Migrating a Complex Java 8 Application to Java 17 Using GitHub Copilot

DataOps LabsDataOps Labs
3 min read

🚀 Migrating a Complex Java 8 Application to Java 17 Using GitHub Copilot

Migrating a legacy Java 8 application—especially one filled with SOAP APIs and outdated structures—to Java 17 can feel overwhelming. But with a structured approach and the help of GitHub Copilot, you can make this transformation systematic, test-driven, and efficient.

This guide focuses on Copilot-driven prompts to guide every step of your migration journey.


📌 Project Background

  • Current Java Version: 1.8
  • Target Version: Java 17
  • Architecture: Legacy SOAP-based services
  • Build Tool: Gradle (VM setup)
  • Objective: Migrate the full codebase and tests to Java 17 using BDD + TDD principles.

🧭 Migration Roadmap

✅ Step 1: Understand the Legacy Code — Generate BDD Scenarios

Goal: Scan the existing Java 8 code and translate the behavior into BDD format using Gherkin.

Copilot Prompt:

"Analyze this legacy Java 8 service class and generate Gherkin-style BDD scenarios describing its business behavior using Given-When-Then syntax."

Expected Output:

Feature: Payment Processing
  Scenario: Successful payment
    Given the user has a valid account
    When they initiate a payment
    Then the payment should be processed and a receipt generated

✅ Step 2: Translate BDD to Java 17 TDD Test Cases

Goal: Use Copilot to convert BDD features into JUnit 5 test cases.

Copilot Prompt:

"Generate a JUnit 5 test class for the following Gherkin scenario using Java 17 features and best practices."

Example Test:

@Test
void testPaymentSuccess() {
    PaymentService service = new PaymentService();
    boolean result = service.process("validUser", 100);
    assertTrue(result);
}

✅ Step 3: Compare Existing Java 8 Tests vs New Java 17 TDD

Goal: Identify gaps and outdated logic between old and new tests.

Copilot Prompt:

"Compare these Java 8 test cases with the new Java 17 test cases and list any missing scenarios or deprecated logic."

What to Look For:

  • Coverage gaps
  • Deprecated JUnit 4 methods
  • Missing edge-case tests

✅ Step 4: Rewrite or Refactor Implementation in Java 17

Goal: Build a new Java 17-compliant implementation using your test cases as a blueprint.

Copilot Prompt:

"Write Java 17 implementation code that satisfies this JUnit 5 test case. Use Java 17 constructs like records, switch expressions, and sealed classes."

Example Code:

public record User(String id, String role) {}

public boolean isAdmin(User user) {
    return switch (user.role()) {
        case "admin" -> true;
        default -> false;
    };
}

✅ Step 5: Run Java 17 Tests Against New Code (Gradle)

Goal: Use Gradle to compile and run all tests, ensuring compatibility and correctness.

Copilot Prompt:

"Create a Gradle test task using Java 17 with JUnit 5 support and show test output for debugging."

build.gradle Settings:

test {
    useJUnitPlatform()
    jvmArgs '--enable-preview'
}

Run Tests:

./gradlew clean test

🏗️ Optional: Directory Structure for Migration

legacy-to-java17/
├── src/
│   ├── main/java/com/yourcompany/
│   └── test/java/com/yourcompany/
│       └── resources/features/
├── build.gradle
└── settings.gradle

🧠 Final Thoughts

GitHub Copilot can accelerate your modernization efforts dramatically—but only if your prompts are precise and contextual. This blog provides a roadmap and prompt-driven workflow that makes even the most complex migrations manageable.

Start small, test continuously, and let AI be your co-pilot on this transformation journey.


✨ Bonus Tip

Use the following prompt template to document each service:

"Summarize the responsibility of this class and generate BDD + TDD for each public method."

Happy Migrating! ☕️➡️🚀

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Written by

DataOps Labs
DataOps Labs

I'm Ayyanar Jeyakrishnan ; aka AJ. With over 18 years in IT, I'm a passionate Multi-Cloud Architect specialising in crafting scalable and efficient cloud solutions. I've successfully designed and implemented multi-cloud architectures for diverse organisations, harnessing AWS, Azure, and GCP. My track record includes delivering Machine Learning and Data Platform projects with a focus on high availability, security, and scalability. I'm a proponent of DevOps and MLOps methodologies, accelerating development and deployment. I actively engage with the tech community, sharing knowledge in sessions, conferences, and mentoring programs. Constantly learning and pursuing certifications, I provide cutting-edge solutions to drive success in the evolving cloud and AI/ML landscape.