The Complete Guide to AI-Powered Software Development: Tools, Techniques, and Real-World Implementation

Introduction: The AI Revolution in Software Development

Artificial Intelligence is fundamentally transforming how software is conceived, developed, tested, and deployed. AI-powered software development represents the integration of machine learning algorithms, natural language processing, and intelligent automation throughout the entire software development lifecycle (SDLC). This paradigm shift extends far beyond simple code completion, encompassing intelligent testing, predictive debugging, automated quality assurance, and strategic project management optimization.

The market impact has been substantial and accelerating. According to recent industry analysis, the global AI in software development market is projected to reach $85.9 billion by 2030, growing at a compound annual growth rate of 23.2%. Organizations implementing AI-powered development tools report significant productivity improvements, with GitHub's 2023 research indicating that developers using AI assistants complete tasks 55% faster than those using traditional methods.

Traditional development workflows follow linear, manual processes: requirements gathering, design, coding, testing, debugging, and deployment. Each phase relies heavily on human expertise and manual validation. In contrast, AI-enhanced approaches introduce intelligent automation at every stage. Code generation becomes context-aware and adaptive, testing evolves into predictive quality assurance, debugging transforms into proactive issue prevention, and deployment becomes self-optimizing based on real-time performance analytics.

The quantifiable benefits from early AI adopters demonstrate compelling business value. Microsoft reported a 55% increase in developer productivity following GitHub Copilot integration across their engineering teams. Netflix achieved a 40% reduction in QA cycle times through AI-powered testing infrastructure. Google's implementation of AI-driven security analysis prevented 75% of critical vulnerabilities before production deployment. These improvements translate directly to faster time-to-market, reduced development costs, and enhanced software quality.

The strategic imperative for AI integration extends beyond competitive advantage. Organizations failing to adopt AI-powered development tools risk falling behind in delivery speed, quality standards, and talent retention. Modern developers increasingly expect intelligent tooling that augments their capabilities rather than burdening them with repetitive tasks. Companies that successfully integrate AI development tools report higher developer satisfaction, improved retention rates, and enhanced ability to attract top-tier engineering talent.

This comprehensive guide provides a practical implementation framework covering intelligent code generation, automated testing, predictive debugging, AI-enhanced project management, intelligent DevOps strategies, security considerations, and organizational change management. Each section includes specific tool recommendations, implementation strategies, performance metrics, and real-world case studies to guide successful AI adoption across development organizations.

Intelligent Code Generation and Completion

The landscape of AI-powered code generation has evolved rapidly, with GitHub Copilot, Amazon CodeWhisperer, and Tabnine emerging as leading solutions, each optimized for different organizational needs and use cases. GitHub Copilot excels in general-purpose development with broad language support and contextual awareness, making it ideal for diverse development environments. Amazon CodeWhisperer integrates seamlessly with AWS services and provides enhanced security scanning for cloud-native applications. Tabnine offers on-premises deployment options and specialized models for enterprise environments requiring data sovereignty.

Implementing effective code generation workflows requires establishing quality and security standards that AI tools must meet. This begins with configuring AI assistants to understand project-specific coding conventions, architectural patterns, and security requirements. Organizations should establish code templates and style guides that AI tools can reference, ensuring generated code aligns with established best practices.

// AI-powered code review automation using GitHub API and OpenAI integration
import { Octokit } from '@octokit/rest';
import OpenAI from 'openai';
import { promises as fs } from 'fs';

interface CodeReviewConfig {
  githubToken: string;
  openaiApiKey: string;
  repository: string;
  owner: string;
  pullRequestNumber: number;
}

interface ReviewComment {
  path: string;
  line: number;
  body: string;
  severity: 'low' | 'medium' | 'high';
}

class AICodeReviewer {
  private octokit: Octokit;
  private openai: OpenAI;

  constructor(private config: CodeReviewConfig) {
    this.octokit = new Octokit({ auth: config.githubToken });
    this.openai = new OpenAI({ apiKey: config.openaiApiKey });
  }

  async reviewPullRequest(): Promise<ReviewComment[]> {
    try {
      const { data: pullRequest } = await this.octokit.pulls.get({
        owner: this.config.owner,
        repo: this.config.repository,
        pull_number: this.config.pullRequestNumber,
      });

      const { data: files } = await this.octokit.pulls.listFiles({
        owner: this.config.owner,
        repo: this.config.repository,
        pull_number: this.config.pullRequestNumber,
      });

      const reviews: ReviewComment[] = [];

      for (const file of files) {
        if (file.status === 'added' || file.status === 'modified') {
          const fileReview = await this.analyzeFile(file.filename, file.patch || '');
          reviews.push(...fileReview);
        }
      }

      await this.submitReviewComments(reviews);
      return reviews;

    } catch (error) {
      console.error('Error during AI code review:', error);
      throw new Error(`Code review failed: ${error instanceof Error ? error.message : 'Unknown error'}`);
    }
  }

  private async analyzeFile(filename: string, patch: string): Promise<ReviewComment[]> {
    const prompt = `
      Analyze the following code changes for potential issues:
      
      File: ${filename}
      Changes:
      ${patch}
      
      Focus on:
      1. Security vulnerabilities
      2. Performance issues
      3. Code quality and maintainability
      4. Best practices violations
      5. Potential bugs
      
      Return findings in JSON format with path, line, body, and severity.
    `;

    try {
      const response = await this.openai.chat.completions.create({
        model: 'gpt-4',
        messages: [{ role: 'user', content: prompt }],
        temperature: 0.1,
        max_tokens: 2000,
      });

      const content = response.choices[0]?.message?.content;
      if (!content) {
        throw new Error('No response from OpenAI');
      }

      return JSON.parse(content) as ReviewComment[];

    } catch (error) {
      console.error(`Error analyzing file ${filename}:`, error);
      return [];
    }
  }

  private async submitReviewComments(comments: ReviewComment[]): Promise<void> {
    const reviewComments = comments.map(comment => ({
      path: comment.path,
      line: comment.line,
      body: `🤖 AI Review - ${comment.severity.toUpperCase()}: ${comment.body}`,
    }));

    try {
      await this.octokit.pulls.createReview({
        owner: this.config.owner,
        repo: this.config.repository,
        pull_number: this.config.pullRequestNumber,
        event: 'COMMENT',
        comments: reviewComments,
      });
    } catch (error) {
      console.error('Error submitting review comments:', error);
      throw error;
    }
  }
}

// Usage example with error handling
async function performAICodeReview() {
  const config: CodeReviewConfig = {
    githubToken: process.env.GITHUB_TOKEN || '',
    openaiApiKey: process.env.OPENAI_API_KEY || '',
    repository: 'my-project',
    owner: 'my-org',
    pullRequestNumber: 123,
  };

  if (!config.githubToken || !config.openaiApiKey) {
    throw new Error('Missing required environment variables: GITHUB_TOKEN, OPENAI_API_KEY');
  }

  const reviewer = new AICodeReviewer(config);
  
  try {
    const reviews = await reviewer.reviewPullRequest();
    console.log(`Generated ${reviews.length} review comments`);
    
    // Log metrics for tracking AI review effectiveness
    const severityCounts = reviews.reduce((acc, review) => {
      acc[review.severity] = (acc[review.severity] || 0) + 1;
      return acc;
    }, {} as Record<string, number>);
    
    console.log('Review severity breakdown:', severityCounts);
    
  } catch (error) {
    console.error('AI code review failed:', error);
    // Fallback to manual review process
    throw error;
  }
}

Measuring code quality metrics before and after AI tool integration provides crucial insights into effectiveness. Organizations should track cyclomatic complexity, maintainability index, code duplication rates, and technical debt accumulation. Establishing baseline measurements enables accurate assessment of AI tool impact on code quality over time.

Best practices for reviewing and validating AI-generated code include implementing mandatory human review processes, establishing code quality gates that AI-generated code must pass, and creating automated testing pipelines that validate AI suggestions before integration. Developers should be trained to critically evaluate AI recommendations rather than accepting them unconditionally.

Intellectual property and licensing considerations require careful attention when using AI code generation tools. Organizations must establish clear policies regarding code ownership, ensure compliance with open-source license requirements, and implement processes for tracking the provenance of AI-generated code. Legal review of AI tool terms of service and data usage policies helps mitigate potential intellectual property risks.

Automated Testing and Quality Assurance

AI-powered testing revolutionizes quality assurance by introducing intelligent test case generation, predictive test prioritization, and self-healing test automation. Tools like Testim and Mabl leverage machine learning to create comprehensive test suites that adapt to application changes automatically, reducing maintenance overhead while improving coverage.

Testim employs AI to identify stable element locators and automatically update tests when UI elements change, significantly reducing test flakiness. Mabl provides intelligent test creation through user journey recording and automatic assertion generation, making test automation accessible to non-technical team members while maintaining robust validation coverage.

Intelligent test prioritization based on code change risk analysis optimizes testing efficiency by focusing execution on areas most likely to contain defects. Machine learning models analyze code changes, historical defect patterns, and system complexity to determine optimal test execution sequences. This approach reduces overall testing time while maximizing defect detection probability.

// Implementing ML-based crash prediction system for Android applications
import android.content.Context
import android.os.Build
import android.util.Log
import kotlinx.coroutines.*
import kotlinx.serialization.Serializable
import kotlinx.serialization.json.Json
import java.io.File
import java.text.SimpleDateFormat
import java.util.*
import kotlin.math.sqrt

@Serializable
data class AppMetrics {
    val memoryUsage: Long,
    val cpuUsage: Double,
    val batteryLevel: Int,
    val networkLatency: Long,
    val activeThreads: Int,
    val gcFrequency: Int,
    val timestamp: Long
}

@Serializable
data class CrashPrediction {
    val riskScore: Double,
    val confidence: Double,
    val predictedTimeToFailure: Long,
    val riskFactors: List<String>
}

class CrashPredictionEngine(private val context: Context) {
    private val scope = CoroutineScope(Dispatchers.IO + SupervisorJob())
    private val metricsHistory = mutableListOf<AppMetrics>()
    private val maxHistorySize = 1000
    private val predictionThreshold = 0.7
    
    companion object {
        private const val TAG = "CrashPrediction"
        private const val METRICS_FILE = "app_metrics.json"
        private const val PREDICTION_INTERVAL = 30000L // 30 seconds
    }

    fun startMonitoring() {
        scope.launch {
            while (isActive) {
                try {
                    val metrics = collectCurrentMetrics()
                    addMetrics(metrics)
                    
                    if (metricsHistory.size >= 10) {
                        val prediction = predictCrashRisk(metrics)
                        handlePrediction(prediction)
                    }
                    
                    delay(PREDICTION_INTERVAL)
                } catch (e: Exception) {
                    Log.e(TAG, "Error in monitoring loop", e)
                    // Continue monitoring despite errors
                    delay(PREDICTION_INTERVAL)
                }
            }
        }
    }

    private suspend fun collectCurrentMetrics(): AppMetrics {
        return withContext(Dispatchers.Main) {
            try {
                val runtime = Runtime.getRuntime()
                val memoryInfo = android.app.ActivityManager.MemoryInfo()
                val activityManager = context.getSystemService(Context.ACTIVITY_SERVICE) as android.app.ActivityManager
                activityManager.getMemoryInfo(memoryInfo)
                
                val batteryManager = context.getSystemService(Context.BATTERY_SERVICE) as android.os.BatteryManager
                val batteryLevel = batteryManager.getIntProperty(android.os.BatteryManager.BATTERY_PROPERTY_CAPACITY)
                
                AppMetrics(
                    memoryUsage = runtime.totalMemory() - runtime.freeMemory(),
                    cpuUsage = getCpuUsage(),
                    batteryLevel = batteryLevel,
                    networkLatency = measureNetworkLatency(),
                    activeThreads = Thread.activeCount(),
                    gcFrequency = getGcFrequency(),
                    timestamp = System.currentTimeMillis()
                )
            } catch (e: Exception) {
                Log.e(TAG, "Error collecting metrics", e)
                throw e
            }
        }
    }

    private fun predictCrashRisk(currentMetrics: AppMetrics): CrashPrediction {
        try {
            val riskFactors = mutableListOf<String>()
            var riskScore = 0.0
            
            // Memory usage risk analysis
            val memoryRisk = analyzeMemoryRisk(currentMetrics.memoryUsage)
            riskScore += memoryRisk * 0.3
            if (memoryRisk > 0.5) riskFactors.add("High memory usage")
            
            // CPU usage trend analysis
            val cpuRisk = analyzeCpuRisk(currentMetrics.cpuUsage)
            riskScore += cpuRisk * 0.25
            if (cpuRisk > 0.5) riskFactors.add("High CPU usage")
            
            // Battery level impact
            val batteryRisk = analyzeBatteryRisk(currentMetrics.batteryLevel)
            riskScore += batteryRisk * 0.15
            if (batteryRisk > 0.5) riskFactors.add("Low battery level")
            
            // Thread count analysis
            val threadRisk = analyzeThreadRisk(currentMetrics.activeThreads)
            riskScore += threadRisk * 0.2
            if (threadRisk > 0.5) riskFactors.add("High thread count")
            
            // GC frequency analysis
            val gcRisk = analyzeGcRisk(currentMetrics.gcFrequency)
            riskScore += gcRisk * 0.1
            if (gcRisk > 0.5) riskFactors.add("Frequent garbage collection")
            
            val confidence = calculateConfidence()
            val timeToFailure = estimateTimeToFailure(riskScore)
            
            return CrashPrediction(riskScore, confidence, timeToFailure, riskFactors)
            
        } catch (e: Exception) {
            Log.e(TAG, "Error in crash prediction", e)
            // Return safe default prediction
            return CrashPrediction(0.0, 0.0, Long.MAX_VALUE, emptyList())
        }
    }

    private fun analyzeMemoryRisk(memoryUsage: Long): Double {
        val runtime = Runtime.getRuntime()
        val maxMemory = runtime.maxMemory()
        val usageRatio = memoryUsage.toDouble() / maxMemory
        
        return when {
            usageRatio > 0.9 -> 1.0
            usageRatio > 0.8 -> 0.8
            usageRatio > 0.7 -> 0.6
            usageRatio > 0.6 -> 0.4
            else -> usageRatio * 0.5
        }
    }

    private fun analyzeCpuRisk(cpuUsage: Double): Double {
        val recentCpuUsage = metricsHistory.takeLast(5).map { it.cpuUsage }
        val avgCpuUsage = recentCpuUsage.average()
        val cpuVariance = calculateVariance(recentCpuUsage)
        
        val baseRisk = when {
            avgCpuUsage > 0.9 -> 1.0
            avgCpuUsage > 0.8 -> 0.8
            avgCpuUsage > 0.7 -> 0.6
            else -> avgCpuUsage * 0.7
        }
        
        // High variance indicates instability
        val varianceRisk = (cpuVariance * 2.0).coerceAtMost(0.3)
        return (baseRisk + varianceRisk).coerceAtMost(1.0)
    }

    private fun analyzeBatteryRisk(batteryLevel: Int): Double {
        return when {
            batteryLevel < 10 -> 0.8
            batteryLevel < 20 -> 0.6
            batteryLevel < 30 -> 0.4
            else -> 0.1
        }
    }

    private fun analyzeThreadRisk(activeThreads: Int): Double {
        val maxRecommendedThreads = Runtime.getRuntime().availableProcessors() * 2
        val threadRatio = activeThreads.toDouble() / maxRecommendedThreads
        
        return when {
            threadRatio > 3.0 -> 1.0
            threadRatio > 2.0 -> 0.8
            threadRatio > 1.5 -> 0.6
            else -> (threadRatio - 1.0).coerceAtLeast(0.0) * 0.5
        }
    }

    private fun analyzeGcRisk(gcFrequency: Int): Double {
        return (gcFrequency / 10.0).coerceAtMost(1.0)
    }

    private fun calculateVariance(values: List<Double>): Double {
        if (values.isEmpty()) return 0.0
        val mean = values.average()
        val squaredDiffs = values.map { (it - mean) * (it - mean) }
        return sqrt(squaredDiffs.average())
    }

    private fun calculateConfidence(): Double {
        val dataPoints = metricsHistory.size
        return when {
            dataPoints > 100 -> 0.95
            dataPoints > 50 -> 0.85
            dataPoints > 20 -> 0.75
            dataPoints > 10 -> 0.65
            else -> 0.5
        }
    }

    private fun estimateTimeToFailure(riskScore: Double): Long {
        return when {
            riskScore > 0.9 -> 60000L // 1 minute
            riskScore > 0.8 -> 300000L // 5 minutes
            riskScore > 0.7 -> 900000L // 15 minutes
            riskScore > 0.5 -> 3600000L // 1 hour
            else -> Long.MAX_VALUE
        }
    }

    private fun handlePrediction(prediction: CrashPrediction) {
        if (prediction.riskScore > predictionThreshold) {
            Log.w(TAG, "High crash risk detected: ${prediction.riskScore}")
            
            // Trigger preventive measures
            scope.launch {
                try {
                    triggerPreventiveMeasures(prediction)
                    reportPrediction(prediction)
                } catch (e: Exception) {
                    Log.e(TAG, "Error handling prediction", e)
                }
            }
        }
    }

    private suspend fun triggerPreventiveMeasures(prediction: CrashPrediction) {
        withContext(Dispatchers.Main) {
            try {
                // Force garbage collection
                System.gc()
                
                // Clear non-essential caches
                clearCaches()
                
                // Reduce background processing
                reduceBackgroundTasks()
                
                Log.i(TAG, "Preventive measures applied for risk score: ${prediction.riskScore}")
            } catch (e: Exception) {
                Log.e(TAG, "Error applying preventive measures", e)
            }
        }
    }

    private fun addMetrics(metrics: AppMetrics) {
        synchronized(metricsHistory) {
            metricsHistory.add(metrics)
            if (metricsHistory.size > maxHistorySize) {
                metricsHistory.removeAt(0)
            }
        }
        
        // Persist metrics for analysis
        scope.launch {
            try {
                saveMetrics(metrics)
            } catch (e: Exception) {
                Log.e(TAG, "Error saving metrics", e)
            }
        }
    }

    private suspend fun saveMetrics(metrics: AppMetrics) {
        withContext(Dispatchers.IO) {
            try {
                val file = File(context.filesDir, METRICS_FILE)
                val json = Json.encodeToString(AppMetrics.serializer(), metrics)
                file.appendText("$json\n")
            } catch (e: Exception) {
                Log.e(TAG, "Error writing metrics to file", e)
                throw e
            }
        }
    }

    private fun getCpuUsage(): Double {
        return try {
            // Simplified CPU usage calculation
            val usage = Math.random() * 0.5 + 0.1 // Placeholder for actual CPU monitoring
            usage.coerceIn(0.0, 1.0)
        } catch (e: Exception) {
            Log.e(TAG, "Error getting CPU usage", e)
            0.0
        }
    }

    private fun measureNetworkLatency(): Long {
        return try {
            // Placeholder for actual network latency measurement
            (Math.random() * 100 + 10).toLong()
        } catch (e: Exception) {
            Log.e(TAG, "Error measuring network latency", e)
            0L
        }
    }

    private fun getGcFrequency(): Int {
        return try {
            // Placeholder for actual GC frequency measurement
            (Math.random() * 10).toInt()
        } catch (e: Exception) {
            Log.e(TAG, "Error getting GC frequency", e)
            0
        }
    }

    private fun clearCaches() {
        // Implementation for clearing non-essential caches
        Log.i(TAG, "Clearing application caches")
    }

    private fun reduceBackgroundTasks() {
        // Implementation for reducing background processing
        Log.i(TAG, "Reducing background task intensity")
    }

    private fun reportPrediction(prediction: CrashPrediction) {
        // Implementation for reporting predictions to analytics/monitoring systems
        Log.i(TAG, "Reporting prediction: $prediction")
    }

    fun stopMonitoring() {
        scope.cancel()
    }
}

Automated visual regression testing with AI-driven image comparison eliminates manual visual validation while improving accuracy. AI models trained on visual differences can detect subtle UI inconsistencies that human reviewers might miss, while ignoring irrelevant variations like timestamp changes or dynamic content updates.

Machine learning for anomaly detection in application performance transforms reactive monitoring into proactive quality assurance. By analyzing performance metrics patterns, AI systems can identify performance degradation trends before they impact users, enabling preventive optimization measures.

Self-healing test suites represent a significant advancement in test automation sustainability. These systems automatically update test scripts when UI elements change, maintain test data consistency, and adapt to API modifications without manual intervention. Implementation requires careful configuration to balance automation with control, ensuring that self-healing mechanisms don't mask legitimate application issues.

Integration of AI quality gates into CI/CD pipelines enables continuous validation throughout the development process. These intelligent checkpoints analyze code changes, predict potential quality impacts, and automatically trigger appropriate testing strategies based on risk assessment. Quality gates can block deployments when AI models detect high-risk changes, ensuring that only validated code reaches production environments.

Predictive Debugging and Performance Optimization

AI-driven log analysis transforms traditional reactive debugging into proactive issue identification and resolution. Modern applications generate vast amounts of log data that often contain early warning signals of impending failures. Machine learning models trained on historical log patterns can identify anomalies, correlate seemingly unrelated events, and predict system failures before they occur.

Advanced log analysis platforms utilize natural language processing to extract meaningful insights from unstructured log messages, while time-series analysis identifies performance degradation trends. Implementing effective log analysis requires establishing consistent logging standards, ensuring adequate log coverage across application components, and configuring machine learning models to recognize organization-specific patterns and failure modes.

// AI-driven performance monitoring and optimization for iOS apps using CoreML
import Foundation
import CoreML
import os.log
import Network
import UIKit

@available(iOS 13.0, *)
class AIPerformanceMonitor {
    
    private let logger = Logger(subsystem: "com.app.performance", category: "AIMonitor")
    private var performanceModel: MLModel?
    private let metricsQueue = DispatchQueue(label: "performance.metrics", qos: .utility)
    private var metricsBuffer: [PerformanceMetrics] = []
    private let maxBufferSize = 100
    private var monitoringTimer: Timer?
    
    struct PerformanceMetrics {
        let timestamp: Date
        let memoryUsage: Double
        let cpuUsage: Double
        let batteryLevel: Double
        let networkLatency: TimeInterval
        let frameRate: Double
        let diskIO: Double
        let thermalState: Int
    }
    
    struct OptimizationRecommendation {
        let priority: Priority
        let action: OptimizationAction
        let expectedImprovement: Double
        let implementation: String
        
        enum Priority: Int, CaseIterable {
            case low = 1, medium = 2, high = 3, critical = 4
        }
        
        enum OptimizationAction: String, CaseIterable {
            case reduceMemoryUsage = "reduce_memory_usage"
            case optimizeNetworkCalls = "optimize_network_calls"
            case improveRenderingPerformance = "improve_rendering_performance"
            case reduceBatteryConsumption = "reduce_battery_consumption"
            case optimizeDiskIO = "optimize_disk_io"
            case manageThermalThrottling = "manage_thermal_throttling"
        }
    }
    
    init() {
        loadPerformanceModel()
        setupMonitoring()
    }
    
    private func loadPerformanceModel() {
        guard let modelURL = Bundle.main.url(forResource: "PerformanceOptimizationModel", withExtension: "mlmodelc") else {
            logger.error("Performance model not found in bundle")
            return
        }
        
        do {
            performanceModel = try MLModel(contentsOf: modelURL)
            logger.info("Performance optimization model loaded successfully")
        } catch {
            logger.error("Failed to load performance model: \(error.localizedDescription)")
        }
    }
    
    private func setupMonitoring() {
        monitoringTimer = Timer.scheduledTimer(withTimeInterval: 5.0, repeats: true) { [weak self] _ in
            self?.collectMetrics()
        }
    }
    
    private func collectMetrics() {
        Task {
            do {
                let metrics = try await gatherCurrentMetrics()
                await processMetrics(metrics)
            } catch {
                logger.error("Error collecting performance metrics: \(error.localizedDescription)")
            }
        }
    }
    
    private func gatherCurrentMetrics() async throws -> PerformanceMetrics {
        return try await withCheckedThrowingContinuation { continuation in
            metricsQueue.async {
                do {
                    let memoryUsage = self.getCurrentMemoryUsage()
                    let cpuUsage = self.getCurrentCPUUsage()
                    let batteryLevel = self.getCurrentBatteryLevel()
                    let networkLatency = self.measureNetworkLatency()
                    let frameRate = self.getCurrentFrameRate()
                    let diskIO = self.getCurrentDiskIOUsage()
                    let thermalState = self.getCurrentThermalState()
                    
                    let metrics = PerformanceMetrics(
                        timestamp: Date(),
                        memoryUsage: memoryUsage,
                        cpuUsage: cpuUsage,
                        batteryLevel: batteryLevel,
                        networkLatency: networkLatency,
                        frameRate: frameRate,
                        diskIO: diskIO,
                        thermalState: thermalState
                    )
                    
                    continuation.resume(returning: metrics)
                } catch {
                    continuation.resume(throwing: error)
                }
            }
        }
    }
    
    private func processMetrics(_ metrics: PerformanceMetrics) async {
        metricsQueue.async {
            self.metricsBuffer.append(metrics)
            
            if self.metricsBuffer.count > self.maxBufferSize {
                self.metricsBuffer.removeFirst()
            }
            
            // Analyze metrics for immediate issues
            self.analyzeCurrentMetrics(metrics)
            
            // Generate AI-powered optimization recommendations
            if self.metricsBuffer.count >= 10 {
                Task {
                    await self.generateOptimizationRecommendations()
                }
            }
        }
    }
    
    private func analyzeCurrentMetrics(_ metrics: PerformanceMetrics) {
        // Immediate performance issue detection
        var issues: [String] = []
        
        if metrics.memoryUsage > 0.8 {
            issues.append("High memory usage detected: \(Int(metrics.memoryUsage * 100))%")
        }
        
        if metrics.cpuUsage > 0.7 {
            issues.append("High CPU usage detected: \(Int(metrics.cpuUsage * 100))%")
        }
        
        if metrics.frameRate < 30 {
            issues.append("Low frame rate detected: \(Int(metrics.frameRate)) FPS")
        }
        
        if metrics.thermalState > 2 {
            issues.append("Device thermal throttling detected")
        }
        
        if !issues.isEmpty {
            logger.warning("Performance issues detected: \(issues.joined(separator: ", "))")
            Task {
                await self.triggerImmediateOptimizations(for: issues)
            }
        }
    }
    
    private func generateOptimizationRecommendations() async {
        guard let model = performanceModel else {
            logger.error("Performance model not available for recommendations")
            return
        }
        
        do {
            let recentMetrics = Array(metricsBuffer.suffix(30))
            let features = extractFeaturesFromMetrics(recentMetrics)
            
            let input = try MLDictionaryFeatureProvider(dictionary: features)
            let prediction = try model.prediction(from: input)
            
            let recommendations = parseModelOutput(prediction)
            await implementRecommendations(recommendations)
            
        } catch {
            logger.error("Error generating AI recommendations: \(error.localizedDescription)")
        }
    }
    
    private func extractFeaturesFromMetrics(_ metrics: [PerformanceMetrics]) -> [String: MLFeatureValue] {
        guard !metrics.isEmpty else { return [:] }
        
        let avgMemory = metrics.map { $0.memoryUsage }.reduce(0, +) / Double(metrics.count)
        let avgCPU = metrics.map { $0.cpuUsage }.reduce(0, +) / Double(metrics.count)
        let avgFrameRate = metrics.map { $0.frameRate }.reduce(0, +) / Double(metrics.count)
        let avgNetwork