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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 section of modernization—from preliminary evaluation to last deployment. You’ll be able to streamline the migration of legacy mainframe functions, together with COBOL, CICS, DB2, and VSAM to fashionable cloud environments—chopping modernization timelines from years to months.
At present, we’re saying enhanced capabilities in AWS Rework for mainframe that embrace AI-powered evaluation options, help for the Reimagine modernization sample, and testing automation. These enhancements remedy two crucial challenges in mainframe modernization: the necessity to utterly remodel functions quite than merely transfer them to the cloud, and the intensive time and experience required for testing.
- Reimagining mainframe modernization – This can be a new AI-driven strategy that utterly reimagines the shopper’s utility structure utilizing fashionable patterns or shifting from batch course of to real-time features. By combining the improved enterprise logic extraction with new knowledge lineage evaluation and automatic knowledge dictionary technology from the legacy supply code by way of AWS Rework, clients remodel monolithic mainframe functions written in languages like COBOL into extra fashionable architectural types, like microservices.
- Automated testing – Clients can use new automated take a look at plan technology, take a look at knowledge assortment scripts, and take a look at case automation scripts. AWS Rework for mainframe additionally offers useful 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 way of automation.
Let’s study extra about reimagining mainframe modernization and automatic testing capabilities.
Find out how to reimagine mainframe modernization
We acknowledge that mainframe modernization just isn’t a one-size-fits-all proposition. Whereas tactical approaches give attention to augmentation and sustaining current 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 crucial enterprise logic whereas enabling fashionable cloud-native capabilities.
AWS Rework offers new superior knowledge evaluation capabilities which might be important for profitable mainframe modernization, together with knowledge lineage evaluation and automatic knowledge dictionary technology. These options work collectively to outline the construction and which means to accompany the utilization and relationships of mainframe knowledge. Clients acquire full visibility into their knowledge panorama, enabling knowledgeable decision-making for modernization. Their technical groups can confidently redesign knowledge architectures whereas preserving crucial enterprise logic and relationships.
The Reimagining technique follows the precept of human within the loop validation, which implies that AI-generated utility specs and code corresponding to AWS Rework and Kiro are repeatedly validated by area consultants. 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 rework legacy mainframe functions into cloud-native microservices:
- Reverse engineering to extract enterprise logic and guidelines from current 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 Internet Providers (AWS) utilizing IaC and to check the performance of the modernized utility.
Though microservices structure presents important advantages for mainframe modernization, it’s essential to grasp that it’s not the very best answer for each situation. The selection of architectural patterns needs to be pushed by the particular necessities and constraints of the system. The secret’s to pick an structure that aligns with each present wants and future aspirations, recognizing that architectural selections 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 well-liked instruments whereas sustaining the integrity of your corporation processes. You get the advantages of recent cloud structure whereas preserving many years of enterprise logic and lowering mission danger.
Automated testing in motion
The brand new automated testing function helps IBM z/OS mainframe batch utility stack at launch, which helps organizations tackle a wider vary of modernization eventualities 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 operating within the goal AWS setting.
To get began with automated testing, you must arrange a workspace, assign a particular function to every consumer, and invite them to onboard your workspace. To study 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 varieties 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 possibly can kick off modernization for exams technology. This workflow is sequential and it’s a place so that you can reply the AI agent’s questions, offering the mandatory enter. You’ll be able to add your collaborators and specify useful resource location the place the codebase or documentation is positioned in your Amazon Easy Storage Service (Amazon S3) bucket.
I take advantage of a pattern utility 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 possibly can expertise new automated testing capabilities corresponding 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 to your modernization mission 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 utility code and may 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 particular program the place the appliance’s execution circulation begins. For instance, the JCL for a batch job. Within the take a look at plan, every useful take a look at case is designed to begin the execution from a particular 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 useful equivalence testing. This step actively generates JCL scripts that can show you how to collect take a look at knowledge from the pattern utility’s varied knowledge sources (corresponding to VSAM recordsdata or DB2 databases) to be used in testing the modernized utility. 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 study extra about automated testing, go to Modernization of mainframe functions within the AWS Rework Consumer Information.
Now out there
The brand new capabilities in AWS Rework for mainframe can be found right this moment in all AWS Areas the place AWS Rework for mainframe is obtainable. For Regional availability, go to the AWS Providers by Area. At the moment, we provide our core options—together with evaluation and transformation—without charge to AWS clients. To study extra, go to AWS Rework Pricing web page.
Give it a attempt within the AWS Rework console. To study 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 way of your traditional AWS Assist contacts.
— Channy