AI is fueling a new wave of excitement around mainframe application modernization, with boards and CIOs demanding plans for leveraging its potential. However, realizing genuine results in COBOL modernization requires more than just AI-powered coding assistants, as recent findings from Amazon Web Services highlight. The challenge lies in accurately understanding existing systems before building new ones.
The journey involves two distinct phases: reverse engineering to understand existing systems, and forward engineering to build new applications. While coding assistants excel at the latter with clear specifications, the former—understanding legacy code—is where most projects succeed or fail. AWS Transform addresses this by providing the necessary foundation.
The Context Problem
Mainframe applications are vast, with programs spanning tens of thousands of lines, intertwined with shared data definitions and JCL. AI struggles with this scale, often missing critical dependencies like copybooks or called subroutines when fed isolated code snippets. AWS Transform first extracts all implicit dependencies deterministically, then presents AI with complete, resolved units, allowing it to focus on business logic rather than guessing connections.
Furthermore, COBOL source code behaves differently based on its compiler and runtime environment, affecting data rounding or memory management—details not present in the source. Replicating decades of hardware-software integration isn't a simple code migration. AWS Transform resolves these platform-specific behaviors deterministically before AI engagement, preventing material errors in critical systems like finance.
For regulated sectors like banking or government, auditable traceability is non-negotiable. Regulators demand proof that no logic was missed during modernization. While Generative AI can accelerate aspects of COBOL modernization, it alone cannot provide this. AWS Transform structures code into precise, bounded units, enabling verifiable tracing of every AI output back to its original source, crucial for project progression.
AWS Transform's Deterministic Approach
AWS Transform builds a complete, deterministic model of the application. Specialized agents map code structure, runtime behavior, and data relationships across the entire system. This dependency graph, aligned with compiler semantics, preps the data for AI. Large programs are then decomposed into manageable units, with platform specifics resolved, allowing AI to extract business logic and generate validated, traceable specifications.
This approach ensures AI never operates without known inputs and expected outputs, closing a critical validation loop unmatched by other market solutions. The result: precise technical specifications ready for any modern development environment. The platform recognizes that enterprise modernization extends beyond single applications; portfolios often contain hundreds of interconnected systems.
End-to-End Modernization
AWS Transform automates the full lifecycle—analysis, test planning, refactoring, reimagination—recognizing that different applications require different paths, from Java conversion to remaining on the mainframe. Crucially, the platform integrates test data planning and on-premise data capture from day one. Neglecting real production data and scenarios is a common pitfall that can derail projects post-conversion.
The effectiveness of AWS Transform is evident in customer outcomes. BMW Group cut testing time by 75% and increased coverage by 60%. Fiserv completed a complex mainframe modernization in 17 months, down from an estimated 29+. Itau reduced discovery and testing time by over 90%, accelerating modernization efforts by 75%.
