Monthly Archives: December 2025

AWS

AWS Transform for mainframe introduces Reimagine capabilities and automated testing functionality

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In May, 2025, we launched AWS Transform for mainframe, the first agentic AI service for modernizing mainframe workloads at scale. The AI-powered mainframe agent accelerates mainframe modernization by automating complex, resource-intensive tasks across every phase of modernization—from initial assessment to final deployment. You can streamline the migration of legacy mainframe applications, including COBOL, CICS, DB2, and VSAM to modern cloud environments—cutting modernization timelines from years to months.

Today, we’re announcing enhanced capabilities in AWS Transform for mainframe that include AI-powered analysis features, support for the Reimagine modernization pattern, and testing automation. These enhancements solve two critical challenges in mainframe modernization: the need to completely transform applications rather than merely move them to the cloud, and the extensive time and expertise required for testing.

  • Reimagining mainframe modernization – This is a new AI-driven approach that completely reimagines the customer’s application architecture using modern patterns or moving from batch process to real-time functions. By combining the enhanced business logic extraction with new data lineage analysis and automated data dictionary generation from the legacy source code through AWS Transform, customers transform monolithic mainframe applications written in languages like COBOL into more modern architectural styles, like microservices.
  • Automated testing – Customers can use new automated test plan generation, test data collection scripts, and test case automation scripts. AWS Transform for mainframe also provides functional testing tools for data migration, results validation, and terminal connectivity. These AI-powered capabilities work together to accelerate testing timelines and improve accuracy through automation.

Let’s learn more about reimagining mainframe modernization and automated testing capabilities.

How to reimagine mainframe modernization
We recognize that mainframe modernization is not a one-size-fits-all proposition. Whereas tactical approaches focus on augmentation and maintaining existing systems, strategic modernization offers distinct paths: Replatform, Refactor, Replace, or the new Reimagine.

In the Reimagine pattern, AWS Transform AI-powered analysis combines mainframe system analysis with organizational knowledge to create detailed business and technical documentation and architecture recommendations. This helps preserve critical business logic while enabling modern cloud-native capabilities.

AWS Transform provides new advanced data analysis capabilities that are essential for successful mainframe modernization, including data lineage analysis and automated data dictionary generation. These features work together to define the structure and meaning to accompany the usage and relationships of mainframe data. Customers gain complete visibility into their data landscape, enabling informed decision-making for modernization. Their technical teams can confidently redesign data architectures while preserving critical business logic and relationships.

The Reimagining strategy follows the principle of human in the loop validation, which means that AI-generated application specifications and code such as AWS Transform and Kiro are continuously validated by domain experts. This collaborative approach between AI capabilities and human judgment significantly reduces transformation risk while maintaining the speed advantages of AI-powered modernization.

The pathway has a three-phase methodology to transform legacy mainframe applications into cloud-native microservices:

  • Reverse engineering to extract business logic and rules from existing COBOL or job control language (JCL) code using AWS Transform for mainframe.
  • Forward engineering to generate microservice specification, modernized source code, infrastructure as code (IaC), and modernized database.
  • Deploy and test to deploy the generated microservices to Amazon Web Services (AWS) using IaC and to test the functionality of the modernized application.

Although microservices architecture offers significant benefits for mainframe modernization, it’s crucial to understand that it’s not the best solution for every scenario. The choice of architectural patterns should be driven by the specific requirements and constraints of the system. The key is to select an architecture that aligns with both current needs and future aspirations, recognizing that architectural decisions can evolve over time as organizations mature their cloud-native capabilities.

The flexible approach supports both do-it-yourself and partner-led development, so you can use your preferred tools while maintaining the integrity of your business processes. You get the benefits of modern cloud architecture while preserving decades of business logic and reducing project risk.

Automated testing in action
The new automated testing feature supports IBM z/OS mainframe batch application stack at launch, which helps organizations address a wider range of modernization scenarios while maintaining consistent processes and tooling.

Here are the new mainframe capabilities:

  • 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 your mainframe to your test plan.
  • Generate test automation scripts – Generate execution scripts to automate testing of modernized applications running in the target AWS environment.

To get started with automated testing, you should set up a workspace, assign a specific role to each user, and invite them to onboard your workspace. To learn more, visit Getting started with AWS Transform in the AWS Transform User Guide.

Choose Create job in your workspace. You can see all types of supported transformation jobs. For this example, I select the Mainframe Modernization job to modernize mainframe applications.

After a new job is created, you can kick off modernization for tests generation. This workflow is sequential and it is a place for you to answer the AI agent’s questions, providing the necessary input. You can add your collaborators and specify resource location where the codebase or documentation is located in your Amazon Simple Storage Service (Amazon S3) bucket.

I use a sample application for a credit card management system as the mainframe banking case with the presentation (BMS screens), business logic (COBOL) and data (VSAM/DB2), including online transaction processing and batch jobs.

After finishing the steps of analyzing code, extracting business logic, decomposing code, planning migration wave, 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 have three planning steps:

  • Configure test plan inputs – You can link your test plan to your other job files. The test plan is generated based on analyzing the mainframe application code and can provide more details optionally using the extracted business logic, the technical documentation, the decomposition, and using a scheduler plan.
  • Define test plan scope – You can define the entry point, the specific program where the application’s execution flow begins. For example, the JCL for a batch job. In the test plan, each functional test case is designed to start the execution from a specific entry point.
  • Refine test plan – A test plan is made up 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 collects test data from mainframe applications for functional equivalence testing. This step actively generates JCL scripts that will help you gather test data from the sample application’s various data sources (such as VSAM files or DB2 databases) for use in testing the modernized application. The step is designed to create automated scripts that can extract test data from VSAM datasets, query DB2 tables for sample data, collect sequential data sets, and generate data collection workflows. After this step is completed, you’ll have comprehensive test data collection scripts ready to use.

To learn more about automated testing, visit Modernization of mainframe applications in the AWS Transform User Guide.

Now available
The new capabilities in AWS Transform for mainframe are available today in all AWS Regions where AWS Transform for mainframe is offered. For Regional availability, visit the AWS Services by Region. Currently, we offer our core features—including assessment and transformation—at no cost to AWS customers. To learn more, visit AWS Transform Pricing page.

Give it a try in the AWS Transform console. To learn more, visit the AWS Transform for mainframe product page and send feedback to AWS re:Post for AWS Transform for mainframe or through your usual AWS Support contacts.

— Channy

AWS

AWS Transform announces full-stack Windows modernization capabilities

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Earlier this year in May, we announced the general availability of AWS Transform for .NET, the first agentic AI service for modernizing .NET applications at scale. During the early adoption period of the service, we received valuable feedback indicating that, in addition to .NET application modernization, you would like to modernize SQL Server and legacy UI frameworks. Your applications typically follow a three-tier architecture—presentation tier, application tier, and database tier—and you need a comprehensive solution that can transform all of these tiers in a coordinated way.

Today, based on your feedback, we’re excited to announce AWS Transform for full-stack Windows modernization, to offload complex, tedious modernization work across the Windows application stack. You can now identify application and database dependencies and modernize them in an orchestrated way through a centralized experience.

AWS Transform accelerates full-stack Windows modernization by up to five times across application, UI, database, and deployment layers. Along with porting .NET Framework applications to cross-platform .NET, it migrates SQL Server databases to Amazon Aurora PostgreSQL-Compatible Edition with intelligent stored procedure conversion and dependent application code refactoring. For validation and testing, AWS Transform deploys applications to Amazon Elastic Compute Cloud (Amazon EC2) Linux or Amazon Elastic Container Service (Amazon ECS), and provides customizable AWS CloudFormation templates and deployment configurations for production use. AWS Transform has also added capabilities to modernize ASP.NET Web Forms UI to Blazor.

There is much to explore, so in this post I’ll provide the first look at AWS Transform for full-stack Windows modernization capabilities across all layers.

Create a full-stack Windows modernization transformation job
AWS Transform connects to your source code repositories and database servers, analyzes application and database dependencies, creates modernization waves, and orchestrates full-stack transformations for each wave.

To get started with AWS Transform, I first complete the onboarding steps outlined in the getting started with AWS Transform user guide. After onboarding, I sign in to the AWS Transform console using my credentials and create a job for full-stack Windows modernization.

Create a new job for Windows Modernization
Create a new job by choosing SQL Server Database Modernization

After creating the job, I complete the prerequisites. Then, I configure the database connector for AWS Transform to securely access SQL Server databases running on Amazon EC2 and Amazon Relational Database Service (Amazon RDS). The connector can connect to multiple databases within the same SQL Server instance.

Create new database connector by adding connector name and AWS Account ID

Next, I set up a connector to connect to my source code repositories.

Add a source code connector by adding Connection name, AWS Account ID and Code Connector Arn

Furthermore, I have the option to choose if I would like AWS Transform to deploy the transformed applications. I choose Yes and provide the target AWS account ID and AWS Region for deploying the applications. The deployment option can be configured later as well.

Choose if you would like to deploy transformed apps

After the connectors are set up, AWS Transform connects to the resources and runs the validation to verify IAM roles, network settings, and related AWS resources.

After the successful validation, AWS Transform discovers databases and their associated source code repositories. It identifies dependencies between databases and applications to create waves for transforming related components together. Based on this analysis, AWS Transform creates a wave-based transformation plan.

Start assessment for discovered database and source code repositories

Assessing database and dependent applications
For the assessment, I review the databases and source code repositories discovered by AWS Transform and choose the appropriate branches for code repositories. AWS Transform scans these databases and source code repositories, then presents a list of databases along with their dependent .NET applications and transformation complexity.

Start wave planning of asessed databases and dependent repositories

I choose the target databases and repositories for modernization. AWS Transform analyzes these selections and generates a comprehensive SQL Modernization Assessment Report with a detailed wave plan. I download the report to review the proposed modernization plan. The report includes an executive summary, wave plan, dependencies between databases and code repositories, and complexity analysis.

View SQL Modernization Assessment Report

Wave transformation at scale
The wave plan generated by AWS Transform consists of four steps for each wave. First, it converts the SQL Server schema to PostgreSQL. Second, it migrates the data. Third, it transforms the dependent .NET application code to make it PostgreSQL compatible. Finally, it deploys the application for testing.

Before converting the SQL Server schema, I can either create a new PostgreSQL database or choose an existing one as the target database.

Choose or create target database

After I choose the source and target databases, AWS Transform generates conversion reports for my review. AWS Transform converts the SQL Server schema to PostgreSQL-compatible structures, including tables, indexes, constraints, and stored procedures.

Download Schema conversion reports

For any schema that AWS Transform can’t automatically convert, I can manually address them in the AWS Database Migration Service (AWS DMS) console. Alternatively, I can fix them in my preferred SQL editor and update the target database instance.

After completing schema conversion, I have the option to proceed with data migration, which is an optional step. AWS Transform uses AWS DMS to migrate data from my SQL Server instance to the PostgreSQL database instance. I can choose to perform data migration later, after completing all transformations, or work with test data by loading it into my target database.

Choose if you would like to migrate data

The next step is code transformation. I specify a target branch for AWS Transform to upload the transformed code artifacts. AWS Transform updates the codebase to make the application compatible with the converted PostgreSQL database.

Specify target branch destination for transformed codebase

With this release, AWS Transform for full-stack Windows modernization supports only codebases in .NET 6 or later. For codebases in .NET Framework 3.1+, I first use AWS Transform for .NET to port them to cross-platform .NET. I’ll expand on this in a following section.

After the conversion is completed, I can view the source and target branches along with their code transformation status. I can also download and review the transformation report.

Download transformation report

Modernizing .NET Framework applications with UI layer
One major feature we’re releasing today is the modernization of UI frameworks from ASP.NET Web Forms to Blazor. This is added to existing support for modernizing model-view-controller (MVC) Razor views to ASP.NET Core Razor views.

As mentioned previously, if I have a .NET application in legacy .NET Framework, then I continue using AWS Transform for .NET to port it to cross-platform .NET. For legacy applications with UIs built on ASP.NET Web Forms, AWS Transform now modernizes the UI layer to Blazor along with porting the backend code.

AWS Transform for .NET converts ASP.NET Web Forms projects to Blazor on ASP.NET Core, facilitating the migration of ASP.NET websites to Linux. The UI modernization feature is enabled by default in AWS Transform for .NET on both the AWS Transform web console and Visual Studio extension.

During the modernization process, AWS Transform handles the conversion of ASPX pages, ASCX custom controls, and code-behind files, implementing them as server-side Blazor components rather than web assembly. The following project and file changes are made during the transformation:

From To Description
*.aspx, *.ascx *.razor .aspx pages and .ascx custom controls become .razor files
Web.config appsettings.json Web.config settings become appsettings.json settings
Global.asax Program.cs Global .asax code becomes Program.cs code
*.master *layout.razor Master files become layout.razor files

Image showcasing how the specific project files are transformed

Other new features in AWS Transform for .NET
Along with UI porting, AWS Transform for .NET has added support for more transformation capabilities and enhanced developer experience. These new features include the following:

  • Port to .NET 10 and .NET Standard – AWS Transform now supports porting to .NET 10, the latest Long-Term Support (LTS) release, which was released on November 11, 2025. It also supports porting class libraries to .NET Standard, a formal specification for a set of APIs that are common across all .NET implementations. Furthermore, AWS Transform is now available with AWS Toolkit for Visual Studio 2026.
  • Editable transformation report – After the assessment is complete, you can now view and customize the transformation plan based on your specific requirements and preferences. For example, you can update package replacement details.
  • Real-time transformation updates with estimated remaining time – Depending on the size and complexity of the codebase, AWS Transform can take some time to complete the porting. You can now track transformation updates in real-time along with the estimated remaining time.
  • Next steps markdown – After the transformation is complete, AWS Transform now generates a next steps markdown file with the remaining tasks to complete the porting. You can use this as a revised plan to repeat the transformation with AWS Transform or use AI code-companions to complete the porting.

Things to know
Some more things to know are:

  • AWS Regions – AWS Transform for full-stack Windows modernization is generally available today in the US East (N. Virginia) Region. For Regional availability and future roadmap, visit the AWS Capabilities by Region.
  • Pricing – Currently, there is no added charge for Windows modernization features of AWS Transform. Any resources you create or continue to use in your AWS account using the output of AWS Transform are billed according to their standard pricing. For limits and quotas, refer to the AWS Transform User Guide.
  • SQL Server versions supported – AWS Transform supports the transformation of SQL Server versions from 2008 R2 through 2022, including all editions (Express, Standard, and Enterprise). SQL Server must be hosted on Amazon RDS or Amazon EC2 in the same Region as AWS Transform.
  • Entity Framework versions supported – AWS Transform supports the modernization of Entity Framework versions 6.3 through 6.5 and Entity Framework Core 1.0 through 8.0.
  • Getting started – To get started, visit AWS Transform for full-stack Windows modernization User Guide.

– Prasad

AWS

Introducing AWS Transform custom: Crush tech debt with AI-powered code modernization

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Technical debt is one of the most persistent challenges facing enterprise development teams today. Studies show that organizations spend 20% of their IT budget on technical debt instead of advancing new capabilities. Whether it’s upgrading legacy frameworks, migrating to newer runtime versions, or refactoring outdated code patterns, these essential but repetitive tasks consume valuable developer time that could be spent on innovation.

Today, we’re excited to announce AWS Transform custom, a new agent that fundamentally changes how organizations approach modernization at scale. This intelligent agent combines pre-built transformations for Java, Node.js, and Python upgrades with the ability to define custom transformations. By learning specific transformation patterns and automating them across entire codebases, customers using AWS Transform custom have achieved up to 80% reduction in execution time in many cases, freeing developers to focus on innovation.

You can define transformations using your documentation, natural language descriptions, and code samples. The service then applies these specific patterns consistently across hundreds or thousands of repositories, improving its effectiveness through both explicit feedback and implicit signals like developers’ manual fixes within your transformation projects.

AWS Transform custom offers both CLI and web interfaces to suit different modernization needs. You can use the CLI to define transformations through natural language interactions and execute them on local codebases, either interactively or autonomously. You can also integrate it into code modernization pipelines or workflows, making it ideal for machine-driven automation. Meanwhile, the web interface provides comprehensive campaign management capabilities, helping teams track and coordinate transformation progress across multiple repositories at scale.

Language and framework modernization
AWS Transform supports runtime upgrades without the need to provide additional information, understanding not only the syntax changes required but also the subtle behavioral differences and optimization opportunities that come with newer versions. The same intelligent approach applies to Node.js, Python and Java runtime upgrades, and even extends to infrastructure-level transitions, such as migrating workloads from x86 processors to AWS Graviton.

It also navigates framework modernization with sophistication. When organizations need to update their Spring Boot applications to take advantage of newer features and security patches, AWS Transform custom doesn’t merely update version numbers but understands the cascading effects of dependency changes, configuration updates, and API modifications.

For teams facing more dramatic shifts, such as migrating from Angular to React, AWS Transform custom can learn the patterns of component translation, state management conversion, and routing logic transformation that make such migrations successful.

Infrastructure and enterprise-scale transformations
The challenge of keeping up with evolving APIs and SDKs becomes particularly acute in cloud-based environments where services are continuously improving. AWS Transform custom supports AWS SDK updates across a broad spectrum of programming languages that enterprises use including Java, Python, and JavaScript. The service understands not only the mechanical aspects of API changes, but also recognizes best practices and optimization opportunities available in newer SDK versions.

Infrastructure as Code transformations represent another critical capability, especially as organizations evaluate different tooling strategies. Whether you’re converting AWS Cloud Development Kit (AWS CDK) templates to Terraform for standardization purposes, or updating AWS CloudFormation configurations to access new service features, AWS Transform custom understands the declarative nature of these tools and can maintain the intent and structure of your infrastructure definitions.

Beyond these common scenarios, AWS Transform custom excels at addressing the unique, organization-specific code patterns that accumulate over years of development. Every enterprise has its own architectural conventions, utility libraries, and coding standards that need to evolve over time. It can learn these custom patterns and help refactor them systematically so that institutional knowledge and best practices are applied consistently across the entire application portfolio.

AWS Transform custom is designed with enterprise development workflows in mind, enabling center of excellence teams and system integrators to define and execute organization-wide transformations while application developers focus on reviewing and integrating the transformed code. DevOps engineers can then configure integrations with existing continuous integration and continuous delivery (CI/CD) pipelines and source control systems. It also includes pre-built transformations for Java, Node.js and Python runtime updates which can be particularly useful for AWS Lambda functions, along with transformations for AWS SDK modernization to help teams get started immediately.

Getting started
AWS Transform makes complex code transformations manageable through both pre-built and custom transformation capabilities. Let’s start by exploring how to use an existing transformation to address a common modernization challenge: upgrading AWS Lambda functions due to end-of-life (EOL) runtime support.

For this example, I’ll demonstrate migrating a Python 3.8 Lambda function to Python 3.13, as Python 3.8 reached EOL and is no longer receiving security updates. I’ll use the CLI for this demo, but I encourage you to also explore the web interface’s powerful campaign management capabilities.

First, I use the command atx custom def list to explore the available transformation definitions. You can also access this functionality through a conversational interface by typing only atx instead of issuing the command directly, if you prefer.

This command displays all available transformations, including both AWS-managed defaults and any existing custom transformations created by users in my organization. AWS-managed transformations are identified by the AWS/ prefix, indicating they’re maintained and updated by AWS. In the results, I can see several options such as AWS/java-version-upgrade for Java runtime modernization, AWS/python-boto2-to-boto3-migration for updating Python AWS SDK usage, AWS/nodejs-version-upgrade for Node.js runtime updates.

For my Python 3.8 to 3.13 migration, I’ll use the AWS/python-version-upgrade transformation.

You run a migration by using the atx custom def exec command.  Please consult the documentation for more details about the command and all its options. Here, I run it against my project repository specifying the transformation name. I also add pytest to run unit tests for validation. More importantly, I use the additionalPlanContext section in the  --configuration input to specify which Python version I want to upgrade to. For reference, here’s the command I have for my demo (I’ve used multiple lines and indented it here for clarity):

atx custom def exec 
-p /mnt/c/Users/vasudeve/Documents/Work/Projects/ATX/lambda/todoapilambda 
-n AWS/python-version-upgrade
-C "pytest" 
--configuration 
    "additionalPlanContext= The target Python version to upgrade to is Python 3.13" 
-x -t

AWS Transform then starts the migration process. It analyzes my Lambda function code, identifies Python 3.8-specific patterns, and automatically applies the necessary changes for Python 3.13 compatibility. This includes updating syntax for deprecated features, modifying import statements, and adjusting any version-specific behaviors.

After execution, it provides a comprehensive summary including a report on dependencies updated in requirements.txt with Python 3.13-compatible package versions, instances of deprecated syntax replaced with current equivalents, updated runtime configuration notes for AWS Lambda deployment, suggested test cases to validate the migration, and more. It also provides a body of evidence that serve as proof of success.

The migrated code lives in a local branch so you can review and merge when satisfied. Alternatively, you can keep providing feedback and reiterating until yo’re happy that the migration is fully complete and meets your expectations.

This automated process changes what would typically require hours of manual work into a streamlined, consistent upgrade that maintains code quality while maintaining compatibility with the newer Python runtime.

Creating a new custom transformation
While AWS-managed transformations handle common scenarios effectively, you can also create custom transformations tailored to your organization’s specific needs. Let’s explore how to create a custom transformation to see how AWS Transform learns from your specific requirements.

I type atx to initialize the atx cli and start the process.

The first thing it asks me is if I want to use one of the existing transformations or create a new one. I choose to create a new one. Notice that from here on the whole conversation takes place using natural language, not commands. I typed new one but I could have typed I want to create a new one and it would’ve understood it exactly the same.

It then prompts me to provide more information about the kind of transformation I’d like to perform. For this demo, I’m going to migrate an Angular application, so I type angular 16 to 19 application migration which prompts the CLI to search for all transformations available for this type of migration. In my case, my team has already created and made available a few Angular migrations, so it shows me those. However, it warns me that none of them is an exact match to my specific request for migrating from Angular 16 to 19. It then asks if I’d like to select from one of the existing transformations listed or create a custom one.

I choose to create a custom one by continuing to use natural language and typing create a new one as a command. Again, this could be any variation of that statement provided that you indicate your intentions clearly. It follows by asking me a few questions including whether I have any useful documentation, example code or migration guides that I can provide to help customize the transformation plan.

For this demo, I’m only going to rely on AWS Transform to provide me with good defaults. I type I don't have these details. Follow best practices. and the CLI responds by telling me that it will create a comprehensive transformation definition for migrating Angular 16 to Angular 19.  Of course, I relied on the pre-trained data to generate results based on best practices. As usual, the recommendation is to provide as much information and relevant data as possible at this stage of the process for better results. However, you don’t need to have all the data upfront. You can keep on providing data at any time› as you iterate through the process of creating the custom transformation definition.

The transformation definition is generated as a markup file containing a summary and a comprehensive sequence of implementation steps grouped logically into phases such as premigration preparation, processing and partitioning, static dependency analysis, searching and applying specific transformation rules, and step-by-step migration and iterative validation.

It’s interesting to see that AWS Transform opted for the best practice of doing incremental framework updates creating steps for migrating the application first to 17 then 18 then 19 instead of trying to go directly from 16 to 19 to minimize issues.

Note that the plan includes various stages of testing and verification to confirm that the various phases can be concluded with confidence. At the very end, it also includes a final validation stage listing exit criteria that performs a comprehensive set of tests against all aspects of the application that will be used to accept the migration as successfully complete.

After the transformation definition is created, AWS Transform asks me about what I would like to do next. I can choose to review or modify the transformation definition and I can reiterate through this process as much as I need until I arrive at one that I’m satisfied with. I can also choose to already apply this transformation definition to an Angular codebase. However, first I want to make this transformation available to my team members as well as myself so we can all use it again in the future. So, I choose option 4 to publish this transformation to the registry.

This custom transformation needs a name and a description of its objective which is displayed when users browse the registry. AWS Transforms automatically extracts those from context for me and asks me if I would like to modify them before going ahead. I like the sensible default of “Angular-16-to-19-Migration”, and the objective is clearly stated, so I choose to accept the suggestions and publish it by answering with yes, looks good.

Now that the transformation definition is created and published, I can use it and run it multiple times against any code repository. Let’s apply the transformation to a code repository with a project written in Angular 16. I now choose option 1 from the follow-up prompt and the CLI asks me for the path in my file system to the application that I want to migrate and, optionally, the build command that it should use.

After I provide that information, AWS Transform proceeds to analyze the code base and formulate a thorough step-by-step transformation plan based on the definition created earlier. After it’s done, it creates a JSON file containing the detailed migration plan specifically designed for applying our transformation definition to this code base. Similar to the process of creating the transformation definition, you can review and iterate through this plan as much as you need, providing it with feedback and adjusting it to any specific requirements you might have.

When I’m ready to accept the plan, I can use natural language to tell AWS Transform that we can start the migration process. I type looks good, proceed and watch the progress in my shell as it starts executing the plan and making the changes to my code base one step at a time.

The time it takes will vary depending on the complexity of the application. In my case, it took a few minutes to complete. After it has finished, it provides me with a transformation summary and the status of each one of the exit criteria that were included in the final verification phase of the plan alongside all the evidence to support the reported status. For example, the Application Build – Production criteria was listed as passed and some of the evidence provided included the incremental Git commits, the time that it took to complete the production build, the bundle size, the build output message, and the details about all the output files created.

Conclusion
AWS Transform represents a fundamental shift in how organizations approach code modernization and technical debt. The service helps to transform what was at one time a fragmented, team-by-team effort into a unified, intelligent capability that eliminates knowledge silos, keeping your best practices and institutional knowledge available as scalable assets across the entire organization. This helps to accelerate modernization initiatives while freeing developers to spend more time on innovation and driving business value instead of focusing on repetitive maintenance and modernization tasks.

Things to know

AWS Transform custom is now generally available. Visit the get started guide to start your first transformation campaign or check out the documentation to learn more about setting up custom transformation definitions.