Build a repeatable PHP-to-Java migration engine with Kiro

When your team moves a mature PHP application to Java, you gain stronger type safety, broader library support, and more predictable scaling. A PHP codebase built over the years with Laravel, Symfony, or Doctrine carries business rules in places that are not obvious, from Eloquent traits to XML mapping files, so a structurally similar Java port can compile cleanly and still behave differently.

This post shows how to turn PHP-to-Java migration from a one-time leap of faith into a measurable, repeatable process using Kiro . Kiro is an AI integrated development environment (IDE) that pairs an AI agent with your codebase to run multi-step engineering tasks with you in the loop. The approach centers on a reusable engine: a shared methodology, a mechanical discovery scanner, and an eight-phase workflow. This engine treats the original PHP application as the specification and refuses to declare the Java port correct until a parity harness verifies it.

You start with an open repository, so you can run the engine yourself rather than read about it. Everything the engine knows lives in a small set of skill files, and with those files, you can run the same methodology in more than one way.

The mapping from PHP to Spring Boot is mechanical and traceable, as shown in the following diagram. Each manifest entry on the left maps to a Java construct on the right, producing the layered runtime architecture of the generated application slice.

Figure 5: Manifest-driven mappings on the left. The layered runtime architecture of the generated application slice on the right

Everything here is packaged as skill files: versioned Markdown capability packages that encode the migration methodology (php-to-java-migration) and the discovery scanner workflow (php-model-discovery). Because that knowledge lives in files rather than any one tool, you can run the same methodology in two ways.

Kiro brings an AI agent into your editor to drive a migration step by step. Open the repository root as a Kiro workspace and the skills activate automatically when you ask Kiro to migrate a PHP application. Kiro then walks the eight-phase workflow with explicit approval gates, so you review the discovery manifest, approve the rulebook, and sign off on each phase before the next begins. It’s the mode used for every example in this post.

A shared, global layer under .kiro/ holds two Skills, php-model-discovery (the scanner) and php-to-java-migration (the eight-phase playbook), plus the core-workflow.md Steering file. Each per-project folder holds its own PHP source, Java output, and spec sub-folder under .kiro/specs/, so improving the methodology once lets every project inherit the gain.

AWS Transform is the AWS agentic AI service for large-scale migration and modernization, and it now integrates natively with Kiro. Through that integration, you can hand AWS Transform the same skill files that govern an interactive Kiro session, so discovery before translation, the parity harness as referee, and ‘ fix the loop, not the code ’ apply to an autonomous run.

The engine is organized around a single principle: one shared brain, many projects. To open it, clone the repository locally, then in Kiro open a folder and select the repository root (the folder containing .kiro/). Kiro loads the skills and steering automatically, with fuller steps in “Getting started.”

The workspace separates a shared methodology layer from the individual projects that use it, as shown in the following diagram. At the top is a global .kiro/ layer that every project draws on. Beneath it sit the per-project folders, each with its own PHP source, Java output, and specification sub-folder.