reasonkit-core 0.1.8

//! # Evolution Module
//!
//! Self-improvement and optimization capabilities for the ARF platform.
//! This module enables the system to benchmark, analyze, and optimize its own performance.

use crate::error::Result;
use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;
use wasmtime::{Engine, Module};

/// Performance metrics for self-optimization
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    pub function_name: String,
    pub execution_time_ns: u64,
    pub memory_usage_bytes: usize,
    pub call_count: u64,
    pub last_executed: chrono::DateTime<chrono::Utc>,
}

/// Optimization candidate identified for improvement
#[derive(Debug, Clone)]
pub struct OptimizationCandidate {
    pub function_name: String,
    pub performance_score: f64,
    pub improvement_potential: f64,
    pub optimization_strategy: OptimizationStrategy,
}

/// Available optimization strategies
#[derive(Debug, Clone)]
pub enum OptimizationStrategy {
    AlgorithmicImprovement,
    CachingLayer,
    Parallelization,
    MemoryOptimization,
    HotSwapReplacement,
}

/// Self-improvement engine
pub struct EvolutionEngine {
    metrics_store: Arc<RwLock<HashMap<String, PerformanceMetrics>>>,
    optimization_candidates: Arc<RwLock<Vec<OptimizationCandidate>>>,
    wasm_engine: Engine,
    active_optimizations: Arc<RwLock<HashMap<String, OptimizedFunction>>>,
}

#[derive(Debug)]
struct OptimizedFunction {
    _wasm_module: Module,
    performance_gain: f64,
    _activation_time: chrono::DateTime<chrono::Utc>,
}

impl EvolutionEngine {
    /// Create a new evolution engine
    pub fn new() -> Result<Self> {
        let wasm_engine = Engine::default();

        Ok(Self {
            metrics_store: Arc::new(RwLock::new(HashMap::new())),
            optimization_candidates: Arc::new(RwLock::new(Vec::new())),
            wasm_engine,
            active_optimizations: Arc::new(RwLock::new(HashMap::new())),
        })
    }

    /// Record performance metrics for a function
    pub async fn record_metrics(
        &self,
        function_name: &str,
        execution_time_ns: u64,
        memory_usage: usize,
    ) -> Result<()> {
        let mut store = self.metrics_store.write().await;

        let metrics = store
            .entry(function_name.to_string())
            .or_insert(PerformanceMetrics {
                function_name: function_name.to_string(),
                execution_time_ns: 0,
                memory_usage_bytes: 0,
                call_count: 0,
                last_executed: chrono::Utc::now(),
            });

        // Update running averages
        metrics.call_count += 1;
        metrics.execution_time_ns = (metrics.execution_time_ns + execution_time_ns) / 2;
        metrics.memory_usage_bytes = (metrics.memory_usage_bytes + memory_usage) / 2;
        metrics.last_executed = chrono::Utc::now();

        // Check if this function needs optimization
        self.analyze_for_optimization(function_name, metrics.clone())
            .await?;

        Ok(())
    }

    /// Analyze a function for optimization opportunities
    async fn analyze_for_optimization(
        &self,
        function_name: &str,
        metrics: PerformanceMetrics,
    ) -> Result<()> {
        // Calculate performance score (lower is better)
        let performance_score =
            (metrics.execution_time_ns as f64) * (metrics.memory_usage_bytes as f64);

        // Identify optimization potential
        let baseline_performance = 1_000_000.0; // Baseline for "acceptable" performance
        let improvement_potential = if performance_score > baseline_performance {
            (performance_score - baseline_performance) / baseline_performance
        } else {
            0.0
        };

        // If improvement potential is significant, add to candidates
        if improvement_potential > 0.2 {
            // 20% improvement potential threshold
            let strategy = self.select_optimization_strategy(&metrics);

            let candidate = OptimizationCandidate {
                function_name: function_name.to_string(),
                performance_score,
                improvement_potential,
                optimization_strategy: strategy,
            };

            let mut candidates = self.optimization_candidates.write().await;
            candidates.push(candidate);
        }

        Ok(())
    }

    /// Select the best optimization strategy for given metrics
    fn select_optimization_strategy(&self, metrics: &PerformanceMetrics) -> OptimizationStrategy {
        // Simple heuristic-based selection
        if metrics.execution_time_ns > 10_000_000 {
            // > 10ms
            OptimizationStrategy::Parallelization
        } else if metrics.memory_usage_bytes > 100_000_000 {
            // > 100MB
            OptimizationStrategy::MemoryOptimization
        } else if metrics.call_count > 1000 {
            OptimizationStrategy::CachingLayer
        } else {
            OptimizationStrategy::AlgorithmicImprovement
        }
    }

    /// Apply available optimizations
    pub async fn apply_optimizations(&self) -> Result<usize> {
        let candidates = self.optimization_candidates.read().await.clone();
        let mut applied_count = 0;

        for candidate in candidates {
            if let Ok(optimized) = self.create_optimized_version(&candidate).await {
                let mut active = self.active_optimizations.write().await;
                active.insert(candidate.function_name.clone(), optimized);
                applied_count += 1;
            }
        }

        // Clear processed candidates
        let mut candidates_store = self.optimization_candidates.write().await;
        candidates_store.clear();

        Ok(applied_count)
    }

    /// Create an optimized version of a function
    async fn create_optimized_version(
        &self,
        candidate: &OptimizationCandidate,
    ) -> Result<OptimizedFunction> {
        // In a real implementation, this would generate optimized WASM code
        // For now, we'll simulate the optimization

        let performance_gain = match candidate.optimization_strategy {
            OptimizationStrategy::Parallelization => 0.4, // 40% improvement
            OptimizationStrategy::MemoryOptimization => 0.3,
            OptimizationStrategy::CachingLayer => 0.5,
            OptimizationStrategy::AlgorithmicImprovement => 0.25,
            OptimizationStrategy::HotSwapReplacement => 0.6,
        };

        // Create a mock WASM module (in reality, this would be compiled optimized code)
        let wasm_bytes = include_bytes!("../mock_optimized.wasm");
        let wasm_module = Module::from_binary(&self.wasm_engine, wasm_bytes)?;

        Ok(OptimizedFunction {
            _wasm_module: wasm_module,
            performance_gain,
            _activation_time: chrono::Utc::now(),
        })
    }

    /// Get evolution statistics
    pub async fn get_statistics(&self) -> Result<EvolutionStats> {
        let metrics = self.metrics_store.read().await;
        let candidates = self.optimization_candidates.read().await;
        let active = self.active_optimizations.read().await;

        Ok(EvolutionStats {
            total_functions_monitored: metrics.len(),
            optimization_candidates: candidates.len(),
            active_optimizations: active.len(),
            total_performance_improvement: active.values().map(|opt| opt.performance_gain).sum(),
        })
    }

    /// Run self-improvement cycle
    pub async fn self_improve(&self) -> Result<()> {
        tracing::info!("Starting self-improvement cycle");

        // Analyze current performance
        let stats = self.get_statistics().await?;
        tracing::info!("Current stats: {:?}", stats);

        // Apply pending optimizations
        let applied = self.apply_optimizations().await?;
        if applied > 0 {
            tracing::info!("Applied {} optimizations", applied);
        }

        // Run benchmarks to measure improvement
        self.run_benchmarks().await?;

        tracing::info!("Self-improvement cycle completed");
        Ok(())
    }

    /// Run performance benchmarks
    async fn run_benchmarks(&self) -> Result<()> {
        // This would run criterion benchmarks
        // For simulation, we'll just log
        tracing::info!("Running performance benchmarks");
        Ok(())
    }
}

/// Evolution statistics
#[derive(Debug, Clone)]
pub struct EvolutionStats {
    pub total_functions_monitored: usize,
    pub optimization_candidates: usize,
    pub active_optimizations: usize,
    pub total_performance_improvement: f64,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[tokio::test]
    async fn test_evolution_engine_creation() {
        let engine = EvolutionEngine::new().unwrap();
        assert!(engine.metrics_store.read().await.is_empty());
    }

    #[tokio::test]
    async fn test_metrics_recording() {
        let engine = EvolutionEngine::new().unwrap();

        engine
            .record_metrics("test_function", 1_000_000, 1024)
            .await
            .unwrap();

        let stats = engine.get_statistics().await.unwrap();
        assert_eq!(stats.total_functions_monitored, 1);
    }
}