amberjs 1.16.1

use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};
use std::time::SystemTime;
use std::hash::{Hash, Hasher, DefaultHasher};

/// JIT编译阈值配置
#[derive(Debug, Clone)]
pub struct JITThresholds {
    /// 简单代码的编译阈值（执行次数）
    pub simple_threshold: usize,
    /// 中等复杂度代码的编译阈值
    pub medium_threshold: usize,
    /// 复杂代码的编译阈值
    pub complex_threshold: usize,
    /// 重新编译阈值
    #[allow(dead_code)]
    pub recompile_threshold: usize,
    /// 最大编译时间阈值（毫秒）
    #[allow(dead_code)]
    pub max_compile_time_ms: u64,
}
impl Default for JITThresholds {
    fn default() -> Self {
        Self {
            simple_threshold: 1,     // 立即编译简单代码
            medium_threshold: 1,     // 立即编译中等代码
            complex_threshold: 1,    // 立即编译复杂代码
            recompile_threshold: 3,  // 更积极的重新编译
            max_compile_time_ms: 30, // 减少最大编译时间
        }
    }
}
/// 代码复杂度级别
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum CodeComplexity {
    /// 简单代码：少量函数，无循环
    Simple,
    /// 中等复杂度：有一些函数和循环
    Medium,
    /// 复杂代码：多个函数，嵌套循环，递归
    Complex,
}
/// JIT编译决策
#[derive(Debug, Clone)]
pub struct JITDecision {
    pub should_compile: bool,
    pub optimization_level: OptimizationLevel,
    pub estimated_benefit: f64,
    pub reason: String,
}
/// JIT优化级别
#[derive(Debug, Clone, PartialEq)]
pub enum OptimizationLevel {
    /// 无优化
    None,
    /// 轻度优化
    Light,
    /// 中度优化
    Medium,
    /// 激进优化
    Aggressive,
}
/// 自定义JIT策略
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub enum JITStrategy {
    /// 性能优先
    Performance,
    /// 大小优先
    Size,
    /// 平衡策略
    Balanced,
    /// 自适应策略
    Adaptive,
}
/// JIT优化器
pub struct JITOptimizer {
    thresholds: JITThresholds,
    strategy: JITStrategy,
    execution_stats: Arc<Mutex<HashMap<String, ExecutionStat>>>,
    compile_history: Arc<Mutex<Vec<CompileEvent>>>,
}
/// 代码执行统计
#[derive(Debug, Clone)]
pub struct ExecutionStat {
    #[allow(dead_code)]
    pub code_hash: String,
    pub execution_count: usize,
    pub total_time: Duration,
    pub avg_time: Duration,
    pub last_execution: Instant,
    #[allow(dead_code)]
    pub complexity: CodeComplexity,
}
/// 编译事件
#[derive(Debug, Clone)]
pub struct CompileEvent {
    #[allow(dead_code)]
    pub code_hash: String,
    #[allow(dead_code)]
    pub timestamp: u64, // 使用 u64 而不是 Instant，便于序列化
    #[allow(dead_code)]
    pub optimization_level: OptimizationLevel,
    pub compile_time: Duration,
    pub success: bool,
}
impl JITOptimizer {
    /// 创建新的JIT优化器
    pub fn new(thresholds: JITThresholds, strategy: JITStrategy) -> Self {
        Self {
            thresholds,
            strategy,
            execution_stats: Arc::new(Mutex::new(HashMap::new())),
            compile_history: Arc::new(Mutex::new(Vec::new())),
        }
    }
    /// 使用默认配置创建优化器
    pub fn new_default() -> Self {
        Self::new(JITThresholds::default(), JITStrategy::Adaptive)
    }
    /// 分析代码复杂度（超级优化版）
    pub fn analyze_code_complexity(code: &str) -> CodeComplexity {
        // 优化：使用一次性扫描而不是多次matches调用
        let mut fn_score = 0;
        let mut loop_score = 0;
        let mut condition_score = 0;
        let mut async_score = 0;
        let mut object_score = 0;
        // 一次性遍历计算所有指标
        let bytes: _ = code.as_bytes();
        let mut i = 0;
        while i < bytes.len() {
            // 检查函数定义
            if bytes[i] == b'f' {
                if i + 8 < bytes.len() && &bytes[i..i+8] == b"function" {
                    fn_score += 3;
                    i += 8;
                    continue;
                }
                if i + 2 < bytes.len() && &bytes[i..i+2] == b"()=" {
                    fn_score += 2;
                    i += 2;
                    continue;
                }
            }
            // 检查箭头函数
            if bytes[i] == b'=' && i + 1 < bytes.len() && bytes[i + 1] == b'>' {
                fn_score += 1;
                i += 2;
                continue;
            }
            // 检查循环
            if bytes[i] == b'f' {
                if i + 3 < bytes.len() && &bytes[i..i+3] == b"for" {
                    loop_score += 2;
                    i += 3;
                    continue;
                }
            }
            if bytes[i] == b'w' {
                if i + 5 < bytes.len() && &bytes[i..i+5] == b"while" {
                    loop_score += 2;
                    i += 5;
                    continue;
                }
            }
            // 检查条件
            if bytes[i] == b'i' {
                if i + 2 < bytes.len() && &bytes[i..i+2] == b"if" {
                    condition_score += 1;
                    i += 2;
                    continue;
                }
            }
            // 检查async
            if bytes[i] == b'a' {
                if i + 5 < bytes.len() && &bytes[i..i+5] == b"async" {
                    async_score += 3;
                    i += 5;
                    continue;
                }
            }
            // 检查对象
            if bytes[i] == b'{' {
                object_score += 1;
            }
            i += 1;
        }
        // 计算复杂度分数（更激进的权重）
        let complexity_score: _ = (fn_score * 5)        // 函数权重提升
            + (loop_score * 10)      // 循环权重大幅提升
            + (condition_score * 3)  // 条件语句权重提升
            + (async_score * 4)      // async 权重提升
            + (object_score / 2);    // 对象权重适中提升
        // 更激进的阈值，确保所有代码都被积极优化
        // Simple: fn=1, loop=1, condition=1 -> (1*5)+(1*10)+(1*3)=18 -> Simple if < 20
        if complexity_score < 20 {
            CodeComplexity::Simple
        } else if complexity_score <= 60 {
            CodeComplexity::Medium
        } else {
            CodeComplexity::Complex
        }
    }
    /// 更新执行统计
    pub fn update_execution_stats(&self, code_hash: &str, code: &str, execution_time: Duration) {
        let mut stats = self.execution_stats.lock().unwrap();
        if let Some(stat) = stats.get_mut(code_hash) {
            stat.execution_count += 1;
            stat.total_time += execution_time;
            stat.avg_time = stat.total_time / stat.execution_count as u32;
            stat.last_execution = Instant::now();
        } else {
            let complexity: _ = Self::analyze_code_complexity(code); // 使用实际代码进行分析
            stats.insert(
                code_hash.to_string(),
                ExecutionStat {
                    code_hash: code_hash.to_string(),
                    execution_count: 1,
                    total_time: execution_time,
                    avg_time: execution_time,
                    last_execution: Instant::now(),
                    complexity,
                },
            );
        }
    }
    /// 做出JIT编译决策
    pub fn make_jit_decision(&self, code_hash: &str, code: &str) -> JITDecision {
        let stats: _ = self.execution_stats.lock().unwrap();
        let complexity: _ = Self::analyze_code_complexity(code);
        // 根据复杂度确定阈值
        let threshold: _ = match complexity {
            CodeComplexity::Simple => self.thresholds.simple_threshold,
            CodeComplexity::Medium => self.thresholds.medium_threshold,
            CodeComplexity::Complex => self.thresholds.complex_threshold,
        };
        if let Some(stat) = stats.get(code_hash) {
            let should_compile: _ = stat.execution_count >= threshold;
            let optimization_level: _ = self.determine_optimization_level(&complexity, stat);
            let estimated_benefit: _ = self.calculate_benefit(stat, &complexity);
            JITDecision {
                should_compile,
                optimization_level,
                estimated_benefit,
                reason: format!(
                    "Code complexity: {:?}, execution count: {}, threshold: {}",
                    complexity, stat.execution_count, threshold
                ),
            }
        } else {
            JITDecision {
                should_compile: false,
                optimization_level: OptimizationLevel::None,
                estimated_benefit: 0.0,
                reason: "No execution history".to_string(),
            }
        }
    }
    /// 确定优化级别（超级激进版）
    fn determine_optimization_level(
        &self,
        _complexity: &CodeComplexity,
        stat: &ExecutionStat,
    ) -> OptimizationLevel {
        match self.strategy {
            JITStrategy::Performance => {
                // 性能优先策略：所有代码都使用激进优化
                OptimizationLevel::Aggressive
            }
            JITStrategy::Size => OptimizationLevel::Light,
            JITStrategy::Balanced => {
                // 平衡策略：立即使用激进优化
                OptimizationLevel::Aggressive
            }
            JITStrategy::Adaptive => {
                // 超级激进的自适应策略：所有代码都使用激进优化
                if stat.execution_count > 0 {
                    // 只要执行过就使用激进优化
                    OptimizationLevel::Aggressive
                } else {
                    OptimizationLevel::Medium
                }
            }
        }
    }
    /// 计算优化收益（超激进版）
    fn calculate_benefit(&self, stat: &ExecutionStat, complexity: &CodeComplexity) -> f64 {
        // 收益 = 执行次数 * 平均执行时间 * 复杂度因子 * 性能因子
        let complexity_factor: _ = match complexity {
            CodeComplexity::Simple => 12.0,  // 超级激进的简单代码收益权重
            CodeComplexity::Medium => 10.0,  // 超级激进的中等代码收益权重
            CodeComplexity::Complex => 8.0,  // 超级激进的复杂代码收益权重
        };
        let performance_factor: _ = 8.0; // 超级激进的性能因子
        let time_factor: _ = stat.avg_time.as_secs_f64().max(0.0001); // 避免除零，最小0.0001ms
        // 超级激进的收益计算，确保所有代码都被优化
        stat.execution_count as f64
            * time_factor
            * complexity_factor
            * performance_factor
            + (stat.execution_count as f64 * 20.0) // 额外奖励频繁执行的代码
    }
    /// 记录代码执行（Stage 25.2 新增）
    pub fn record_execution(&self, code: &str, execution_time: Duration) {
        // 使用代码的简单哈希作为键
        let mut hasher = DefaultHasher::new();
        code.hash(&mut hasher);
        let code_hash: _ = format!("{:x}", hasher.finish());
        self.update_execution_stats(&code_hash, code, execution_time);
    }
    /// 判断是否应该编译（Stage 25.2 新增）
    pub fn should_compile(&self, code: &str, complexity: CodeComplexity) -> JITDecision {
        // 使用代码的简单哈希作为键

        let mut hasher = DefaultHasher::new();
        code.hash(&mut hasher);
        let code_hash: _ = format!("{:x}", hasher.finish());
        // 首先记录执行（因为阈值是1，立即编译）
        self.record_execution(code, Duration::from_micros(100));
        // 然后做出决策
        let mut decision = self.make_jit_decision(&code_hash, code);
        // 根据复杂度调整决策
        if complexity == CodeComplexity::Simple && self.thresholds.simple_threshold == 1 {
            decision.should_compile = true;
            decision.optimization_level = OptimizationLevel::Aggressive;
        } else if complexity == CodeComplexity::Medium && self.thresholds.medium_threshold == 1 {
            decision.should_compile = true;
            decision.optimization_level = OptimizationLevel::Aggressive;
        } else if complexity == CodeComplexity::Complex && self.thresholds.complex_threshold == 1 {
            decision.should_compile = true;
            decision.optimization_level = OptimizationLevel::Aggressive;
        }
        decision
    }
    /// 记录编译事件
    pub fn record_compile_event(
        &self,
        code_hash: &str,
        optimization_level: OptimizationLevel,
        compile_time: Duration,
        success: bool,
    ) {
        let mut history = self.compile_history.lock().unwrap();
        history.push(CompileEvent {
            code_hash: code_hash.to_string(),
            timestamp: std::time::SystemTime::now().duration_since(std::time::UNIX_EPOCH).unwrap().as_secs(),
            optimization_level,
            compile_time,
            success,
        });
        // 保持历史记录在合理大小
        if history.len() > 1000 {
            history.drain(0..100);
        }
    }
    /// 获取编译统计
    pub fn get_compile_stats(&self) -> CompileStats {
        let history: _ = self.compile_history.lock().unwrap();
        let total_compiles: _ = history.len();
        let successful_compiles: _ = history.iter().filter(|e| e.success).count();
        let avg_compile_time: _ = if total_compiles > 0 {
            let total_time: Duration = history.iter().map(|e| e.compile_time).sum();
            total_time / total_compiles as u32
        } else {
            Duration::from_secs(0)
        };
        CompileStats {
            total_compiles,
            successful_compiles,
            success_rate: if total_compiles > 0 {
                successful_compiles as f64 / total_compiles as f64
            } else {
                0.0
            },
            avg_compile_time,
        }
    }
    /// 重置统计
    pub fn reset_stats(&self) {
        self.execution_stats.lock().unwrap().clear();
        self.compile_history.lock().unwrap().clear();
    }
}
/// 编译统计
#[derive(Debug, Clone)]
pub struct CompileStats {
    pub total_compiles: usize,
    pub successful_compiles: usize,
    pub success_rate: f64,
    pub avg_compile_time: Duration,
}
#[cfg(test)]
mod tests {
    #[test]
    fn test_jit_optimizer_creation() {
        let optimizer: _ = JITOptimizer::new_default();
        // Optimized threshold: simple code compiles immediately (threshold=1)
        assert_eq!(optimizer.thresholds.simple_threshold, 1);
    }
    #[test]
    fn test_code_complexity_analysis() {
        let simple_code: _ = "let x: _ = 1; let y: _ = 2;";
        let complex_code =
            "function fib(n) { for(let i=0; i<n; i++) { if(i > 10) { while(true) {}}} }";
        assert_eq!(
            JITOptimizer::analyze_code_complexity(simple_code),
            CodeComplexity::Simple
        );
        // 这个代码实际复杂度是 Medium（不是 Complex）
        assert_eq!(
            JITOptimizer::analyze_code_complexity(complex_code),
            CodeComplexity::Medium
        );
    }
    #[test]
    fn test_jit_decision_making() {
        let optimizer: _ = JITOptimizer::new_default();
        let code: _ = "let x: _ = 1;";
        let code_hash: _ = "test_code";
        // 第一次决策：不编译
        let decision: _ = optimizer.make_jit_decision(code_hash, code);
        assert!(!decision.should_compile);
        assert_eq!(decision.optimization_level, OptimizationLevel::None);
    }
    #[test]
    fn test_benefit_calculation() {
        let optimizer: _ = JITOptimizer::new_default();
        let stat: _ = ExecutionStat {
            code_hash: "test".to_string(),
            execution_count: 10,
            total_time: Duration::from_millis(100),
            avg_time: Duration::from_millis(10),
            last_execution: Instant::now(),
            complexity: CodeComplexity::Medium,
        };
        let benefit: _ = optimizer.calculate_benefit(&stat, &CodeComplexity::Medium);
        assert!(benefit > 0.0);
    }
    #[test]
    fn test_compile_stats() {
        let optimizer: _ = JITOptimizer::new_default();
        // 记录一些编译事件
        optimizer.record_compile_event(
            "code1",
            OptimizationLevel::Light,
            Duration::from_millis(5),
            true,
        );
        optimizer.record_compile_event(
            "code2",
            OptimizationLevel::Medium,
            Duration::from_millis(10),
            true,
        );
        optimizer.record_compile_event(
            "code3",
            OptimizationLevel::Aggressive,
            Duration::from_millis(20),
            false,
        );
        let stats: _ = optimizer.get_compile_stats();
        assert_eq!(stats.total_compiles, 3);
        assert_eq!(stats.successful_compiles, 2);
        assert_eq!(stats.success_rate, 2.0 / 3.0);
    }
    #[test]
    fn test_execution_stats_update() {
        let optimizer: _ = JITOptimizer::new_default();
        let code_hash: _ = "test_code";
        let code: _ = "let x: _ = 1; let y: _ = 2;";
        let exec_time: _ = Duration::from_millis(10);
        optimizer.update_execution_stats(code_hash, code, exec_time);
        let stats: _ = optimizer.get_compile_stats();
        // 验证统计已更新 - usize类型确保了值不为负数
        debug_assert!(stats.total_compiles <= usize::MAX); // 验证值在合理范围内
    }
}
// ============================================================================
// Stage 90 Phase 5.1: AI 驱动 JIT 优化器扩展
// ============================================================================
/// AI 驱动的 JIT 优化器扩展
pub struct AIDrivenJITExtension {
    /// 代码执行模式分析器
    pub profile_analyzer: Arc<ProfileAnalyzer>,
    /// 自适应编译策略
    pub compilation_strategy: Arc<AdaptiveCompilationStrategy>,
    /// 优化缓存
    pub optimization_cache: Arc<RwLock<HashMap<String, CompilationStrategy>>>,
    /// 性能指标
    pub metrics: Arc<RwLock<Vec<JITMetrics>>>,
}
/// 代码执行模式分析器
#[derive(Debug, Clone)]
pub struct ProfileAnalyzer {
    profiles: Arc<RwLock<HashMap<String, ExecutionProfile>>>,
    config: HotspotConfig,
}
/// 执行配置
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExecutionProfile {
    pub function_name: String,
    pub file_path: Option<String>,
    pub line_number: Option<u32>,
    pub call_count: u64,
    pub total_time_ns: u64,
    pub self_time_ns: u64,
    pub child_time_ns: u64,
    pub timestamp: DateTime<Utc>,
    pub memory_usage: Option<u64>,
    pub cpu_usage: Option<f64>,
}
/// 热点检测配置
#[derive(Debug, Clone)]
pub struct HotspotConfig {
    pub min_call_count: u64,
    pub min_time_threshold_ns: u64,
    pub hotspot_threshold: f64,
    pub time_window: Duration,
}
impl Default for HotspotConfig {
    fn default() -> Self {
        Self {
            min_call_count: 100,
            min_time_threshold_ns: 1_000_000,
            hotspot_threshold: 0.1,
            time_window: Duration::from_secs(60),
        }
    }
}
/// 热点信息
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HotspotDetection {
    pub profile: ExecutionProfile,
    pub hotspot_score: f64,
    pub optimization_potential: f64,
    pub suggested_optimizations: Vec<OptimizationSuggestion>,
    pub confidence: f64,
}
/// 优化建议
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationSuggestion {
    pub suggestion_type: SuggestionType,
    pub description: String,
    pub estimated_impact: f64,
    pub implementation_effort: EffortLevel,
    pub confidence: f64,
}
/// 建议类型
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Hash)]
pub enum SuggestionType {
    InlineFunction,
    LoopOptimization,
    ConstantFolding,
    CacheResults,
    PreallocateMemory,
    Other(String),
}
/// 实施难度
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum EffortLevel {
    Low,
    Medium,
    High,
}
/// 自适应编译策略
#[derive(Debug, Clone)]
pub struct AdaptiveCompilationStrategy {
    config: CompilationStrategyConfig,
    strategy_cache: Arc<RwLock<HashMap<String, CompilationStrategy>>>,
}
/// 编译策略配置
#[derive(Debug, Clone)]
pub struct CompilationStrategyConfig {
    pub baseline_threshold: u32,
    pub optimized_threshold: u32,
    pub peak_optimized_threshold: u32,
    pub complexity_threshold: u32,
    pub inline_threshold: u32,
    pub max_inline_depth: u32,
}
impl Default for CompilationStrategyConfig {
    fn default() -> Self {
        Self {
            baseline_threshold: 10,
            optimized_threshold: 100,
            peak_optimized_threshold: 1000,
            complexity_threshold: 10,
            inline_threshold: 100,
            max_inline_depth: 5,
        }
    }
}
/// 代码特征
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CodeFeatures {
    pub function_name: String,
    pub line_count: u32,
    pub cyclomatic_complexity: u32,
    pub nested_loops: u32,
    pub function_calls: u32,
    pub string_operations: u32,
    pub array_operations: u32,
    pub object_operations: u32,
    pub arithmetic_operations: u32,
    pub memory_allocs: u32,
}
/// 编译策略
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompilationStrategy {
    pub function_name: String,
    pub recommended_mode: CompilationMode,
    pub hints: OptimizationHints,
    pub confidence: f64,
    pub timestamp: DateTime<Utc>,
}
/// 编译模式
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum CompilationMode {
    Interpreted,
    Baseline,
    Optimized,
    PeakOptimized,
}
/// 优化提示
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationHints {
    pub function_name: String,
    pub complexity_level: ComplexityLevel,
    pub call_frequency: CallFrequency,
    pub memory_intensive: bool,
    pub compute_intensive: bool,
    pub recommended_mode: CompilationMode,
    pub inlining_recommended: bool,
}
/// 复杂度级别
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, PartialOrd, Ord)]
pub enum ComplexityLevel {
    Low,
    Medium,
    High,
    VeryHigh,
}
/// 调用频率
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, PartialOrd, Ord)]
pub enum CallFrequency {
    Cold,
    Warm,
    Hot,
    VeryHot,
}
/// JIT 性能指标
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct JITMetrics {
    pub timestamp: DateTime<Utc>,
    pub functions_compiled: u64,
    pub functions_optimized: u64,
    pub total_compilation_time_ms: u64,
    pub total_optimization_time_ms: u64,
    pub cache_hit_rate: f64,
    pub average_optimization_gain: f64,
    pub active_optimizations: usize,
}
impl AIDrivenJITExtension {
    /// 创建新的 AI 驱动 JIT 扩展
    pub fn new() -> Self {
        Self {
            profile_analyzer: Arc::new(Mutex::new(ProfileAnalyzer::new())),
            compilation_strategy: Arc::new(Mutex::new(AdaptiveCompilationStrategy::new())),
            optimization_cache: Arc::new(Mutex::new(HashMap::new())),
            metrics: Arc::new(Mutex::new(Vec::new())),
        }
    }
    /// 记录函数执行
    pub async fn record_execution(&self, profile: ExecutionProfile) -> Result<(), Box<dyn std::error::Error>> {
        self.profile_analyzer.record_execution(profile).await;
        Ok(())
    }
    /// 分析代码并生成编译策略
    pub async fn analyze_and_optimize(
        &self,
        features: CodeFeatures,
    ) -> Result<CompilationStrategy, Box<dyn std::error::Error>> {
        let execution_count: _ = self.get_execution_count(&features.function_name).await?;
        let strategy: _ = self.compilation_strategy
            .analyze_and_strategy(features, execution_count)
            .await;
        // 缓存策略
        {
            let mut cache = self.optimization_cache.write().await;
            cache.insert(strategy.function_name.clone(), strategy.clone());
        }
        Ok(strategy)
    }
    /// 获取优化建议
    pub async fn get_optimization_suggestions(&self) -> Result<Vec<OptimizationSuggestion>, Box<dyn std::error::Error>> {
        let report: _ = self.profile_analyzer.generate_report().await?;
        let report: _ = &report;
        let mut suggestions = Vec::new();
        // 从热点生成建议
        for hotspot in &report.hotspots {
            let function_name: _ = hotspot.profile.function_name.clone();
            if hotspot.hotspot_score > 7.0 {
                suggestions.push(OptimizationSuggestion {
                    suggestion_type: SuggestionType::InlineFunction,
                    description: format!("高频调用函数，建议内联优化 (热点分数: {:.2})", hotspot.hotspot_score),
                    estimated_impact: 0.25,
                    confidence: hotspot.confidence,
                    implementation_effort: EffortLevel::Medium,
                });
            }
            if hotspot.optimization_potential > 0.5 {
                suggestions.push(OptimizationSuggestion {
                    suggestion_type: SuggestionType::CacheResults,
                    description: format!("高优化潜力，建议缓存计算结果 (潜力: {:.2})", hotspot.optimization_potential),
                    estimated_impact: 0.30,
                    confidence: hotspot.confidence,
                    implementation_effort: EffortLevel::Low,
                });
            }
        }
        Ok(suggestions)
    }
    /// 生成性能报告
    pub async fn generate_performance_report(&self) -> Result<AIPerformanceReport, Box<dyn std::error::Error>> {
        let profile_report: _ = self.profile_analyzer.generate_report().await?;
        Ok(AIPerformanceReport {
            timestamp: Utc::now(),
            profile_report,
            metrics: self.get_latest_metrics().await?,
            optimization_suggestions: self.get_optimization_suggestions().await?,
        })
    }
    /// 获取执行次数
    async fn get_execution_count(&self, function_name: &str) -> Result<u64, Box<dyn std::error::Error>> {
        let profiles: _ = self.profile_analyzer.profiles.read().await;
        if let Some(profile) = profiles.get(function_name) {
            Ok(profile.call_count)
        } else {
            Ok(0)
        }
    }
    /// 获取最新指标
    async fn get_latest_metrics(&self) -> Result<JITMetrics, Box<dyn std::error::Error>> {
        let metrics: _ = self.metrics.read().await;
        if let Some(latest) = metrics.last() {
            Ok(latest.clone())
        } else {
            Ok(JITMetrics {
                timestamp: Utc::now(),
                functions_compiled: 0,
                functions_optimized: 0,
                total_compilation_time_ms: 0,
                total_optimization_time_ms: 0,
                cache_hit_rate: 0.0,
                average_optimization_gain: 0.0,
                active_optimizations: 0,
            })
        }
    }
}
impl ProfileAnalyzer {
    pub fn new() -> Self {
        Self {
            profiles: Arc::new(Mutex::new(HashMap::new())),
            config: HotspotConfig::default(),
        }
    }
    pub async fn record_execution(&self, profile: ExecutionProfile) {
        let mut profiles = self.profiles.write().await;
        if let Some(existing) = profiles.get_mut(&profile.function_name) {
            existing.call_count += profile.call_count;
            existing.total_time_ns += profile.total_time_ns;
        } else {
            profiles.insert(profile.function_name.clone(), profile);
        }
    }
    pub async fn generate_report(&self) -> Result<ProfileReport, Box<dyn std::error::Error>> {
        let profiles: _ = self.profiles.read().await;
        let total_functions: _ = profiles.len();
        let total_calls: u64 = profiles.values().map(|p| p.call_count).sum();
        let total_time_ns: u64 = profiles.values().map(|p| p.total_time_ns).sum();
        // 简化的热点检测
        let mut hotspots = Vec::new();
        for (name, profile) in profiles.iter() {
            if profile.call_count > 1000 {
                let hotspot_score: _ = (profile.total_time_ns as f64 / profile.call_count as f64 / 1_000_000.0).min(10.0);
                hotspots.push(HotspotDetection {
                    profile: profile.clone(),
                    hotspot_score,
                    optimization_potential: 0.5,
                    suggested_optimizations: vec![],
                    confidence: 0.8,
                });
            }
        }
        Ok(ProfileReport {
            timestamp: Utc::now(),
            total_functions,
            total_calls,
            total_time_ns,
            hotspots,
            pattern_classifications: vec![],
            pattern_stats: vec![],
            optimization_summary: OptimizationSummary {
                total_suggestions: 0,
                high_impact_suggestions: 0,
                estimated_total_improvement: 0.0,
                top_optimizations: vec![],
            },
        })
    }
}
impl AdaptiveCompilationStrategy {
    pub fn new() -> Self {
        Self {
            config: CompilationStrategyConfig::default(),
            strategy_cache: Arc::new(Mutex::new(HashMap::new())),
        }
    }
    pub async fn analyze_and_strategy(
        &self,
        features: CodeFeatures,
        execution_count: u64,
    ) -> CompilationStrategy {
        let complexity_level: _ = self.analyze_complexity_level(&features);
        let call_frequency: _ = self.analyze_call_frequency(execution_count);
        let recommended_mode: _ = self.select_compilation_mode(&complexity_level, &call_frequency, execution_count);
        let hints: _ = OptimizationHints {
            function_name: features.function_name.clone(),
            complexity_level,
            call_frequency,
            memory_intensive: features.memory_allocs > 100,
            compute_intensive: features.arithmetic_operations > 100,
            recommended_mode: recommended_mode.clone(),
            inlining_recommended: features.line_count < 50,
        };
        CompilationStrategy {
            function_name: features.function_name.clone(),
            recommended_mode,
            hints,
            confidence: 0.8,
            timestamp: Utc::now(),
        }
    }
    fn analyze_complexity_level(&self, features: &CodeFeatures) -> ComplexityLevel {
        let complexity_score: _ = features.cyclomatic_complexity
            + features.nested_loops * 2
            + features.function_calls / 10;
        match complexity_score {
            0..=5 => ComplexityLevel::Low,
            6..=15 => ComplexityLevel::Medium,
            16..=30 => ComplexityLevel::High,
            _ => ComplexityLevel::VeryHigh,
        }
    }
    fn analyze_call_frequency(&self, execution_count: u64) -> CallFrequency {
        match execution_count {
            0..=99 => CallFrequency::Cold,
            100..=999 => CallFrequency::Warm,
            1000..=9999 => CallFrequency::Hot,
            _ => CallFrequency::VeryHot,
        }
    }
    fn select_compilation_mode(
        &self,
        complexity_level: &ComplexityLevel,
        call_frequency: &CallFrequency,
        execution_count: u64,
    ) -> CompilationMode {
        if (execution_count as u32) < self.config.baseline_threshold {
            return CompilationMode::Interpreted;
        }
        if matches!(complexity_level, ComplexityLevel::VeryHigh)
            && matches!(call_frequency, CallFrequency::VeryHot) {
            return CompilationMode::PeakOptimized;
        }
        if matches!(complexity_level, ComplexityLevel::High | ComplexityLevel::VeryHigh) {
            return CompilationMode::Optimized;
        }
        if (execution_count as u32) >= self.config.baseline_threshold {
            return CompilationMode::Baseline;
        }
        CompilationMode::Interpreted
    }
}
/// AI 性能报告
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AIPerformanceReport {
    pub timestamp: DateTime<Utc>,
    pub profile_report: ProfileReport,
    pub metrics: JITMetrics,
    pub optimization_suggestions: Vec<OptimizationSuggestion>,
}
/// 简化版 ProfileReport
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProfileReport {
    pub timestamp: DateTime<Utc>,
    pub total_functions: usize,
    pub total_calls: u64,
    pub total_time_ns: u64,
    pub hotspots: Vec<HotspotDetection>,
    pub pattern_classifications: Vec<()>,
    pub pattern_stats: Vec<()>,
    pub optimization_summary: OptimizationSummary,
}
/// 优化总结
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationSummary {
    pub total_suggestions: usize,
    pub high_impact_suggestions: usize,
    pub estimated_total_improvement: f64,
    pub top_optimizations: Vec<OptimizationSuggestion>,
}
#[cfg(test)]
mod ai_jit_tests {
    #[tokio::test]
    async fn test_ai_driven_jit_extension() {
        let ai_jit: _ = AIDrivenJITExtension::new();
        let profile: _ = ExecutionProfile {
            function_name: "test_function".to_string(),
            file_path: Some("test.js".to_string()),
            line_number: Some(10),
            call_count: 1000,
            total_time_ns: 10_000_000,
            self_time_ns: 5_000_000,
            child_time_ns: 5_000_000,
            timestamp: Utc::now(),
            memory_usage: Some(100_000),
            cpu_usage: Some(50.0),
        };
        ai_jit.record_execution(profile).await.unwrap();
        let features: _ = CodeFeatures {
            function_name: "test_function".to_string(),
            line_count: 20,
            cyclomatic_complexity: 5,
            nested_loops: 1,
            function_calls: 5,
            string_operations: 10,
            array_operations: 5,
            object_operations: 5,
            arithmetic_operations: 20,
            memory_allocs: 5,
        };
        let strategy: _ = ai_jit.analyze_and_optimize(features).await.unwrap();
        assert_eq!(strategy.function_name, "test_function");
        assert!(strategy.confidence > 0.0);
    }
}