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

🚀 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Migrating a legacy Java 8 application—especially one filled with SOAP APIs and outdated structures—to Java 17 can feel overwhelming.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Goal: Identify gaps and outdated logic between old and new tests.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.