Real-world mainframe modernization with AI: A safe, scalable path from mainframe to cloud

Sure, modernizing a single, isolated and small application is relatively easy, especially with recent advancements with AI and large language models. The real challenge lies in modernizing at real-world scale without breaking the intricate web of dependencies and legacy data formats you find in a large global enterprise, all while ensuring functional equivalence. For example, some of these "real world" challenges include: Application logic is tightly fused directly to legacy and proprietary databases and record schemas.

Non-relational formats that are inaccessible by AI Agents, such as VSAM, flat files, IMS hierarchical structures. Transaction monitors like CICS and IMS TM deliver highly integrated transaction management. A single transaction scenario can consist of millions of lines of code.

Intricate sequential workflows with complex conditional step logic and dependencies. Internal/external boundaries utilizing proprietary protocols like CTG, IMS Connect, MQ, LU 6. Deep operational lock-in with specialized proprietary mainframe utility suites.

In other words, real-world modernization of mainframe applications is so much more than converting COBOL to Java. You also need to modernize the underlying data models, handle decades of obscured application dependencies and interfaces and modernize the underlying data stores. Most importantly, you need to validate and de-risk the modern code with actual production traffic before going live.

Our approach combines the advanced reasoning and scale of our Gemini models for code understanding, with mainframe-specific modernization products to address real-world complexity and challenges. Our solutions span four core pillars: assessment, modernization, de-risking, and data migration. Let's take a look at each of these.

Assessment: AI reverse-engineering of the legacy applications Our Mainframe Assessment Tool (MAT) reverse-engineers legacy codebases at massive scale to provide both explainability for the current legacy applications and sets the required foundation for modernization. MAT delivers deep insights into your mainframe environment in four key areas: Dependency visualization: Mapping relationships and interconnectivity between the different applications and data stores, such as DB2 databases or VSAM files. Automated business rule extraction (BRE): Translating complex mainframe application logic into both plain-language requirements and visual decision trees.

Automated documentation: Generating comprehensive, up-to-date technical documentation directly from your production mainframe source code. Domain and business function discovery: Automatically identifying application boundaries, grouping applications into high-level business domains and visualizing the architecture for these domains including inputs, outputs, interfaces, and where processing occurs. MAT gives you the clean, verified logic requirements needed to understand your existing applications and business processes and to design a cloud-native future.