AWS Transform custom: A Practical Code Upgrade Guide

The same dependency upgrade can land on several teams’ backlogs. Each team then has to work out what might break and how to check the result. For repeated changes, AWS Transform custom uses AI agents to update application code, libraries and frameworks using instructions, documentation and examples supplied by developers.

The sensible starting point is a small pilot. It should show whether the tool can produce changes your team can review, test and maintain with a reasonable amount of effort.

What the tool is designed to do

Version upgrades, API migrations, framework changes and organisation-specific refactoring fall within the service’s documented scope. Their complexity varies considerably. Updating one dependency offers limited evidence about the effort involved in replacing an entire framework.

AWS-managed transformations provide prepared definitions for common tasks, including Java, Python, Node.js and AWS software development kit (SDK) upgrades. Teams can also create custom definitions for their own libraries, coding patterns and requirements.

Before creating a definition, check whether a managed option covers the intended change. Evaluate either route against the same criteria: compatibility, preserved behaviour, test results and the work needed to correct the output.

Describe the change in practical terms

A transformation definition gives the agent instructions for a particular change. You can create and refine one through plain language, migration notes and example code.

A useful definition identifies:

• The current technology and intended target.
• Files and components that may change.
• Behaviour the application must preserve.
• Known exceptions and unsupported patterns.
• Build commands and acceptance checks.

Imagine a team replacing an internal notification client. The replacement reports failed deliveries differently. Its transformation instructions need to preserve retries, message identifiers and audit records while explaining how to handle the new responses.

Include examples of successful and failed deliveries. A reviewer can then compare the output with a clear requirement instead of guessing what the migration was supposed to achieve.

Build a pilot around real conditions

AWS describes a process of defining the transformation, testing it through a pilot, scaling execution and reviewing results. The pilot helps establish the likely effort and service usage before broader adoption.

Establish a working baseline

Build the application and run its existing tests before changing anything. Record known failures and keep the starting commit available for comparison and recovery. If the baseline is unstable, resolve that uncertainty before using it to judge the upgrade.

The command-line interface (CLI) requires a Git repository, suitable AWS permissions and a supported environment. Check the configured AWS Region as part of setup because availability is region-specific.

Choose representative repositories

Include a straightforward service and another with unusual configuration or awkward dependencies. Testing only the cleanest repository leaves important exceptions unexplored.

For the notification example, a web service and a scheduled job make useful comparison cases. Both may use the same client while handling delivery failures differently.

Give the agent meaningful checks

Supply explicit build and test commands so the agent receives concrete feedback about the changes. The checks need to exercise the behaviour affected by the upgrade.

For notification delivery, that means checking retries, duplicate prevention and failure reporting. Compilation alone leaves these questions unanswered. Where possible, run focused integration tests against a controlled test environment.

Review the changes and remaining gaps

Read the code diff alongside the test results and transformation report. AWS provides a report describing the outcome and can recommend further action when validation is incomplete.

Pay close attention to unrelated edits, weakened test assertions, changed error handling and configuration updates that affect deployment. If a test disappeared or became easier to pass, establish the reason before approving the change.

A notification service could compile successfully while losing the identifier needed to investigate failed deliveries. A targeted check should confirm that the identifier remains available throughout the relevant workflow.

The pilot record needs to distinguish accepted changes, manual corrections and unresolved issues. Give each outstanding issue an owner before planning the next batch.

Measure the work before expanding

A wider rollout of AWS Transform custom should be based on the total effort needed to accept a change. Track execution time, developer review, manual corrections and unresolved defects. Compare similar repositories before estimating the wider programme.

The service extracts lessons from execution and developer feedback. These lessons are specific to a transformation, and teams can review and manage them.

Check that learned guidance fits the repositories where it will be reused. A rule that works for a scheduled job may need qualification before it applies to a customer-facing service.

Expand in manageable batches once results are consistent. Repeated exceptions call for a clearer definition, a narrower scope or separate treatment for repositories with different requirements.

Key Takeaways

• Choose a recurring upgrade with clear boundaries.
• Define the behaviour and exceptions that matter.
• Test representative code against a dependable baseline.
• Include review and correction effort in rollout estimates.

Let the pilot guide the rollout

Useful automation leaves the team with code it can understand and maintain. A pilot provides evidence for expanding the work, narrowing the scope or reconsidering the approach.

Use this Ebtechsol guide to agree the scope, checks and owner of one pilot before planning a wider rollout.

FAQs About Custom Code Upgrades

Is this the mainframe migration tool?

No. AWS directs COBOL and mainframe transformation needs to AWS Transform for Mainframe. Choose the capability that matches the workload and the intended change.

Can I add requirements to a managed definition?

Yes. The additionalPlanContext configuration parameter lets you supply project-specific guidance when running an AWS-managed transformation. The underlying managed definition remains unchanged.

How do I assess an application with few tests?

Establish checks for its most important behaviour first. Limited test coverage makes regressions harder to identify and increases the manual verification needed to evaluate the pilot.

Can I inspect the changes with Git?

Yes. Standard Git commands let developers inspect transformation changes. Compare the resulting code with the starting commit to understand the complete set of edits.

Can I guide the agent while it works?

Yes. Interactive conversational mode allows developers to interrupt execution and provide feedback. This helps clarify unexpected cases while testing and refining a transformation.

Can an unhelpful lesson be disabled?

Yes. Archiving a lesson stops the system applying it to future runs. Archived lessons can be restored or permanently deleted when they are no longer useful.

Are definitions shared across AWS accounts?

Definitions are account-specific. To use a definition in another AWS account, publish it separately there and configure the permissions required by the people running it.

Is IAM Identity Center required for the CLI?

No. The CLI can use standard AWS credentials. The AWS Transform web application requires IAM Identity Center, together with the appropriate access configuration.

How do I estimate the pilot’s duration?

Base the estimate on the change’s complexity, available examples and validation effort. Include time for investigation, corrections and review before using the pilot to forecast a larger project.

What if repositories produce different results?

Compare their dependencies, configuration and code patterns. Clarify the requirements or separate incompatible cases, then retest the affected repositories before extending the rollout.