Transforming Legacy C Code to Modern Java/Spring Framework: A Systematic Approach Using Amazon Bedrock Converse API

Abstract

Modern enterprises are encumbered by critical systems reliant on outdated technologies, making maintenance and extension increasingly challenging. This article illustrates how to leverage the Amazon Bedrock Converse API alongside Amazon Nova Premier in an agentic workflow to effectively transition legacy C code to contemporary Java/Spring applications.

Key Benefits of the Approach

1. Reduced Migration Time and Cost: Automation streamlines repetitive tasks, allowing engineers to concentrate on high-value activities.
2. Improved Code Quality: Specialized validation agents ensure adherence to modern best practices during migration.
3. Minimized Risk: A systematic migration process prevents the loss of critical business logic.
4. Enhanced Cloud Integration: The resultant Java/Spring code seamlessly integrates with AWS services.

Addressing Migration Challenges

Migration from legacy systems to modern frameworks introduces significant challenges, necessitating a balanced approach that merges AI functionalities with human oversight:

• Language Paradigm Differences: Navigating between C’s procedural nature and Java’s object-oriented model.
• Architectural Complexity: Understanding intricate dependencies within legacy systems.
• Maintaining Business Logic: Preserving critical business logic during translation, especially in extensive files.
• Inconsistent Naming and Structures: Standardizing legacy code naming conventions.
• Integration Complexity: Ensuring cohesive communication among converted modules.

Agentic Workflow Overview

Utilizing Amazon Bedrock Converse API with Amazon Nova Premier, the migration is conducted through a structured multi-agent workflow comprising:

1. Code Analysis Agent: Evaluates the C codebase’s structure and dependencies.
2. Conversion Agent: Transforms C code into Java/Spring.
3. Security Assessment Agent: Identifies vulnerabilities in both legacy and migrated code.
4. Validation Agent: Ensures completeness and accuracy of the conversion.
5. Refine Agent: Implements feedback from validation and security assessments.
6. Integration Agent: Combines individual converted files into a cohesive application.

Prerequisites for Implementation

• An AWS account with access to Amazon Bedrock.
• Development environment set up with the required SDKs, IDE, and version control systems.
• A structured source code directory for legacy C and target Java applications.

Conclusion

The deployment of the Amazon Bedrock Converse API within a systematic agentic workflow provides a robust platform for migrating legacy C code to the Java/Spring framework. Through careful planning, specialized agent roles, and efficient handling of token limitations, organizations can significantly enhance migration accuracy and reduce the potential for errors.

About the Authors

• Aditya Prakash: Senior Data Scientist specializing in generative AI solutions for AWS.
• Jihye Seo: Senior Deep Learning Architect focused on generative AI applications across industries.
• Yash Shah: Science Manager at AWS with expertise in machine learning use cases from diverse sectors.

This structured approach provides enterprises with the tools required to transition smoothly to modern frameworks while ensuring quality, security, and maintainability.

Migrating Legacy C Code to Modern Java/Spring Framework Applications with Amazon Bedrock Converse API

In today’s fast-paced digital landscape, many enterprises find themselves trapped with mission-critical systems built on outdated technologies. These legacy systems, often written in languages like C, are becoming increasingly challenging to maintain and extend. However, the migration to modern frameworks like Java/Spring doesn’t need to be fraught with risk or inefficiency. Leveraging the Amazon Bedrock Converse API with Amazon Nova Premier within an agentic workflow can smartly facilitate this transition.

Why Migrate?

Migrating legacy systems to modern architectures can unlock several benefits for enterprises:

• Reduced Migration Time and Cost: Automation of repetitive conversion tasks allows engineers to focus on high-value work.
• Improved Code Quality: Specialized agents enforce adherence to modern best practices during code migration.
• Minimized Risk: A systematic approach helps prevent the loss of critical business logic.
• Seamless Cloud Integration: Java/Spring applications can easily integrate with AWS services, enhancing scalability and functionality.

Challenges in Code Migration

Despite the advantages, several challenges complicate the migration process:

1. Language Paradigm Differences: Converting from C, a procedural language with manual memory management, to Java, which employs automatic memory management and an object-oriented approach, requires careful oversight.

2. Architectural Complexity: Legacy systems often feature intricate interdependencies that necessitate strategic planning for a successful migration.

3. Maintaining Business Logic: Ensuring critical business logic remains intact involves meticulous human review, especially for complex code.

4. Inconsistent Naming and Structures: Migrating often unstructured legacy code necessitates standardization during transition.

5. Integration Complexity: After conversion, extensive manual integration is needed to create a cohesive application from individual files.

6. Quality Assurance: Advanced testing, including automated tests and human verification, is required to confirm that the converted code behaves as expected.

A Systematic Solution Using Amazon Bedrock Converse API

The solution employs the Amazon Bedrock Converse API along with Amazon Nova Premier, wrapped in a systematic agentic workflow consisting of specialized roles:

Key Components of the Migration Framework

1. Code Analysis Agent: Analyzes C code structure and interdependencies.
2. Conversion Agent: Transforms C code into Java/Spring framework code.
3. Security Assessment Agent: Identifies vulnerabilities in legacy and migrated code.
4. Validation Agent: Verifies completeness and accuracy.
5. Refine Agent: Rewrites code based on feedback.
6. Integration Agent: Combines converted files into a cohesive application.

Workflow Steps

1. Code Analysis: The code analysis agent identifies dependencies, complexities, and categorizes files based on their size.

2. File Categorization and Metadata Creation: Establishes file types and categorizes them into simple, medium, and complex.

3. Individual File Conversion: The conversion agent executes code migration, handling large files through token optimization techniques.

4. Security Assessment: Comprehensive vulnerability analysis ensures migration does not carry forward security issues.

5. Validation and Feedback Loop: Iterative feedback allows for continual improvement of the converted code.

6. Integration and Finalization: The integration agent consolidates the converted files and resolves naming conflicts.

7. DBIO Conversion: A specialized agent transforms SQL code into formats compatible with Java/Spring frameworks.

Implementation Prerequisites

To implement this solution, ensure the following components are in place:

• AWS Environment: Access to Amazon Bedrock, EC2 instance for development, etc.
• Development Setup: Python, Boto3 SDK, Strands Agents, and Git for version control.
• Source/Target Code Requirements: Organized C source code and a Java setup with Spring dependencies.

Handling Token Limitations

Token limitations in the Amazon Bedrock Converse API can hinder the conversion of large files. We tackle this through:

• Response Monitoring: The system triggers continuation when the response exceeds token limits.
• Context Preservation: Last few lines of generated code are used as context for continuations.
• Response Stitching: Smoothly combines multiple responses while maintaining code integrity.

Optimizing Migration Success

Through practical implementations, several factors have been identified to enhance migration quality:

• Iterative Refinement: Multiple feedback loops yield thorough conversions.
• Detail-Oriented Instructions: Providing clear instructions on transformation enhances consistency.

Results and Performance Metrics

Our migration approach has proven effective in various enterprise scenarios, demonstrating strong performance across different file sizes. The conversion accuracy varies by code complexity:

• Small Files (0–300 lines): 93% structural completeness
• Medium Files (300–700 lines): 81% structural completeness
• Large Files (>700 lines): 62% structural completeness

These findings highlight the hybrid approach’s efficiency: AI drives routine conversions, while human oversight ensures complex logic is accurately translated.

Conclusion

The integration of the Amazon Bedrock Converse API with an agentic workflow represents a robust solution for migrating legacy C code to modern Java/Spring frameworks. By combining automated transformation with human oversight, organizations can expedite their migration processes, enhance code quality, and mitigate risks.

About the Authors

Aditya Prakash, Jihye Seo, and Yash Shah are AI/ML experts with extensive experience in developing and implementing generative AI solutions. They focus on helping enterprises leverage technologies for better strategic and operational outcomes.

This solution presents an opportunity for enterprises grappling with legacy code to transition seamlessly to a more agile, cloud-based architecture. If you have further questions or wish to explore this migration approach for your organization, share your comments below!