In May 2025, Amazon Web Services (AWS) launched AWS Transform for Mainframe, an innovative AI-driven service aimed at modernizing mainframe workloads on a large scale. This agentic AI service is designed to simplify and accelerate the modernization process of mainframe applications by automating various complex, resource-intensive tasks that typically span from the initial assessment phase to the final deployment stage. This service allows businesses to migrate legacy mainframe applications, such as those written in COBOL, CICS, DB2, and VSAM, to modern cloud environments. This transformation process can significantly reduce the timeline for modernization from years to mere months.
Today, AWS announced a series of enhancements to AWS Transform for Mainframe, introducing new AI-powered analysis features, support for the Reimagine modernization pattern, and automated testing capabilities. These advancements aim to tackle two major challenges associated with mainframe modernization: the need to thoroughly transform applications rather than just transferring them to the cloud, and the extensive time and expertise required for effective testing.
Reimagining Mainframe Modernization
The introduction of the Reimagine modernization pattern represents a significant shift in how mainframe applications are modernized. This new AI-driven approach allows for a complete reimagining of a customer’s application architecture, leveraging modern patterns and transitioning from batch processes to real-time functions. By integrating enhanced business logic extraction with novel data lineage analysis and automated data dictionary generation, AWS Transform enables customers to convert monolithic mainframe applications, traditionally written in languages like COBOL, into more contemporary architectural styles such as microservices.
Automated Testing
The automated testing capabilities introduced with AWS Transform for Mainframe allow customers to generate automated test plans, collect test data, and automate test case scripts. AWS Transform also provides tools for functional testing of data migration, results validation, and terminal connectivity. These AI-powered capabilities work in unison to expedite testing timelines and enhance accuracy through automation.
How to Reimagine Mainframe Modernization
Mainframe modernization is not a one-size-fits-all solution. While tactical approaches focus on augmenting and maintaining existing systems, strategic modernization offers several distinct pathways: Replatform, Refactor, Replace, or the newly introduced Reimagine approach. In the Reimagine pattern, AWS Transform combines mainframe system analysis with organizational knowledge to generate comprehensive business and technical documentation and architecture recommendations. This approach helps preserve critical business logic while enabling modern cloud-native capabilities.
AWS Transform introduces advanced data analysis capabilities that are crucial for successful mainframe modernization, such as data lineage analysis and automated data dictionary generation. These features work in tandem to provide clarity on the meaning, usage, and relationships of mainframe data, offering customers complete visibility into their data landscape. This transparency allows for informed decision-making and confident redesign of data architectures while preserving essential business logic and relationships.
The Reimagine strategy incorporates the principle of human-in-the-loop validation, meaning that AI-generated application specifications and code, such as those produced by AWS Transform and Kiro, are continuously validated by domain experts. This collaborative approach between AI capabilities and human expertise significantly reduces transformation risks while maintaining the speed benefits of AI-powered modernization.
The transformation pathway follows a three-phase methodology to convert legacy mainframe applications into cloud-native microservices:
- Reverse Engineering: Extract business logic and rules from existing COBOL or JCL code using AWS Transform for Mainframe.
- Forward Engineering: Generate microservice specifications, modernized source code, infrastructure as code (IaC), and modernized databases.
- Deploy and Test: Deploy the generated microservices to Amazon Web Services (AWS) using IaC and test the functionality of the modernized application.
While microservices architecture offers significant benefits for mainframe modernization, it’s essential to understand that it may not be the optimal solution for every scenario. The choice of architectural patterns should be guided by the specific requirements and constraints of the system. The key is to select an architecture that aligns with both the current needs and future aspirations of the organization, recognizing that architectural decisions can evolve over time as organizations enhance their cloud-native capabilities.
This flexible approach supports both do-it-yourself and partner-led development, allowing businesses to use their preferred tools while maintaining the integrity of their business processes. Organizations can reap the benefits of modern cloud architecture while preserving decades of business logic and reducing project risks.
To explore more about reimagining mainframe modernization, visit the interactive demo provided by AWS.
Automated Testing in Action
The new automated testing feature supports the IBM z/OS mainframe batch application stack at launch, enabling organizations to address a broader range of modernization scenarios while maintaining consistent processes and tooling.
New mainframe capabilities include:
- Plan Test Cases: Create test plans from mainframe code, business logic, and scheduler plans.
- Generate Test Data Collection Scripts: Create JCL scripts for data collection from mainframes for your test plan.
- Generate Test Automation Scripts: Generate execution scripts to automate testing of modernized applications running in the target AWS environment.
To start with automated testing, set up a workspace, assign roles to users, and invite them to onboard into your workspace. More information can be found in the AWS Transform User Guide. Choose "Create job" in your workspace to see all types of supported transformation jobs. For example, selecting the Mainframe Modernization job will initiate the process of modernizing mainframe applications.
After creating a new job, you can initiate modernization for test generation. This workflow is sequential and allows you to answer the AI agent’s questions, providing necessary input. You can add collaborators and specify resource locations where the codebase or documentation is stored, such as in an Amazon Simple Storage Service (Amazon S3) bucket.
An Example: Credit Card Management System
For instance, consider a sample application for a credit card management system as a mainframe banking case. It includes presentation (BMS screens), business logic (COBOL), and data (VSAM/DB2), along with online transaction processing and batch jobs.
Following the steps of analyzing code, extracting business logic, decomposing code, and planning migration waves, you can experience new automated testing capabilities such as planning test cases, generating test data collection scripts, and test automation scripts.
The new testing workflow creates a test plan for your modernization project and generates test data collection scripts. You will follow three planning steps:
- Configure Test Plan Inputs: Link your test plan to other job files. The test plan is generated based on analyzing the mainframe application code and can provide more details using extracted business logic, technical documentation, decomposition, and a scheduler plan.
- Define Test Plan Scope: Define the entry point, such as the specific program where the application’s execution flow begins. For example, the JCL for a batch job. Each functional test case in the test plan is designed to start execution from a specific entry point.
- Refine Test Plan: A test plan consists of sequential test cases. You can reorder them, add new ones, merge multiple cases, or split one into two on the test case detail page. Batch test cases are composed of a sequence of JCLs following the scheduler plan.
Generating test data collection scripts gathers test data from mainframe applications for functional equivalence testing. This step generates JCL scripts to help collect test data from various data sources (such as VSAM files or DB2 databases) for testing the modernized application. Automated scripts are created to extract test data from VSAM datasets, query DB2 tables for sample data, collect sequential datasets, and generate data collection workflows. Once completed, comprehensive test data collection scripts will be ready for use.
To learn more about automated testing, visit the Modernization of Mainframe Applications section in the AWS Transform User Guide.
Availability and Pricing
The new capabilities in AWS Transform for Mainframe are now available in all AWS Regions where AWS Transform for Mainframe is offered. For information on regional availability and the future roadmap, visit the AWS Capabilities by Region page. Currently, core features—such as assessment and transformation—are offered at no cost to AWS customers. For more information, visit the AWS Transform Pricing page.
You can try out the new capabilities in the AWS Transform console. To learn more, visit the AWS Transform for Mainframe product page or send feedback through AWS re:Post for AWS Transform for Mainframe or your usual AWS Support contacts.
For additional insights and information, feel free to explore the official AWS Transform for Mainframe page.
For more Information, Refer to this article.
