Streamline code migration utilizing Amazon Nova Premier with an agentic workflow

Many enterprises are burdened with mission-critical techniques constructed on outdated applied sciences which have change into more and more troublesome to keep up and prolong.

This put up demonstrates how you should utilize the Amazon Bedrock Converse API with Amazon Nova Premier inside an agentic workflow to systematically migrate legacy C code to fashionable Java/Spring framework functions. By breaking down the migration course of into specialised agent roles and implementing strong suggestions loops, organizations can accomplish the next:

• Cut back migration time and value – Automation handles repetitive conversion duties whereas human engineers give attention to high-value work.
• Enhance code high quality – Specialised validation brokers make certain the migrated code follows fashionable finest practices.
• Reduce threat – The systematic method prevents vital enterprise logic loss throughout migration.
• Allow cloud integration – The ensuing Java/Spring code can seamlessly combine with AWS companies.

Challenges

Code migration from legacy techniques to fashionable frameworks presents a number of vital challenges that require a balanced method combining AI capabilities with human experience:

• Language paradigm variations – Changing C code to Java includes navigating basic variations in reminiscence administration, error dealing with, and programming paradigms. C’s procedural nature and direct reminiscence manipulation distinction sharply with Java’s object-oriented method and automated reminiscence administration. Though AI can deal with many syntactic transformations routinely, builders should overview and validate the semantic correctness of those conversions.
• Architectural complexity – Legacy techniques usually characteristic advanced interdependencies between elements that require human evaluation and planning. In our case, the C code base contained intricate relationships between modules, with some TPs (Transaction Applications) related to as many as 12 different modules. Human builders should create dependency mappings and decide migration order, usually ranging from leaf nodes with minimal dependencies. AI can help in figuring out these relationships, however the strategic choices about migration sequencing require human judgment.
• Sustaining enterprise logic – Ensuring vital enterprise logic is precisely preserved throughout translation requires steady human oversight. Our evaluation confirmed that though automated migration is very profitable for easy, well-structured code, advanced enterprise logic embedded in bigger information (over 700 traces) requires cautious human overview and sometimes handbook refinement to stop errors or omissions.
• Inconsistent naming and constructions – Legacy code usually accommodates inconsistent naming conventions and constructions that should be standardized throughout migration. AI can deal with many routine transformations—changing alphanumeric IDs in perform names, reworking C-style error codes to Java exceptions, and changing C structs into Java lessons—however human builders should set up naming requirements and overview edge circumstances the place automated conversion could also be ambiguous.
• Integration complexity – After changing particular person information, human-guided integration is crucial for making a cohesive utility. Variable names that have been constant throughout the unique C information usually change into inconsistent throughout particular person file conversion, requiring builders to carry out reconciliation work and facilitate correct inter-module communication.
• High quality assurance – Validating that transformed code maintains useful equivalence with the unique requires a mixture of automated testing and human verification. That is significantly vital for advanced enterprise logic, the place delicate variations can result in vital points. Builders should design complete check suites and carry out thorough code opinions to make sure migration accuracy.

These challenges necessitate a scientific method that mixes the sample recognition capabilities of enormous language fashions (LLMs) with structured workflows and important human oversight to provide profitable migration outcomes. The secret’s utilizing AI to deal with routine transformations whereas conserving people within the loop for strategic choices, advanced logic validation, and high quality assurance.

Answer overview

The answer employs the Amazon Bedrock Converse API with Amazon Nova Premier to transform legacy C code to fashionable Java/Spring framework code via a scientific agentic workflow. This method breaks down the advanced migration course of into manageable steps, permitting for iterative refinement and dealing with of token limitations. The answer structure consists of a number of key elements:

• Code evaluation agent – Analyzes C code construction and dependencies
• Conversion agent – Transforms C code to Java/Spring code
• Safety evaluation agent – Identifies vulnerabilities in legacy and migrated code
• Validation agent – Verifies conversion completeness and accuracy
• Refine agent – Rewrites the code primarily based on the suggestions from the validation agent
• Integration agent – Combines individually transformed information

Our agentic workflow is applied utilizing a Strands Brokers framework mixed with the Amazon Bedrock Converse API for strong agent orchestration and LLM inference. The structure (as proven within the following diagram) makes use of a hybrid method that mixes Strands’s session administration capabilities with customized BedrockInference dealing with for token continuation.

The answer makes use of the next core applied sciences:

• Strands Brokers framework (v1.1.0+) – Offers agent lifecycle administration, session dealing with, and structured agent communication
• Amazon Bedrock Converse API – Powers the LLM inference with Amazon Nova Premier mannequin
• Customized BedrockInference class – Handles token limitations via textual content prefilling and response continuation
• Asyncio-based orchestration – Permits concurrent processing and non-blocking agent execution

The workflow consists of the next steps:

1. Code evaluation:

• Code evaluation agent – Performs enter code evaluation to know the conversion necessities. Examines C code base construction, identifies dependencies, and assesses complexity.
• Framework integration – Makes use of Strands for session administration whereas utilizing BedrockInference for evaluation.
• Output – JSON-structured evaluation with dependency mapping and conversion suggestions.

2. File categorization and metadata creation:

• Implementation – FileMetadata information class with complexity evaluation.
• Classes – Easy (0–300 traces), Medium (300–700 traces), Advanced (over 700 traces).
• File sorts – Normal C information, header information, and database I/O (DBIO) information.

3. Particular person file conversion:

• Conversion agent – Performs code migration on particular person information primarily based on the knowledge from the code evaluation agent.
• Token dealing with – Makes use of the stitch_output() methodology for dealing with giant information that exceed token limits.

4. Safety evaluation part:

• Safety evaluation agent – Performs complete vulnerability evaluation on each legacy C code and transformed Java code.
• Threat categorization – Classifies safety points by severity (Vital, Excessive, Medium, Low).
• Mitigation suggestions – Offers particular code fixes and safety finest practices.
• Output – Detailed safety report with actionable remediation steps.

5. Validation and suggestions loop:

• Validation agent – Analyzes conversion completeness and accuracy.
• Refine agent – Applies iterative enhancements primarily based on validation outcomes.
• Iteration management – Most 5 suggestions iterations with early termination on passable outcomes.
• Session persistence – Strands framework maintains dialog context throughout iterations.

6. Integration and finalization:

• Integration agent – Makes an attempt to mix individually transformed information.
• Consistency decision – Standardizes variable naming and gives correct dependencies.
• Output era – Creates cohesive Java/Spring utility construction.

7. DBIO conversion (specialised)

• Function – Converts SQL DBIO C supply code to MyBatis XML mapper information.
• Framework – Makes use of the identical Strands and BedrockInference hybrid method for consistency.

The answer consists of the next key orchestration options:

• Session persistence – Every conversion maintains session state throughout agent interactions.
• Error restoration – Complete error dealing with with sleek degradation.
• Efficiency monitoring – Constructed-in metrics for processing time, iteration counts, and success charges.
• Token continuation – Seamless dealing with of enormous information via response stitching.

This framework-specific implementation facilitates dependable, scalable code conversion whereas sustaining the pliability to deal with various C code base constructions and complexities.

Stipulations

Earlier than implementing this code conversion answer, ensure you have the next elements configured:

• AWS surroundings:
  • AWS account with acceptable permissions for Amazon Bedrock with Amazon Nova Premier mannequin entry
  • Amazon Elastic Compute Cloud (Amazon EC2) occasion (t3.medium or bigger) for growth and testing or growth surroundings in native machine
• Improvement setup:
  • Python 3.10+ put in with Boto3 SDK and Strands Brokers
  • AWS Command Line Interface (AWS CLI) configured with acceptable credentials and AWS Area
  • Git for model management of legacy code base and transformed code
  • Textual content editor or built-in growth surroundings (IDE) able to dealing with each C and Java code bases
• Supply and goal code base necessities:
  • C supply code organized in a structured listing format
  • Java 11+ and Maven/Gradle construct instruments
  • Spring Framework 5.x or Spring Boot 2.x+ dependencies

The supply code and prompts used within the put up might be discovered within the GitHub repo.

Agent-based conversion course of

The answer makes use of a classy multi-agent system applied utilizing the Strands framework, the place every agent focuses on a particular side of the code conversion course of. This distributed method gives thorough evaluation, correct conversion, and complete validation whereas sustaining the pliability to deal with various code constructions and complexities.

Strands framework integration

Every agent extends the BaseStrandsConversionAgent class, which gives a hybrid structure combining Strands session administration with customized BedrockInference capabilities:

class BaseStrandsConversionAgent(ABC):
    def (self, title: str, bedrock_inference, system_prompt: str):
        self.title = title
        self.bedrock = bedrock_inference  # Customized BedrockInference for token dealing with
        self.system_prompt = system_prompt
        
        # Create strands agent for session administration
        self.strands_agent = Agent(title=title, system_prompt=system_prompt)
    
    async def execute_async(self, context: ConversionContext) -> Dict[str, Any]:
        # Applied by every specialised agent
        cross

Code evaluation agent

The code evaluation agent examines the construction of the C code base, figuring out dependencies between information and figuring out the optimum conversion technique. This agent helps prioritize which information to transform first and identifies potential challenges. The next is the immediate template for the code evaluation agent:

You're a Code Evaluation Agent with experience in legacy C codebases and fashionable Java/Spring structure.

<c_codebase>
{c_code}
</c_codebase>

## TASK
Your job is to research the supplied C code to arrange for migration.

Carry out a complete evaluation and supply the next:
## INSTRUCTIONS

1. DEPENDENCY ANALYSIS:
   - Determine all file dependencies (which information embody or reference others)
   - Map perform calls between information
   - Detect shared information constructions and world variables

2. COMPLEXITY ASSESSMENT:
   - Categorize every file as Easy (0-300 traces), Medium (300-700 traces), or Advanced (700+ traces)
   - Determine information with advanced management stream, pointer manipulation, or reminiscence administration
   - Flag any platform-specific or hardware-dependent code

3. CONVERSION PLANNING:
   - Advocate a conversion sequence (which information to transform first)
   - Counsel logical splitting factors for big information
   - Determine widespread patterns that may be standardized throughout conversion

4. RISK ASSESSMENT:
   - Spotlight potential conversion challenges (e.g., pointer arithmetic, bitwise operations)
   - Determine business-critical sections requiring particular consideration
   - Observe any undocumented assumptions or behaviors

5. ARCHITECTURE RECOMMENDATIONS:
   - Counsel acceptable Java/Spring elements for every C module
   - Advocate DTO construction and repair group
   - Suggest database entry technique utilizing a persistence framework

Format your response as a structured JSON doc with these sections.

Conversion agent

The conversion agent handles the precise transformation of C code to Java/Spring code. This agent is assigned the function of a senior software program developer with experience in each C and Java/Spring frameworks. The immediate template for the conversion agent is as follows:

You're a Senior Software program Developer with 15+ years of expertise in each C and Java Spring framework. 

<c_file>
{c_code}
</c_file>

## TASK
Your job is to transform legacy C code to fashionable Java Spring code with precision and completeness.

## CONVERSION GUIDELINES:

1. CODE STRUCTURE:
   - Create acceptable Java lessons (Service, DTO, Mapper interfaces)
   - Protect authentic perform and variable names until they battle with Java conventions
   - Use Spring annotations appropriately (@Service, @Repository, and many others.)
   - Implement correct package deal construction primarily based on performance

2. JAVA BEST PRACTICES:
   - Use Lombok annotations (@Knowledge, @Slf4j, @RequiredArgsConstructor) to scale back boilerplate
   - Implement correct exception dealing with as an alternative of error codes
   - Change pointer operations with acceptable Java constructs
   - Convert C-style arrays to Java collections the place acceptable

3. SPRING FRAMEWORK INTEGRATION:
   - Use dependency injection as an alternative of worldwide variables
   - Implement a persistence framework mappers for database operations
   - Change direct SQL calls with mapper interfaces
   - Use Spring's transaction administration

4. SPECIFIC TRANSFORMATIONS:
   - Change PFM_TRY/PFM_CATCH with Java try-catch blocks
   - Convert mpfmdbio calls to a persistence framework mapper methodology calls
   - Change mpfm_dlcall with acceptable Service bean injections
   - Convert NGMHEADER references to enter.getHeaderVo() calls
   - Change PRINT_ and PFM_DBG macros with SLF4J logging
   - Convert ngmf_ strategies to CommonAPI.ngmf methodology calls

5. DATA HANDLING:
   - Create separate DTO lessons for enter and output constructions
   - Use correct Java information sorts (String as an alternative of char arrays, and many others.)
   - Implement correct null dealing with and validation
   - Take away handbook reminiscence administration code

## OUTPUT FORMAT:
   - Embrace filename on the high of every Java file: #filename: [filename].java
   - Place executable Java code inside <java></java> tags
   - Manage a number of output information clearly with correct headers

Generate full, production-ready Java code that absolutely implements all performance from the unique C code.

Safety evaluation agent

The safety evaluation agent performs complete vulnerability evaluation on the unique C code and the transformed Java code, figuring out potential safety dangers and offering particular mitigation methods. This agent is essential for ensuring safety vulnerabilities should not carried ahead throughout migration and new code follows safety finest practices. The next is the immediate template for the safety evaluation agent:

You're a Safety Evaluation Agent with experience in figuring out vulnerabilities in each C and Java codebases, specializing in safe code migration practices.

ORIGINAL C CODE:
<c_code>
{c_code}
</c_code>

CONVERTED JAVA CODE:
<java_code>
{java_code}
</java_code>

## TASK
Your job is to carry out complete safety evaluation on each the legacy C code and transformed Java code, figuring out vulnerabilities and offering particular mitigation suggestions.

## SECURITY ANALYSIS FRAMEWORK

1. **LEGACY C CODE VULNERABILITIES:**
   - Buffer overflow dangers (strcpy, strcat, sprintf utilization)
   - Reminiscence administration points (dangling pointers, reminiscence leaks)
   - Integer overflow/underflow vulnerabilities
   - Format string vulnerabilities
   - Race circumstances in multi-threaded code
   - Improper enter validation and sanitization
   - SQL injection dangers in database operations
   - Insecure cryptographic implementations

2. **JAVA CODE SECURITY ASSESSMENT:**
   - Enter validation and sanitization gaps
   - SQL injection vulnerabilities in persistence framework queries
   - Improper exception dealing with that leaks delicate data
   - Authentication and authorization bypass dangers
   - Insecure deserialization vulnerabilities
   - Cross-site scripting (XSS) prevention in internet endpoints
   - Logging of delicate information
   - Dependency vulnerabilities in Spring framework utilization

3. **MIGRATION-SPECIFIC RISKS:**
   - Safety assumptions that do not translate between languages
   - Privilege escalation via improper Spring Safety configuration
   - Knowledge publicity via overly permissive REST endpoints
   - Session administration vulnerabilities
   - Configuration safety (hardcoded credentials, insecure defaults)

4. **COMPLIANCE AND BEST PRACTICES:**
   - OWASP Prime 10 compliance evaluation
   - Spring Safety finest practices implementation
   - Safe coding requirements adherence
   - Knowledge safety and privateness issues

## OUTPUT FORMAT
Present your evaluation as a structured JSON with these fields:
- "critical_vulnerabilities": array of vital safety points requiring rapid consideration
- "security_risk_issues": array of safety considerations
- "secure_code_recommendations": particular code modifications to implement safety fixes
- "spring_security_configurations": really useful Spring Safety configurations
- "compliance_gaps": areas the place code would not meet safety requirements
- "migration_security_notes": safety issues particular to the C-to-Java migration

For every vulnerability, embody:
- Description of the safety threat
- Potential impression and assault vectors
- Particular line numbers or code sections affected
- Detailed remediation steps with code examples
- Precedence degree and really useful timeline for fixes

Be thorough in figuring out each apparent and delicate safety points that could possibly be exploited in manufacturing environments.

Validation agent

The validation agent opinions the transformed code to determine lacking or incorrectly transformed elements. This agent gives detailed suggestions that’s utilized in subsequent conversion iterations. The immediate template for the validation agent is as follows:

You're a Code Validation Agent specializing in verifying C to Java/Spring migrations.

ORIGINAL C CODE:
<c_code>
{c_code}
</c_code>

CONVERTED JAVA CODE:
<java_code>
{java_code}
</java_code>

## TASK
Your job is to totally analyze the conversion high quality and determine any points or omissions.
Carry out a complete validation specializing in these elements:

## INSTRUCTIONS
1. COMPLETENESS CHECK:
   - Confirm all features from C code are applied in Java
   - Verify all variables and information constructions are correctly transformed
   - Verify that every one logical branches and circumstances are preserved
   - Guarantee all error dealing with paths are applied

2. CORRECTNESS ASSESSMENT:
   - Determine any logical errors within the conversion
   - Confirm correct transformation of C-specific constructs (pointers, structs, and many others.)
   - Verify for proper implementation of reminiscence administration patterns
   - Validate correct dealing with of string operations and byte manipulation

3. SPRING FRAMEWORK COMPLIANCE:
   - Confirm acceptable use of Spring annotations and patterns
   - Verify correct implementation of dependency injection
   - Validate appropriate use of persistence framework mappers
   - Guarantee correct service construction and group

4. CODE QUALITY EVALUATION:
   - Assess Java code high quality and adherence to finest practices
   - Verify for correct exception dealing with
   - Confirm acceptable logging implementation
   - Consider general code group and readability

## OUTPUT FORMAT
Present your evaluation as a structured JSON with these fields:
- "full": boolean indicating if conversion is full
- "missing_elements": array of particular features, variables, or logic blocks which might be lacking
- "incorrect_transformations": array of parts that have been incorrectly reworked
- "spring_framework_issues": array of Spring-specific implementation points
- "quality_concerns": array of code high quality points
- "suggestions": particular, actionable suggestions for enchancment

Be thorough and exact in your evaluation, as your suggestions will instantly inform the following iteration of the conversion course of.

Suggestions loop implementation with refine agent

The suggestions loop is a vital part that allows iterative refinement of the transformed code. This course of includes the next steps:

1. Preliminary conversion by the conversion agent.
2. Safety evaluation by the safety evaluation agent.
3. Validation by the validation agent.
4. Suggestions incorporation by the refine agent (incorporating each validation and safety suggestions).
5. Repeat till passable outcomes are achieved.

The refine agent incorporates safety vulnerability fixes alongside useful enhancements, and safety evaluation outcomes are supplied to growth groups for last overview and approval earlier than manufacturing deployment. The next code is the immediate template for code refinement:

You're a Senior Software program Developer specializing in C to Java/Spring migration with experience in safe coding practices.
ORIGINAL C CODE:
<c_code>
{c_code}
</c_code>

YOUR PREVIOUS JAVA CONVERSION:
<previous_java>
{previous_java_code}
</previous_java>

VALIDATION FEEDBACK:
<validation_feedback>
{validation_feedback}
</validation_feedback>

SECURITY ASSESSMENT:
<security_feedback>
{security_feedback}
</security_feedback>

## TASK
You've got beforehand transformed C code to Java, however validation and safety evaluation have recognized points that have to be addressed. Your job is to enhance the conversion by addressing all recognized useful and safety points whereas sustaining full performance.
## INSTRUCTIONS
1. ADDRESSING MISSING ELEMENTS:
   - Implement any features, variables, or logic blocks recognized as lacking
   - Guarantee all management stream paths from the unique code are preserved
   - Add any lacking error dealing with or edge circumstances
2. CORRECTING TRANSFORMATIONS:
   - Repair any incorrectly reworked code constructs
   - Appropriate any logical errors within the conversion
   - Correctly implement C-specific patterns in Java
3. IMPLEMENTING SECURITY FIXES:
   - Deal with all vital and high-risk safety vulnerabilities recognized
   - Implement safe coding practices (enter validation, parameterized queries, and many others.)
   - Change insecure patterns with safe Java/Spring options
   - Add correct exception dealing with that doesn't leak delicate data
4. IMPROVING SPRING IMPLEMENTATION:
   - Appropriate any points with Spring annotations or patterns
   - Guarantee correct dependency injection and repair construction
   - Repair persistence framework mapper implementations if wanted
   - Implement Spring Safety configurations as really useful
5. MAINTAINING CONSISTENCY:
   - Guarantee naming conventions are constant all through the code
   - Keep constant patterns for related operations
   - Protect the construction of the unique code the place acceptable
## OUTPUT FORMAT
Output the improved Java code inside <java></java> tags, with acceptable file headers. Guarantee all safety vulnerabilities are addressed whereas sustaining full performance from the unique C code.

Integration agent

The combination agent combines individually transformed Java information right into a cohesive utility, resolving inconsistencies in variable naming and offering correct dependencies. The immediate template for the mixing agent is as follows:

You're an Integration Agent specializing in combining individually transformed Java information right into a cohesive Spring utility. 

CONVERTED JAVA FILES:
<converted_files>
{converted_java_files}
</converted_files>

ORIGINAL FILE RELATIONSHIPS:
<relationships>
{file_relationships}
</relationships>

## TASK
Your job is to combine a number of Java information that have been transformed from C, making certain they work collectively correctly.

Carry out the next integration duties:
## INSTRUCTIONS
1. DEPENDENCY RESOLUTION:
   - Determine and resolve dependencies between companies and elements
   - Guarantee correct autowiring and dependency injection
   - Confirm that service methodology signatures match their utilization throughout information

2. NAMING CONSISTENCY:
   - Standardize variable and methodology names that must be constant throughout information
   - Resolve any naming conflicts or inconsistencies
   - Guarantee DTO subject names match throughout associated lessons

3. PACKAGE ORGANIZATION:
   - Manage lessons into acceptable package deal construction
   - Group associated performance collectively
   - Guarantee correct import statements throughout all information

4. SERVICE COMPOSITION:
   - Implement correct service composition patterns
   - Guarantee companies work together appropriately with one another
   - Confirm that information flows appropriately between elements

5. COMMON COMPONENTS:
   - Extract and standardize widespread utility features
   - Guarantee constant error dealing with throughout companies
   - Standardize logging patterns

6. CONFIGURATION:
   - Create obligatory Spring configuration lessons
   - Arrange acceptable bean definitions
   - Configure any required properties or settings

Output the built-in Java code as a set of correctly organized information, every with:
- Applicable package deal declarations
- Appropriate import statements
- Correct Spring annotations
- Clear file headers (#filename: [filename].java)

Place every file's code inside <java></java> tags. Make sure the built-in utility maintains all performance from the person elements whereas offering a cohesive construction.

DBIO conversion agent

This specialised agent handles the conversion of SQL DBIO C supply code to XML information appropriate with persistence framework within the Java Spring framework. The next is the immediate template for the DBIO conversion agent:

You're a Database Integration Specialist with experience in changing C-based SQL DBIO code to persistence framework XML mappings for Spring functions. 

SQL DBIO C SOURCE CODE:
<sql_dbio>
{sql_dbio_code}
</sql_dbio>

## TASK
Your job is to remodel the supplied SQL DBIO C code into correctly structured persistence framework XML information.

Carry out the conversion following these pointers:

## INSTRUCTIONS
1. XML STRUCTURE:
   - Create a correctly formatted persistence framework mapper XML file
   - Embrace acceptable namespace matching the Java mapper interface
   - Set appropriate resultType or resultMap attributes for queries
   - Use correct persistence framework XML construction and syntax

2. SQL TRANSFORMATION:
   - Protect the precise SQL logic from the unique code
   - Convert any C-specific SQL parameter dealing with to persistence framework parameter markers
   - Keep all WHERE clauses, JOIN circumstances, and different SQL logic
   - Protect any feedback explaining SQL performance

3. PARAMETER HANDLING:
   - Convert C variable bindings to persistence framework parameter references (#{param})
   - Deal with advanced parameters utilizing acceptable persistence framework strategies
   - Guarantee parameter sorts match Java equivalents (String as an alternative of char[], and many others.)

4. RESULT MAPPING:
   - Create acceptable resultMap parts for advanced outcome constructions
   - Map column names to Java DTO property names
   - Deal with any sort conversions wanted between database and Java sorts

5. DYNAMIC SQL:
   - Convert any conditional SQL era to persistence framework dynamic SQL parts
   - Use <if>, <select>, <the place>, and different dynamic parts as acceptable
   - Keep the identical conditional logic as the unique code

6. ORGANIZATION:
   - Group associated queries collectively
   - Embrace clear feedback explaining the aim of every question
   - Comply with persistence framework finest practices for mapper group

## OUTPUT FORMAT
Output the transformed persistence framework XML inside <xml></xml> tags. Embrace a filename remark on the high: #filename: [EntityName]Mapper.xml

Make sure the XML is well-formed, correctly indented, and follows persistence framework conventions for Spring functions.

Dealing with token limitations

To handle token limitations within the Amazon Bedrock Converse API, we applied a textual content prefilling approach that permits the mannequin to proceed producing code the place it left off. This method is especially essential for big information that exceed the mannequin’s context window and represents a key technical innovation in our Strands-based implementation.

Technical implementation

The next code implements the BedrockInference class with continuation help:

class BedrockInference:
    def __init__(self, region_name: str = "us-east-1", model_id: str = "us.amazon.nova-premier-v1:0"):
        self.config = Config(read_timeout=300)
        self.consumer = boto3.consumer("bedrock-runtime", config=self.config, region_name=region_name)
        self.model_id = model_id
        self.continue_prompt = {
            "function": "person",
            "content material": [{"text": "Continue the code conversion from where you left off."}]
        }
    
    def run_converse_inference_with_continuation(self, immediate: str, system_prompt: str) -> Listing[str]:
        """Run inference with continuation dealing with for big outputs"""
        ans_list = []
        messages = [{"role": "user", "content": [{"text": prompt}]}]
        
        response, cease = self.generate_conversation([{'text': system_prompt}], messages)
        ans = response['output']['message']['content'][0]['text']
        ans_list.append(ans)
        
        whereas cease == "max_tokens":
            logger.data("Response truncated, persevering with era...")
            messages.append(response['output']['message'])
            messages.append(self.continue_prompt)
            
            # Extract previous few traces for continuation context
            sec_last_line="n".be a part of(ans.rsplit('n', 3)[1:-1]).strip()
            messages.append({"function": "assistant", "content material": [{"text": sec_last_line}]})
            
            response, cease = self.generate_conversation([{'text': system_prompt}], messages)
            ans = response['output']['message']['content'][0]['text']
            del messages[-1]  # Take away the prefill message
            ans_list.append(ans)
        
        return ans_list

Continuation technique particulars

The continuation technique consists of the next steps:

1. Response monitoring:
   1. The system displays the stopReason subject in Amazon Bedrock responses.
   2. When stopReason equals max_tokens, continuation is triggered routinely. This makes positive no code era is misplaced resulting from token limitations.
2. Context preservation:
   1. The system extracts the previous few traces of generated code as continuation context.
   2. It makes use of textual content prefilling to keep up code construction and formatting. It preserves variable names, perform signatures, and code patterns throughout continuations.
3. Response stitching:

def stitch_output(self, immediate: str, system_prompt: str, tag: str = "java") -> str:
    """Sew collectively a number of responses and extract content material inside specified tags"""
    ans_list = self.run_converse_inference_with_continuation(immediate, system_prompt)
    
    if len(ans_list) == 1:
        final_ans = ans_list[0]
    else:
        final_ans = ans_list[0]
        for i in vary(1, len(ans_list)):
            # Seamlessly mix responses by eradicating overlap
            final_ans = final_ans.rsplit('n', 1)[0] + ans_list[i]
    
    # Extract content material inside specified tags (java, xml, and many others.)
    if f'<{tag}>' in final_ans and f'</{tag}>' in final_ans:
        final_ans = final_ans.break up(f'<{tag}>')[-1].break up(f'</{tag}>')[0].strip()
    
    return final_ans

Optimizing conversion high quality

By way of our experiments, we recognized a number of elements that considerably impression conversion high quality:

• File dimension administration – Recordsdata with greater than 300 traces of code profit from being damaged into smaller logical items earlier than conversion.
• Targeted conversion – Changing totally different file sorts (C, header, DBIO) individually yields higher outcomes as every file sort has distinct conversion patterns. Throughout conversion, C features are reworked into Java strategies inside lessons, and C structs change into Java lessons. Nevertheless, as a result of information are transformed individually with out cross-file context, attaining optimum object-oriented design may require human intervention to consolidate associated performance, set up correct class hierarchies, and facilitate acceptable encapsulation throughout the transformed code base.
• Iterative refinement – A number of suggestions loops (4–5 iterations) produce extra complete conversions.
• Position task – Assigning the mannequin a particular function (senior software program developer) improves output high quality.
• Detailed directions – Offering particular transformation guidelines for widespread patterns improves consistency.

Assumptions

This migration technique makes the next key assumptions:

• Code high quality – Legacy C code follows cheap coding practices with discernible construction. Obfuscated or poorly structured code may require preprocessing earlier than automated conversion.
• Scope limitations – This method targets enterprise logic conversion moderately than low-level system code. C code with {hardware} interactions or platform-specific options may require handbook intervention.
• Check protection – Complete check circumstances exist for the legacy utility to validate useful equivalence after migration. With out satisfactory assessments, extra validation steps are obligatory.
• Area information – Though the agentic workflow reduces the necessity for experience in each C and Java, entry to material consultants who perceive the enterprise area is required to validate preservation of vital enterprise logic.
• Phased migration – The method assumes an incremental migration technique is appropriate, the place elements might be transformed and validated individually moderately than a full challenge degree migration.

Outcomes and efficiency

To guage the effectiveness of our migration method powered by Amazon Nova Premier, we measured efficiency throughout enterprise-grade code bases representing typical buyer eventualities. Our evaluation centered on two success elements: structural completeness (preservation of all enterprise logic and features) and framework compliance (adherence to Spring Boot finest practices and conventions).

Migration accuracy by code base complexity

The agentic workflow demonstrated various effectiveness primarily based on file complexity, with all outcomes validated by material consultants. The next desk summarizes the outcomes.

*After a number of suggestions cycles

Key insights for enterprise adoption

These outcomes reveal an vital sample: the agentic method excels at dealing with the majority of migration work (small to medium information) whereas nonetheless offering vital worth for advanced information that require human oversight. This creates a hybrid method the place AI handles routine conversions and safety assessments, and builders give attention to integration and architectural choices.

Conclusion

Our answer demonstrates that the Amazon Bedrock Converse API with Amazon Nova Premier, when applied inside an agentic workflow, can successfully convert legacy C code to fashionable Java/Spring framework code. The method handles advanced code constructions, manages token limitations, and produces high-quality conversions with minimal human intervention. The answer breaks down the conversion course of into specialised agent roles, implements strong suggestions loops, and handles token limitations via continuation strategies. This method accelerates the migration course of, improves code high quality, and reduces the potential for errors. Check out the answer on your personal use case, and share your suggestions and questions within the feedback.

Concerning the authors

Aditya Prakash is a Senior Knowledge Scientist on the Amazon Generative AI Innovation Heart. He helps prospects leverage AWS AI/ML companies to resolve enterprise challenges via generative AI options. Specializing in code transformation, RAG techniques, and multimodal functions, Aditya allows organizations to implement sensible AI options throughout various industries.

Jihye Search engine optimization is a Senior Deep Studying Architect who focuses on designing and implementing generative AI options. Her experience spans mannequin optimization, distributed coaching, RAG techniques, AI agent growth, and real-time information pipeline development throughout manufacturing, healthcare, gaming, and e-commerce sectors. As an AI/ML advisor, Jihye has delivered production-ready options for shoppers, together with sensible manufacturing unit management techniques, predictive upkeep platforms, demand forecasting fashions, advice engines, and MLOps frameworks

Yash Shah is a Science Supervisor within the AWS Generative AI Innovation Heart. He and his crew of utilized scientists, architects and engineers work on a variety of machine studying use circumstances from healthcare, sports activities, automotive and manufacturing, serving to prospects notice artwork of the doable with GenAI. Yash is a graduate of Purdue College, specializing in human elements and statistics. Exterior of labor, Yash enjoys pictures, mountain climbing and cooking.