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In Might, 2025, we launched AWS Rework for mainframe, the primary agentic AI service for modernizing mainframe workloads at scale. The AI-powered mainframe agent accelerates mainframe modernization by automating advanced, resource-intensive duties throughout each part of modernization—from preliminary evaluation to remaining deployment. You’ll be able to streamline the migration of legacy mainframe functions, together with COBOL, CICS, DB2, and VSAM to trendy cloud environments—slicing modernization timelines from years to months.
As we speak, we’re asserting enhanced capabilities in AWS Rework for mainframe that embody AI-powered evaluation options, assist for the Reimagine modernization sample, and testing automation. These enhancements clear up two important challenges in mainframe modernization: the necessity to fully rework functions somewhat than merely transfer them to the cloud, and the in depth time and experience required for testing.
- Reimagining mainframe modernization – This can be a new AI-driven strategy that fully reimagines the shopper’s software structure utilizing trendy patterns or transferring from batch course of to real-time capabilities. By combining the improved enterprise logic extraction with new knowledge lineage evaluation and automatic knowledge dictionary era from the legacy supply code by AWS Rework, clients rework monolithic mainframe functions written in languages like COBOL into extra trendy architectural types, like microservices.
- Automated testing – Clients can use new automated take a look at plan era, take a look at knowledge assortment scripts, and take a look at case automation scripts. AWS Rework for mainframe additionally supplies purposeful testing instruments for knowledge migration, outcomes validation, and terminal connectivity. These AI-powered capabilities work collectively to speed up testing timelines and enhance accuracy by automation.
Let’s be taught extra about reimagining mainframe modernization and automatic testing capabilities.
How one can reimagine mainframe modernization
We acknowledge that mainframe modernization shouldn’t be a one-size-fits-all proposition. Whereas tactical approaches deal with augmentation and sustaining present techniques, strategic modernization presents distinct paths: Replatform, Refactor, Substitute, or the brand new Reimagine.
Within the Reimagine sample, AWS Rework AI-powered evaluation combines mainframe system evaluation with organizational information to create detailed enterprise and technical documentation and structure suggestions. This helps protect important enterprise logic whereas enabling trendy cloud-native capabilities.
AWS Rework supplies new superior knowledge evaluation capabilities which are important for profitable mainframe modernization, together with knowledge lineage evaluation and automatic knowledge dictionary era. These options work collectively to outline the construction and that means to accompany the utilization and relationships of mainframe knowledge. Clients achieve full visibility into their knowledge panorama, enabling knowledgeable decision-making for modernization. Their technical groups can confidently redesign knowledge architectures whereas preserving important enterprise logic and relationships.
The Reimagining technique follows the precept of human within the loop validation, which signifies that AI-generated software specs and code comparable to AWS Rework and Kiro are repeatedly validated by area specialists. This collaborative strategy between AI capabilities and human judgment considerably reduces transformation danger whereas sustaining the velocity benefits of AI-powered modernization.
The pathway has a three-phase methodology to remodel legacy mainframe functions into cloud-native microservices:
- Reverse engineering to extract enterprise logic and guidelines from present COBOL or job management language (JCL) code utilizing AWS Rework for mainframe.
- Ahead engineering to generate microservice specification, modernized supply code, infrastructure as code (IaC), and modernized database.
- Deploy and take a look at to deploy the generated microservices to Amazon Net Providers (AWS) utilizing IaC and to check the performance of the modernized software.
Though microservices structure presents important advantages for mainframe modernization, it’s essential to grasp that it’s not one of the best resolution for each state of affairs. The selection of architectural patterns must be pushed by the precise necessities and constraints of the system. The secret is to pick out an structure that aligns with each present wants and future aspirations, recognizing that architectural choices can evolve over time as organizations mature their cloud-native capabilities.
The versatile strategy helps each do-it-yourself and partner-led improvement, so you should utilize your most popular instruments whereas sustaining the integrity of your small business processes. You get the advantages of contemporary cloud structure whereas preserving many years of enterprise logic and lowering challenge danger.
Automated testing in motion
The brand new automated testing function helps IBM z/OS mainframe batch software stack at launch, which helps organizations tackle a wider vary of modernization situations whereas sustaining constant processes and tooling.
Listed here are the brand new mainframe capabilities:
- Plan take a look at circumstances – Create take a look at plans from mainframe code, enterprise logic, and scheduler plans.
- Generate take a look at knowledge assortment scripts – Create JCL scripts for knowledge assortment out of your mainframe to your take a look at plan.
- Generate take a look at automation scripts – Generate execution scripts to automate testing of modernized functions working within the goal AWS setting.
To get began with automated testing, it is best to arrange a workspace, assign a selected position to every person, and invite them to onboard your workspace. To be taught extra, go to Getting began with AWS Rework within the AWS Rework Consumer Information.
Select Create job in your workspace. You’ll be able to see all kinds of supported transformation jobs. For this instance, I choose the Mainframe Modernization job to modernize mainframe functions.
After a brand new job is created, you’ll be able to kick off modernization for assessments era. This workflow is sequential and it’s a place so that you can reply the AI agent’s questions, offering the required enter. You’ll be able to add your collaborators and specify useful resource location the place the codebase or documentation is situated in your Amazon Easy Storage Service (Amazon S3) bucket.
I take advantage of a pattern software for a bank card administration system because the mainframe banking case with the presentation (BMS screens), enterprise logic (COBOL) and knowledge (VSAM/DB2), together with on-line transaction processing and batch jobs.
After ending the steps of analyzing code, extracting enterprise logic, decomposing code, planning migration wave, you’ll be able to expertise new automated testing capabilities comparable to planning take a look at circumstances, producing take a look at knowledge assortment scripts, and take a look at automation scripts.
The brand new testing workflow creates a take a look at plan in your modernization challenge and generates take a look at knowledge assortment scripts. You should have three planning steps:
- Configure take a look at plan inputs – You’ll be able to hyperlink your take a look at plan to your different job recordsdata. The take a look at plan is generated based mostly on analyzing the mainframe software code and might present extra particulars optionally utilizing the extracted enterprise logic, the technical documentation, the decomposition, and utilizing a scheduler plan.
- Outline take a look at plan scope – You’ll be able to outline the entry level, the precise program the place the appliance’s execution circulate begins. For instance, the JCL for a batch job. Within the take a look at plan, every purposeful take a look at case is designed to start out the execution from a selected entry level.
- Refine take a look at plan – A take a look at plan is made up of sequential take a look at circumstances. You’ll be able to reorder them, add new ones, merge a number of circumstances, or cut up one into two on the take a look at case element web page. Batch take a look at circumstances are composed of a sequence of JCLs following the scheduler plan.
Producing take a look at knowledge assortment scripts collects take a look at knowledge from mainframe functions for purposeful equivalence testing. This step actively generates JCL scripts that can aid you collect take a look at knowledge from the pattern software’s numerous knowledge sources (comparable to VSAM recordsdata or DB2 databases) to be used in testing the modernized software. The step is designed to create automated scripts that may extract take a look at knowledge from VSAM datasets, question DB2 tables for pattern knowledge, gather sequential knowledge units, and generate knowledge assortment workflows. After this step is accomplished, you’ll have complete take a look at knowledge assortment scripts prepared to make use of.
To be taught extra about automated testing, go to Modernization of mainframe functions within the AWS Rework Consumer Information.
Now obtainable
The brand new capabilities in AWS Rework for mainframe can be found as we speak in all AWS Areas the place AWS Rework for mainframe is obtainable. For Regional availability, go to the AWS Providers by Area. Presently, we provide our core options—together with evaluation and transformation—for gratis to AWS clients. To be taught extra, go to AWS Rework Pricing web page.
Give it a strive within the AWS Rework console. To be taught extra, go to the AWS Rework for mainframe product web page and ship suggestions to AWS re:Publish for AWS Rework for mainframe or by your traditional AWS Assist contacts.
— Channy