How Generative AI Can Transform Legacy System Modernization

Scenario‑Driven Design for Legacy Modernization

Legacy applications often consist of mixed languages, tangled code, and scarce documentation. A scenario‑driven approach first identifies concrete developer activities—coding, debugging, testing, integration—where generative AI can add the most value, then tailors AI functions to those contexts.

AI‑Powered IDE Capabilities

Code enhancement : AI‑assisted completion, documentation generation, test case creation, and automated refactoring.

Activity enhancement : AI‑driven error fixing, commit‑message synthesis, code‑review assistance.

Integration of other activities : Embedding documentation lookup, API discovery, and build‑script generation directly in the IDE.

Legacy System Modernization Workflow

Define evaluation metrics and quality gates.

Build a safety‑net of automated tests (unit, integration, contract).

Redesign architecture—e.g., apply Domain‑Driven Design for modularity.

Extract business logic into services and define clear boundaries.

Perform fine‑grained refactoring of code, scripts, and build pipelines.

After the high‑level design is completed, generative AI can accelerate each step.

AI‑Assisted Enhancements

Conversational design of migration plans and architecture suggestions.

Automatic generation of multi‑dimensional test data and test cases.

Extraction of business information from APIs via call‑graph analysis and documentation synthesis.

Infrastructure script rewriting (e.g., Maven → Gradle, Dockerfile generation).

Source‑code translation (e.g., JavaScript → TypeScript, Java → Kotlin) and logic optimisation.

Regeneration of up‑to‑date documentation from source code.

Handling of complex artefacts such as stored‑procedure migration, custom build scripts, and domain‑specific languages.

End‑to‑End Custom Solution (COBOL Migration Example)

Understanding : Analyse COBOL code, data dependencies, and runtime environment; visualise the legacy structure.

Refactoring : Decouple COBOL modules and re‑architect them into a modular design.

Conversion : Translate the modularised code into Java and generate corresponding Java test suites for verification.

Design Considerations for AI‑Enhanced Refactoring

When to develop new features that align with migration needs?

When to expose customizable capabilities for specific scenarios?

When to leave decisions entirely to developers to preserve flexibility?

The goal is to expose simple, single‑click workflows such as test‑generation or validation actions.

Validation of Generated Code

Leverage AI‑assisted code review to flag potential issues.

Generate functional tests that validate business logic against expected input‑output behaviour.

Derive unit tests from existing test‑case documentation and run them automatically.

Human verification remains essential for critical paths, especially for simple API contracts.

Custom Language‑Conversion Action

{
  "title": "Translate to Kotlin",
  "autoInvoke": false,
  "matchRegex": ".*",
  "priority": 0,
  "template": "Translate the following code to Kotlin.
${SIMILAR_CHUNK}
Compare these snippets:
${METHOD_INPUT_OUTPUT}
Original code:
${SELECTION}"
}

The placeholders ${SIMILAR_CHUNK} and ${METHOD_INPUT_OUTPUT} provide contextual snippets to the LLM, improving translation accuracy.

Living Documentation Action

{
  "title": "Living Documentation",
  "prompt": "Write living documentation in the following format:",
  "start": "",
  "end": "",
  "type": "annotated",
  "example": {
    "question": "public BookMeetingRoomResponse bookMeetingRoom(@RequestBody BookMeetingRoomRequest request) { ... }",
    "answer": "@ScenarioDescription(given = \"there is a meeting room available with ID 123\", when = \"a user books the meeting room with ID 123\", then = \"the booking response should contain the details of the booked meeting room\")"
  }
}

This JSON config lets developers annotate code and have the LLM generate structured, up‑to‑date documentation.

Proprietary Feature: API Test Data Generation

A built‑in feature can generate realistic API request/response payloads based on OpenAPI specifications, reducing manual effort while keeping the implementation simple and fast.

Reference Implementation

The open‑source AutoDev project demonstrates these patterns and provides ready‑to‑use plugins: https://github.com/unit-mesh/auto-dev