Developers: Shifting Left in Legacy Applications - AI-Accelerated Modernization

class LegacyCodeAnalyzer:
    def __init__(self):
        self.code_model = CodeAnalysisModel()
        self.documentation_generator = DocumentationGenerator()
        self.dependency_mapper = DependencyMapper()
    
    async def analyze_codebase(self, codebase_path):
        # Scan entire codebase and identify key components
        components = await self.scan_components(codebase_path)
        
        # Generate comprehensive analysis for each component
        analysis_results = {}
        for component in components:
            analysis = await self.code_model.analyze(component.code, {
                'business_logic': True,
                'dependencies': True,
                'security_issues': True,
                'performance_bottlenecks': True,
                'refactoring_opportunities': True
            })
            
            analysis_results[component.name] = {
                'business_purpose': analysis.business_purpose,
                'key_functions': analysis.key_functions,
                'dependencies': analysis.dependencies,
                'risks': analysis.security_risks,
                'optimization_opportunities': analysis.optimizations,
                'migration_complexity': analysis.migration_score
            }
        
        # Generate comprehensive system documentation
        system_docs = await self.documentation_generator.create_docs(
            analysis_results, 
            include_migration_guide=True
        )
        
        return {
            'analysis': analysis_results,
            'documentation': system_docs,
            'migration_roadmap': self.generate_migration_plan(analysis_results)
        }

    def generate_migration_plan(self, analysis):
        # AI-generated migration strategy based on complexity and risk
        return self.code_model.create_migration_strategy(analysis, {
            'risk_tolerance': 'medium',
            'timeline': '6_months',
            'resources': 'small_team'
        })

# Example AI prompt for understanding legacy business rules
BUSINESS_LOGIC_ANALYSIS_PROMPT = """
Analyze this legacy code and extract the core business rules and logic:

```{legacy_code}```

Please provide:
1. Core business rules implemented in this code
2. Input/output data transformations
3. Error handling and edge cases
4. Integration points with other systems
5. Potential issues or bugs in the current implementation
6. Suggestions for modernization while preserving business logic

Format as structured analysis with code examples and explanations.
"""

async def extract_business_logic(self, code_segment):
    analysis = await self.ai_model.complete(
        BUSINESS_LOGIC_ANALYSIS_PROMPT.format(legacy_code=code_segment),
        temperature=0.2  # Lower temperature for more factual analysis
    )
    
    # Validate analysis by generating test cases
    test_cases = await self.generate_test_cases(analysis.business_rules)
    
    return {
        'business_rules': analysis.business_rules,
        'data_flows': analysis.data_transformations,
        'integration_points': analysis.integrations,
        'test_cases': test_cases,
        'modernization_suggestions': analysis.suggestions
    }

class LegacyAPIWrapper {
    constructor() {
        this.schemaGenerator = new SchemaGenerator();
        this.adapterGenerator = new AdapterGenerator();
    }
    
    async createModernAPI(legacySystem) {
        // Analyze legacy system interfaces
        const interfaces = await this.analyzeLegacyInterfaces(legacySystem);
        
        // Generate OpenAPI specification
        const apiSpec = await this.schemaGenerator.generateOpenAPI(interfaces, {
            version: '3.0.0',
            includeValidation: true,
            includeAuth: true,
            includeRateLimit: true
        });
        
        // Generate implementation code
        const implementation = await this.adapterGenerator.generateAdapter(
            apiSpec, 
            interfaces,
            {
                framework: 'express',
                includeErrorHandling: true,
                includeLogging: true,
                includeMetrics: true
            }
        );
        
        return {
            specification: apiSpec,
            implementation: implementation,
            tests: await this.generateIntegrationTests(apiSpec, interfaces),
            documentation: await this.generateAPIDocs(apiSpec)
        };
    }
    
    async generateAdapter(legacyInterface, modernInterface) {
        const adapterCode = await this.ai_model.complete(`
            Create an adapter that translates between these interfaces:
            
            Legacy Interface:
            ${JSON.stringify(legacyInterface, null, 2)}
            
            Modern Interface:
            ${JSON.stringify(modernInterface, null, 2)}
            
            Requirements:
            - Handle all data type conversions
            - Preserve all business logic
            - Include comprehensive error handling
            - Add input validation and sanitization
            - Include performance monitoring
            - Generate TypeScript with proper types
        `);
        
        // Validate generated adapter
        const validation = await this.validateAdapter(adapterCode, legacyInterface, modernInterface);
        if (!validation.isValid) {
            throw new Error(`Adapter validation failed: ${validation.issues.join(', ')}`);
        }
        
        return adapterCode;
    }
}

class DatabaseMigrationGenerator:
    def __init__(self):
        self.schema_analyzer = SchemaAnalyzer()
        self.migration_generator = MigrationGenerator()
    
    async def analyze_legacy_database(self, db_connection):
        # Extract complete schema information
        schema = await self.schema_analyzer.extract_schema(db_connection)
        
        # Analyze data patterns and relationships
        data_analysis = await self.analyze_data_patterns(db_connection, schema)
        
        # Generate modernization recommendations
        recommendations = await self.ai_model.complete(f"""
            Analyze this legacy database schema and provide modernization recommendations:
            
            Schema: {schema}
            Data Patterns: {data_analysis}
            
            Provide:
            1. Schema normalization opportunities
            2. Performance optimization suggestions
            3. Modern database design patterns to implement
            4. Migration strategy with minimal downtime
            5. Data validation and cleanup procedures
            6. Backup and rollback strategies
        """)
        
        return {
            'current_schema': schema,
            'data_analysis': data_analysis,
            'recommendations': recommendations,
            'migration_scripts': await self.generate_migration_scripts(recommendations)
        }
    
    async def generate_migration_scripts(self, recommendations):
        scripts = {}
        
        # Generate schema migration scripts
        scripts['schema_migration'] = await self.migration_generator.create_schema_migration(
            recommendations.schema_changes
        )
        
        # Generate data migration scripts
        scripts['data_migration'] = await self.migration_generator.create_data_migration(
            recommendations.data_transformations
        )
        
        # Generate validation scripts
        scripts['validation'] = await self.migration_generator.create_validation_scripts(
            recommendations.validation_rules
        )
        
        return scripts

class AIRefactoringEngine:
    def __init__(self):
        self.code_analyzer = CodeAnalyzer()
        self.refactor_generator = RefactorGenerator()
        self.test_generator = TestGenerator()
    
    async def analyze_technical_debt(self, codebase):
        # Comprehensive technical debt analysis
        debt_analysis = await self.code_analyzer.analyze_debt(codebase, {
            'code_smells': True,
            'security_vulnerabilities': True,
            'performance_issues': True,
            'maintainability_issues': True,
            'design_pattern_violations': True
        })
        
        # Prioritize issues by impact and effort
        prioritized_issues = self.prioritize_debt(debt_analysis)
        
        return {
            'high_priority': prioritized_issues.high,
            'medium_priority': prioritized_issues.medium,
            'low_priority': prioritized_issues.low,
            'estimated_effort': debt_analysis.effort_estimates,
            'business_impact': debt_analysis.impact_analysis
        }
    
    async def generate_refactoring_plan(self, debt_analysis):
        refactoring_plan = await self.ai_model.complete(f"""
            Create a comprehensive refactoring plan for this technical debt analysis:
            
            {debt_analysis}
            
            Provide:
            1. Step-by-step refactoring sequence
            2. Risk assessment for each change
            3. Testing strategies for validation
            4. Rollback procedures if issues arise
            5. Estimated timeline and effort
            6. Dependencies between refactoring tasks
        """)
        
        # Generate actual refactoring code for each step
        refactoring_implementations = {}
        for step in refactoring_plan.steps:
            implementation = await self.refactor_generator.generate_refactoring(
                step, 
                debt_analysis.affected_code
            )
            refactoring_implementations[step.id] = implementation
        
        return {
            'plan': refactoring_plan,
            'implementations': refactoring_implementations,
            'tests': await self.generate_refactoring_tests(refactoring_plan),
            'validation_procedures': await self.generate_validation_procedures(refactoring_plan)
        }

class SecurityVulnerabilityFixer:
    def __init__(self):
        self.vulnerability_scanner = VulnerabilityScanner()
        self.fix_generator = SecurityFixGenerator()
    
    async def scan_and_fix_vulnerabilities(self, codebase):
        # Scan for security vulnerabilities
        vulnerabilities = await self.vulnerability_scanner.scan(codebase, {
            'sql_injection': True,
            'xss': True,
            'csrf': True,
            'insecure_dependencies': True,
            'authentication_issues': True,
            'authorization_problems': True,
            'data_exposure': True
        })
        
        # Generate fixes for each vulnerability
        fixes = {}
        for vuln in vulnerabilities:
            fix = await self.fix_generator.generate_fix(vuln, {
                'preserve_functionality': True,
                'follow_security_best_practices': True,
                'include_tests': True,
                'add_monitoring': True
            })
            fixes[vuln.id] = fix
        
        # Validate that fixes don't break existing functionality
        validation_results = await self.validate_security_fixes(fixes, codebase)
        
        return {
            'vulnerabilities': vulnerabilities,
            'fixes': fixes,
            'validation': validation_results,
            'implementation_order': self.determine_fix_order(fixes),
            'testing_procedures': await self.generate_security_tests(fixes)
        }

class StranglerFigMigration:
    def __init__(self):
        self.route_analyzer = RouteAnalyzer()
        self.service_generator = ServiceGenerator()
        self.traffic_manager = TrafficManager()
    
    async def plan_migration(self, legacy_system, target_architecture):
        # Analyze legacy system to identify migration boundaries
        migration_boundaries = await self.route_analyzer.identify_boundaries(legacy_system)
        
        # Generate migration plan with phased approach
        migration_plan = await self.ai_model.complete(f"""
            Create a strangler fig migration plan for:
            
            Legacy System: {legacy_system.description}
            Target Architecture: {target_architecture}
            Migration Boundaries: {migration_boundaries}
            
            Provide:
            1. Phased migration sequence (start with least risky components)
            2. Traffic routing strategy for gradual cutover
            3. Data synchronization approach during transition
            4. Rollback procedures for each phase
            5. Monitoring and validation checkpoints
            6. Estimated timeline and resource requirements
        """)
        
        return {
            'phases': migration_plan.phases,
            'routing_strategy': migration_plan.routing,
            'data_sync_plan': migration_plan.data_synchronization,
            'rollback_procedures': migration_plan.rollback,
            'monitoring_plan': migration_plan.monitoring
        }
    
    async def implement_phase(self, phase, legacy_system, new_system):
        # Generate new service implementation
        new_service = await self.service_generator.generate_service(
            phase.requirements,
            legacy_system.interface,
            new_system.architecture
        )
        
        # Create traffic routing configuration
        routing_config = await self.traffic_manager.create_routing(
            phase.traffic_split,
            legacy_system.endpoints,
            new_service.endpoints
        )
        
        # Generate data synchronization mechanisms
        data_sync = await self.create_data_synchronization(
            legacy_system.data_sources,
            new_system.data_sources,
            phase.sync_requirements
        )
        
        return {
            'new_service': new_service,
            'routing_config': routing_config,
            'data_sync': data_sync,
            'validation_tests': await self.generate_phase_tests(phase),
            'monitoring_setup': await self.create_phase_monitoring(phase)
        }

class EventDrivenLegacyIntegration {
    constructor() {
        this.eventBus = new EventBus();
        this.legacyAdapter = new LegacySystemAdapter();
        this.eventGenerator = new EventGenerator();
    }
    
    async setupEventIntegration(legacySystem, modernSystems) {
        // Analyze legacy system events and data changes
        const eventMappings = await this.analyzeEventPatterns(legacySystem);
        
        // Generate event schemas and handlers
        const eventSpecs = await this.eventGenerator.generateEventSpecs(
            eventMappings,
            modernSystems.map(s => s.eventRequirements)
        );
        
        // Create bidirectional event adapters
        const adapters = {};
        for (const system of modernSystems) {
            adapters[system.name] = await this.createEventAdapter(
                legacySystem,
                system,
                eventSpecs
            );
        }
        
        return {
            eventSpecs: eventSpecs,
            adapters: adapters,
            eventBusConfig: await this.configureEventBus(eventSpecs),
            monitoringSetup: await this.setupEventMonitoring(eventSpecs)
        };
    }
    
    async createEventAdapter(legacySystem, modernSystem, eventSpecs) {
        const adapterCode = await this.ai_model.complete(`
            Create an event adapter that:
            1. Monitors ${legacySystem.name} for data changes
            2. Transforms legacy events to modern event format
            3. Publishes events to event bus with proper schema validation
            4. Handles event ordering and deduplication
            5. Implements circuit breaker pattern for resilience
            6. Includes comprehensive error handling and retry logic
            
            Legacy System Interface: ${JSON.stringify(legacySystem.interface)}
            Modern System Requirements: ${JSON.stringify(modernSystem.eventRequirements)}
            Event Specifications: ${JSON.stringify(eventSpecs)}
            
            Generate TypeScript code with proper error handling and monitoring.
        `);
        
        return {
            code: adapterCode,
            config: await this.generateAdapterConfig(legacySystem, modernSystem),
            tests: await this.generateAdapterTests(adapterCode, eventSpecs)
        };
    }
}

class LegacyPerformanceOptimizer:
    def __init__(self):
        self.profiler = PerformanceProfiler()
        self.optimizer = CodeOptimizer()
        self.cache_designer = CacheDesigner()
    
    async def analyze_performance(self, legacy_system):
        # Comprehensive performance analysis
        performance_data = await self.profiler.analyze(legacy_system, {
            'cpu_usage': True,
            'memory_consumption': True,
            'database_queries': True,
            'network_requests': True,
            'file_io': True,
            'algorithm_complexity': True
        })
        
        # AI-generated performance optimization recommendations
        optimization_plan = await self.ai_model.complete(f"""
            Analyze this performance profile and create optimization recommendations:
            
            Performance Data: {performance_data}
            
            Provide:
            1. Top 10 performance bottlenecks ranked by impact
            2. Specific optimization techniques for each bottleneck
            3. Estimated performance improvement for each optimization
            4. Implementation complexity and risk assessment
            5. Caching strategies and opportunities
            6. Database query optimizations
            7. Algorithm improvements and replacements
        """)
        
        return {
            'bottlenecks': optimization_plan.bottlenecks,
            'optimizations': optimization_plan.optimizations,
            'caching_strategy': optimization_plan.caching,
            'database_optimizations': optimization_plan.database,
            'implementation_plan': optimization_plan.implementation
        }
    
    async def implement_optimizations(self, optimization_plan, legacy_code):
        optimized_implementations = {}
        
        for optimization in optimization_plan.optimizations:
            # Generate optimized code
            optimized_code = await self.optimizer.optimize_code(
                legacy_code.get_section(optimization.target),
                optimization.technique,
                {
                    'preserve_functionality': True,
                    'maintain_backwards_compatibility': True,
                    'include_performance_tests': True
                }
            )
            
            # Validate optimization doesn't break functionality
            validation = await self.validate_optimization(
                legacy_code.get_section(optimization.target),
                optimized_code
            )
            
            if validation.maintains_functionality:
                optimized_implementations[optimization.id] = {
                    'code': optimized_code,
                    'performance_gain': validation.performance_improvement,
                    'tests': await self.generate_performance_tests(optimized_code),
                    'rollback_plan': await self.create_rollback_plan(optimization)
                }
        
        return optimized_implementations

class LegacyTestGenerator:
    def __init__(self):
        self.behavior_analyzer = BehaviorAnalyzer()
        self.test_generator = TestGenerator()
        self.data_generator = TestDataGenerator()
    
    async def generate_comprehensive_tests(self, legacy_system):
        # Analyze existing system behavior
        behavior_analysis = await self.behavior_analyzer.analyze_system_behavior(
            legacy_system,
            {
                'user_interactions': True,
                'data_processing': True,
                'error_conditions': True,
                'edge_cases': True,
                'integration_points': True
            }
        )
        
        # Generate test specifications
        test_specs = await self.ai_model.complete(f"""
            Generate comprehensive test specifications for this legacy system:
            
            System Behavior Analysis: {behavior_analysis}
            
            Create test cases for:
            1. All identified user interaction patterns
            2. Data processing and transformation logic
            3. Error handling and edge cases
            4. Integration points with external systems
            5. Performance and load scenarios
            6. Security and access control
            7. Data validation and business rules
            
            Include test data generation strategies and expected outcomes.
        """)
        
        # Generate actual test implementations
        test_implementations = {}
        for test_category in test_specs.categories:
            implementation = await self.test_generator.generate_tests(
                test_category,
                legacy_system.technology_stack,
                {
                    'framework': self.determine_test_framework(legacy_system),
                    'include_mocks': True,
                    'include_integration_tests': True,
                    'generate_test_data': True
                }
            )
            test_implementations[test_category.name] = implementation
        
        return {
            'test_specifications': test_specs,
            'test_implementations': test_implementations,
            'test_data': await self.generate_test_data(test_specs),
            'execution_plan': await self.create_test_execution_plan(test_specs)
        }
    
    async def generate_regression_tests(self, legacy_system, planned_changes):
        # Focus on areas that might be affected by planned changes
        impact_analysis = await self.analyze_change_impact(legacy_system, planned_changes)
        
        regression_tests = await self.test_generator.generate_regression_suite(
            impact_analysis,
            {
                'comprehensive_coverage': True,
                'automated_execution': True,
                'performance_benchmarking': True,
                'data_integrity_checks': True
            }
        )
        
        return {
            'regression_suite': regression_tests,
            'impact_analysis': impact_analysis,
            'execution_automation': await self.create_automated_execution(regression_tests),
            'reporting_dashboard': await self.create_test_reporting(regression_tests)
        }

class MonolithDecomposer:
    def __init__(self):
        self.boundary_analyzer = ServiceBoundaryAnalyzer()
        self.service_generator = MicroserviceGenerator()
    
    async def identify_service_boundaries(self, monolith_codebase):
        # Analyze code dependencies and data flows
        dependencies = await self.boundary_analyzer.analyze_dependencies(monolith_codebase)
        
        # AI-generated service boundary recommendations
        boundaries = await self.ai_model.complete(f"""
            Analyze this monolith and identify optimal microservice boundaries:
            
            Dependency Analysis: {dependencies}
            
            Recommend service boundaries based on:
            1. Single responsibility principle
            2. Data ownership and consistency requirements  
            3. Team ownership and development velocity
            4. Deployment independence
            5. Technology stack compatibility
            6. Performance and latency requirements
            
            For each recommended service, provide:
            - Service purpose and responsibilities
            - API interface design
            - Data model and storage requirements
            - Integration patterns with other services
        """)
        
        return boundaries
    
    async def extract_microservice(self, service_definition, monolith_code):
        # Generate complete microservice implementation
        microservice = await self.service_generator.generate_service(
            service_definition,
            monolith_code,
            {
                'framework': 'fastapi',
                'database': 'postgresql',
                'include_api_docs': True,
                'include_monitoring': True,
                'include_tests': True,
                'include_deployment': True
            }
        )
        
        return microservice