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

Introduction

Migrating a mature PHP codebase to Java can unlock stronger type safety, richer ecosystem libraries, and more predictable scaling. The challenge is avoiding a one‑off, error‑prone rewrite. Kiro, an AI‑integrated development environment, lets you codify the migration process into a reusable engine that treats the original PHP app as the specification and validates the Java output with a parity harness. This article walks you through the shared methodology, the discovery scanner, and the eight‑phase workflow, and shows how the same skill files can be run interactively in Kiro or at scale with AWS Transform.

1. Prerequisites and What You’ll Gain

• Skill set: Familiarity with PHP (Laravel/Eloquent or Symfony/Doctrine) and basic Java (Spring Boot, JPA). You don’t need to be a Java expert; the engine generates idiomatic code.
• Environment: PHP 8+ and Java 21 with Maven installed, plus a Kiro workspace with access to the `.kiro/` skill files.
• Outcome: A repeatable migration engine that produces a Java project verified against a parity test harness, and a set of skill files you can reuse across multiple applications.

2. Shared Methodology – One Brain, Many Projects

The engine is organized around a global `.kiro/` layer that stores two core Skills:

• `php-model-discovery` – a lightweight, dependency‑free scanner that extracts entity definitions from PHP source.
• `php-to-java-migration` – an eight‑phase playbook that drives discovery, translation, and validation.

Each project folder contains its own PHP source, generated Java output, and a `specs/` sub‑folder for the parity harness. Updating a skill file once instantly improves every project that references it.

3. Phase‑Zero Discovery – The Foundation

Before any translation occurs, the scanner (`discover-models.php`) runs a single pass over the codebase and produces a JSON manifest:

• Eloquent model detection – walks inheritance chains to `Illuminate\Database\Eloquent\Model`.
• Doctrine attribute detection – reads `#[ORM\Entity]` attributes.
• XML mapping detection – parses `*.orm.xml` files.

The manifest lists each model’s fields, casts, relationships, and visibility rules. Ambiguities (e.g., dynamically built `$fillable` arrays) are flagged for human review rather than guessed, ensuring the downstream translation has a reliable source of truth.

4. The Eight‑Phase Parity‑First Pipeline

| Phase | Goal | Human Interaction | |------|------|-------------------| | 0 – Discovery | Run the scanner, assemble the model inventory. | Review flagged ambiguities. | | 1 – Harness Build | Generate a test harness that records request/response pairs against the PHP app. | Approve harness design. | | 2 – Rulebook Creation | Translate the JSON manifest into a migration rulebook (mapping PHP types to Java types, relationship conventions, etc.). | Validate mapping decisions. | | 3 – Code Generation | Use the rulebook to emit Spring Boot entities, repositories, and service stubs. | Spot‑check a sample of generated classes. | | 4 – Integration Stitching | Wire generated code into a runnable Maven project, preserving routing and middleware logic. | Confirm project structure. | | 5 – Parity Execution | Replay the harness against both PHP and Java implementations, comparing outputs. | Review any mismatches. | | 6 – Fix‑the‑Loop | Iterate on rulebook or generated code to resolve parity failures; the engine never edits the original PHP. | Approve fixes before proceeding. | | 7 – Final Validation | Run full test suites, performance benchmarks, and static analysis on the Java project. | Sign‑off for production rollout. |

The pipeline halts at each gate until you explicitly approve the step, guaranteeing that no automated translation proceeds without verification.

5. Running the Engine: Kiro vs. AWS Transform

• Kiro (interactive) – Open the repository as a Kiro workspace. Invoke the migration skill, and Kiro’s AI agent guides you through each phase, pausing for approvals and displaying flagged items. Ideal for a first‑time migration or when you need close inspection of generated code.
• AWS Transform (agentic, portfolio scale) – Upload the same skill files to AWS Transform. The service executes the eight‑phase pipeline autonomously across many repositories, still respecting the approval checkpoints defined in the skill files. Use this when you have dozens of similar PHP services to modernize.

Both paths share the exact same knowledge base; improvements made during a Kiro session (e.g., refining a rulebook entry) automatically benefit subsequent AWS Transform runs.

6. Practical Tips for Success

1. Treat flags as a priority list – Resolve the most ambiguous models first; they often hide business‑critical logic.
2. Version your skill files – Store them in Git alongside your migration repository so you can roll back or branch methodology changes.
3. Automate harness generation – Capture end‑to‑end API calls early; the richer the harness, the tighter the parity checks.
4. Leverage Maven profiles – Separate generated code from hand‑written extensions to keep future maintenance clean.
5. Iterate quickly – Run Phase 3 on a small subset of models to validate the rulebook before scaling to the full codebase.

Conclusion

By encapsulating discovery, translation, and validation into a set of versioned skill files, Kiro turns a risky, manual rewrite into a measurable, repeatable migration engine. Whether you prefer the hands‑on guidance of Kiro or the high‑throughput automation of AWS Transform, the same eight‑phase parity‑first workflow ensures that every line of generated Java faithfully mirrors the behavior of the original PHP application. Adopt this methodology once, and you’ll have a scalable path for modernizing legacy PHP services across your portfolio.