<!-- build.xml - The 500-line monster -->
<target name="compile" depends="init">
    <javac srcdir="${src.dir}" 
           destdir="${build.dir}/classes"
           classpathref="compile.classpath"/>
    <copy todir="${build.dir}/classes">
        <fileset dir="${src.dir}" includes="**/*.xml,**/*.properties"/>
    </copy>
</target>

<target name="war" depends="compile">
    <war destfile="${dist.dir}/${app.name}.war" webxml="${web.dir}/WEB-INF/web.xml">
        <fileset dir="${web.dir}"/>
        <lib dir="${lib.dir}"/>
        <classes dir="${build.dir}/classes"/>
    </war>
</target>

<!-- applicationContext.xml - The beginning of sanity -->
<bean id="userService" class="com.oracle.service.UserServiceImpl">
    <property name="userDao" ref="userDao"/>
    <property name="emailService" ref="emailService"/>
</bean>

<bean id="userDao" class="com.oracle.dao.UserDaoImpl">
    <property name="dataSource" ref="dataSource"/>
</bean>

<!-- SOAP Web Service Configuration -->
<jaxws:endpoint id="userServiceEndpoint" 
                implementor="#userService" 
                address="/userService"/>

@WebService(serviceName = "UserService")
public class UserServiceImpl implements UserService {
    
    @WebMethod
    public UserResponse getUserById(@WebParam(name = "userId") String userId) {
        // The beauty of strongly-typed contracts
        User user = userDao.findById(userId);
        return UserResponseMapper.toResponse(user);
    }
}

<!-- Oracle Service Bus Configuration -->
<servicebus:service name="UserService">
    <servicebus:routing>
        <servicebus:condition>
            <servicebus:expression>$header.operation = 'getUser'</servicebus:expression>
            <servicebus:action>
                <servicebus:route>UserServiceImpl</servicebus:route>
            </servicebus:action>
        </servicebus:condition>
    </servicebus:routing>
</servicebus:service>

@RestController
@RequestMapping("/api/users")
public class UserController {
    
    @Autowired
    private UserService userService;
    
    @GetMapping("/{id}")
    public ResponseEntity<User> getUser(@PathVariable Long id) {
        return userService.findById(id)
                .map(ResponseEntity::ok)
                .orElse(ResponseEntity.notFound().build());
    }
    
    @PostMapping
    public ResponseEntity<User> createUser(@RequestBody @Valid User user) {
        User savedUser = userService.save(user);
        return ResponseEntity.status(HttpStatus.CREATED).body(savedUser);
    }
}

@Entity
@Table(name = "users")
public class User {
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;
    
    @Column(nullable = false)
    private String email;
    
    // Spring Data JPA magic - no DAO implementation needed!
}

@Repository
public interface UserRepository extends JpaRepository<User, Long> {
    Optional<User> findByEmail(String email);
    List<User> findByCreatedDateAfter(LocalDateTime date);
    // Spring Data generates implementations automatically
}

# application.yml - Configuration as code
spring:
  datasource:
    url: ${DATABASE_URL:jdbc:postgresql://localhost/myapp}
    username: ${DATABASE_USERNAME:app}
    password: ${DATABASE_PASSWORD:secret}
  
  jpa:
    hibernate:
      ddl-auto: validate
    properties:
      hibernate:
        dialect: org.hibernate.dialect.PostgreSQLDialect
        format_sql: true

  redis:
    host: ${REDIS_HOST:localhost}
    port: ${REDIS_PORT:6379}

management:
  endpoints:
    web:
      exposure:
        include: health,info,metrics,prometheus
  endpoint:
    health:
      show-details: always

# Multi-stage Docker builds for optimal size
FROM maven:3.8-openjdk-11 AS build
WORKDIR /app
COPY pom.xml .
RUN mvn dependency:go-offline

COPY src ./src
RUN mvn clean package -DskipTests

FROM openjdk:11-jre-slim
WORKDIR /app
COPY --from=build /app/target/user-service.jar app.jar

# Non-root user for security
RUN adduser --disabled-password --gecos '' appuser
USER appuser

EXPOSE 8080
HEALTHCHECK --interval=30s --timeout=3s \
    CMD curl -f http://localhost:8080/actuator/health || exit 1

CMD ["java", "-jar", "app.jar"]

# GitHub Actions - The new deployment standard
name: Deploy User Service
on:
  push:
    branches: [main]
    paths: ['user-service/**']

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-java@v3
        with:
          java-version: '11'
      
      - name: Run tests
        run: mvn test
      
      - name: SonarQube analysis
        run: mvn sonar:sonar
  
  deploy:
    needs: test
    runs-on: ubuntu-latest
    steps:
      - name: Build Docker image
        run: |
          docker build -t user-service:${{ github.sha }} .
          docker push registry.company.com/user-service:${{ github.sha }}
      
      - name: Deploy to Kubernetes
        run: |
          kubectl set image deployment/user-service \
            user-service=registry.company.com/user-service:${{ github.sha }}
          kubectl rollout status deployment/user-service

// Service-to-service communication with resilience
@Component
public class NotificationServiceClient {
    
    private final RestTemplate restTemplate;
    private final CircuitBreaker circuitBreaker;
    
    @Retryable(value = {Exception.class}, maxAttempts = 3)
    public void sendNotification(NotificationRequest request) {
        circuitBreaker.executeSupplier(() -> {
            return restTemplate.postForObject(
                "http://notification-service/api/notifications", 
                request, 
                Void.class
            );
        });
    }
    
    @Recover
    public void fallbackNotification(Exception ex, NotificationRequest request) {
        // Fallback to message queue for later processing
        messageQueue.send(request);
        log.warn("Notification service unavailable, queued for later: {}", request.getId());
    }
}

# kubernetes/user-service/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: user-service
  namespace: production
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  selector:
    matchLabels:
      app: user-service
  template:
    metadata:
      labels:
        app: user-service
        version: v1.2.3
    spec:
      containers:
      - name: user-service
        image: user-service:v1.2.3
        ports:
        - containerPort: 8080
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: db-credentials
              key: url
        resources:
          requests:
            memory: "256Mi"
            cpu: "200m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /actuator/health
            port: 8080
          initialDelaySeconds: 30
        readinessProbe:
          httpGet:
            path: /actuator/health/readiness
            port: 8080
          initialDelaySeconds: 10

---
apiVersion: v1
kind: Service
metadata:
  name: user-service
  namespace: production
spec:
  selector:
    app: user-service
  ports:
  - port: 80
    targetPort: 8080
  type: ClusterIP

---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: user-service-ingress
  annotations:
    kubernetes.io/ingress.class: nginx
    cert-manager.io/cluster-issuer: letsencrypt-prod
spec:
  tls:
  - hosts:
    - api.company.com
    secretName: api-tls
  rules:
  - host: api.company.com
    http:
      paths:
      - path: /api/users
        pathType: Prefix
        backend:
          service:
            name: user-service
            port:
              number: 80

// AWS Lambda function for processing user events
export const handler: APIGatewayProxyHandler = async (event) => {
    try {
        const userEvent = JSON.parse(event.body || '{}');
        
        // Process the event
        await processUserRegistration(userEvent);
        
        // Trigger downstream services
        await Promise.all([
            sendWelcomeEmail(userEvent.email),
            updateAnalytics(userEvent),
            syncWithCRM(userEvent)
        ]);
        
        return {
            statusCode: 200,
            body: JSON.stringify({ message: 'Event processed successfully' })
        };
    } catch (error) {
        console.error('Error processing user event:', error);
        return {
            statusCode: 500,
            body: JSON.stringify({ error: 'Internal server error' })
        };
    }
};

// Infrastructure as Code with AWS CDK
export class UserServiceStack extends Stack {
    constructor(scope: Construct, id: string, props?: StackProps) {
        super(scope, id, props);
        
        // Lambda function
        const userEventProcessor = new Function(this, 'UserEventProcessor', {
            runtime: Runtime.NODEJS_16_X,
            handler: 'index.handler',
            code: Code.fromAsset('lambda'),
            environment: {
                DATABASE_URL: process.env.DATABASE_URL!,
                EMAIL_SERVICE_URL: process.env.EMAIL_SERVICE_URL!
            }
        });
        
        // API Gateway
        const api = new RestApi(this, 'UserServiceAPI', {
            restApiName: 'User Service API'
        });
        
        const userEvents = api.root.addResource('events');
        userEvents.addMethod('POST', new LambdaIntegration(userEventProcessor));
        
        // Auto-scaling based on queue length
        const queue = new Queue(this, 'UserEventQueue');
        userEventProcessor.addEventSource(new SqsEventSource(queue, {
            batchSize: 10
        }));
    }
}

// AI-generated API endpoint based on natural language description
/**
 * AI Prompt: "Create a REST endpoint that handles user profile updates with 
 * validation, audit logging, and automatic cache invalidation"
 */

@RestController
@RequestMapping("/api/users")
@Validated
@Slf4j
public class UserProfileController {
    
    // AI understood the requirements and generated comprehensive solution
    @PutMapping("/{userId}/profile")
    @PreAuthorize("hasRole('USER') and #userId == authentication.name")
    @AuditLog(action = "UPDATE_PROFILE")
    @CacheEvict(value = "userProfiles", key = "#userId")
    public ResponseEntity<UserProfile> updateProfile(
            @PathVariable @Valid @UUID String userId,
            @RequestBody @Valid UpdateProfileRequest request,
            Authentication authentication) {
        
        try {
            // AI-generated validation logic
            validateProfileUpdate(request, userId);
            
            // AI-generated business logic with error handling
            UserProfile updatedProfile = userService.updateProfile(userId, request);
            
            // AI-generated event publishing for downstream services
            eventPublisher.publishEvent(new ProfileUpdatedEvent(userId, updatedProfile));
            
            // AI-generated metrics collection
            meterRegistry.counter("profile.updates", 
                                Tags.of("userId", userId, "status", "success")).increment();
            
            return ResponseEntity.ok(updatedProfile);
            
        } catch (ValidationException e) {
            log.warn("Profile update validation failed for user {}: {}", userId, e.getMessage());
            return ResponseEntity.badRequest()
                    .body(ErrorResponse.of("VALIDATION_ERROR", e.getMessage()));
        } catch (Exception e) {
            log.error("Unexpected error updating profile for user {}", userId, e);
            meterRegistry.counter("profile.updates", 
                                Tags.of("userId", userId, "status", "error")).increment();
            return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR)
                    .body(ErrorResponse.of("INTERNAL_ERROR", "Profile update failed"));
        }
    }
    
    // AI automatically generated supporting methods
    private void validateProfileUpdate(UpdateProfileRequest request, String userId) {
        // Comprehensive validation logic generated by AI
    }
}

# AI-optimized Kubernetes configuration
# AI Agent analyzed traffic patterns and optimized resource allocation
apiVersion: apps/v1
kind: Deployment
metadata:
  name: user-service
  annotations:
    ai.optimizer/last-updated: "2024-12-26T10:30:00Z"
    ai.optimizer/recommendation: "Increased replicas based on predicted traffic surge"
spec:
  # AI-determined optimal replica count based on historical data
  replicas: 8  
  strategy:
    type: RollingUpdate
    rollingUpdate:
      # AI-optimized deployment strategy
      maxSurge: 25%
      maxUnavailable: 10%
  template:
    spec:
      containers:
      - name: user-service
        image: user-service:v2.1.3-ai-optimized
        resources:
          requests:
            # AI-calculated optimal resource allocation
            memory: "384Mi"  # Increased based on memory usage patterns
            cpu: "300m"      # Optimized based on CPU utilization trends
          limits:
            memory: "768Mi"  # AI-determined headroom for traffic spikes
            cpu: "600m"
        env:
        - name: AI_OPTIMIZATION_ENABLED
          value: "true"
        - name: AUTO_SCALING_THRESHOLD
          value: "70"  # AI-determined optimal scaling trigger

---
# AI-managed Horizontal Pod Autoscaler
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: user-service-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: user-service
  # AI-optimized scaling parameters
  minReplicas: 3
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  # AI-predicted traffic patterns enable proactive scaling
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 60  # Faster scale-up based on AI predictions
    scaleDown:
      stabilizationWindowSeconds: 300 # Conservative scale-down to avoid thrashing

# AI Agent that manages application lifecycle
class WebApplicationAgent:
    def __init__(self):
        self.deployment_optimizer = AIDeploymentOptimizer()
        self.performance_analyzer = AIPerformanceAnalyzer()
        self.security_scanner = AISecurityScanner()
        self.cost_optimizer = AICostOptimizer()
    
    async def autonomous_management_cycle(self):
        """AI agent runs this cycle every 5 minutes"""
        
        # 1. Analyze current application performance
        performance_metrics = await self.performance_analyzer.analyze()
        
        # 2. Predict future resource needs
        resource_prediction = await self.predict_resource_needs(performance_metrics)
        
        # 3. Optimize infrastructure if needed
        if resource_prediction.confidence > 0.85:
            optimization_plan = await self.deployment_optimizer.create_plan(resource_prediction)
            await self.apply_optimization(optimization_plan)
        
        # 4. Scan for security vulnerabilities
        security_issues = await self.security_scanner.scan_application()
        if security_issues.critical_count > 0:
            await self.auto_remediate_security_issues(security_issues)
        
        # 5. Optimize costs
        cost_savings = await self.cost_optimizer.identify_savings()
        if cost_savings.monthly_savings > 100:  # $100/month threshold
            await self.apply_cost_optimizations(cost_savings)
        
        # 6. Generate human-readable report
        await self.generate_management_report(performance_metrics, resource_prediction, security_issues, cost_savings)
    
    async def predict_resource_needs(self, metrics):
        """Use AI to predict future resource requirements"""
        return await self.ai_model.predict(
            historical_data=metrics.last_30_days,
            external_factors=[
                'upcoming_marketing_campaigns',
                'seasonal_patterns', 
                'business_growth_trends'
            ],
            prediction_horizon='7_days'
        )

graph TD
    A[Raw Servlets] --> B[Spring Framework]
    B --> C[Spring Boot]
    C --> D[Docker Containers] 
    D --> E[Kubernetes]
    E --> F[Serverless Functions]
    F --> G[AI-Generated Applications]
    
    style A fill:#ff6b6b
    style G fill:#4ecdc4