into exact, bounded items so we all know precisely what goes into the AI and might hint each output again to its supply. For purchasers in regulated industries, that is usually the distinction between a undertaking that strikes ahead and one which stalls.
How we set AI up for fulfillment in AWS Rework
We constructed AWS Rework to modernize mainframe functions at scale. The concept is simple: give AI the precise basis, and prospects get traceable, appropriate, and full outcomes they’ll take to manufacturing. AWS Rework begins by constructing a whole, deterministic mannequin of the appliance. Specialised brokers extract code construction, runtime conduct, and knowledge relationships throughout the complete system — not one program at a time, however the entire panorama. This produces a dependency graph aligned with the precise compiler semantics, capturing cross-program dependencies, middleware interactions, and platform-specific conduct earlier than AI will get concerned. From there, giant packages get decomposed into bounded, processable, items. Platform-specific conduct is resolved deterministically. The items are sized for AI to course of successfully. Then AI extracts enterprise logic in pure language, and each output will get validated towards the deterministic proof we’ve already extracted. Specs map again to the unique code. When a regulator asks “did you miss something?”, there’s a verifiable reply. What units this aside is that AI by no means operates at the hours of darkness. Each unit it processes has identified inputs and anticipated outputs, so we will validate what comes again. No different method available on the market closes that loop. What comes out is a set of validated, traceable technical specs that plug into any fashionable improvement setting. The laborious a part of modernization is knowing what exists at the moment. When you’ve captured that in exact specs, AI-powered IDEs can construct the brand new software with confidence.
An end-to-end platform for enterprise transformation
No person modernizes one app. Our prospects are observing portfolios of lots of or hundreds of interconnected functions, and so they want far more than evaluation assist. AWS Rework automates throughout the total lifecycle: evaluation, check planning, refactoring, reimagination. The entire thing. And inside that, totally different apps want totally different paths. Some get re-imagined from scratch. Some simply want a clear, deterministic conversion to Java. Some must get out of the information heart first and modernize later. Some will stay on the mainframe. We discovered the laborious approach that treating all of them the identical is how tasks blow up. The portfolio determination (which app, which path, what order) issues as a lot because the tech. In our expertise, that is the one approach enterprise modernization really finishes. One-size-fits-all approaches are why these tasks fail. Yet another factor that will get missed always: check knowledge. You possibly can’t show the modernized app works with out actual manufacturing knowledge and actual eventualities. We’ve watched groups get all through code conversion after which stall as a result of no one deliberate for knowledge seize. So, we constructed check planning and on-prem knowledge seize into the platform from day one. Not a cleanup train on the finish. That’s what this really appears to be like like when it really works. Finish-to-end automation, the precise path for every app, validation baked in.
How one can get this proper
The query isn’t “ought to we use AI for COBOL modernization?” In fact it is best to. The query is the way you set AI as much as ship: traceability for regulators, platform-specific conduct dealt with accurately, consistency throughout your software portfolio, and the flexibility to scale to lots of of interconnected packages. That’s what we discovered constructing AWS Rework. Deterministic evaluation as the inspiration. AI because the accelerator. An AWS service that covers the total vary of modernization patterns.
And it’s working.
BMW Group lowered testing time by 75% and elevated check protection by 60%, considerably reducing danger whereas accelerating modernization timelines.
Fiserv accomplished a mainframe modernization undertaking that will have taken 29+ months in simply 17 months.
Itau lower mainframe software discovery time and testing time by greater than 90%, enabling groups to modernize functions 75% quicker than with earlier guide efforts.
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