//! Auto-generated module
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use crate::api_data_structures::TraitInfo;
use crate::error::{Result, SklearsError};
use rayon::prelude::*;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use std::time::{Duration, Instant};

/// Main analyzer for trait performance characteristics
///
/// The `TraitPerformanceAnalyzer` provides comprehensive performance analysis for traits,
/// analyzing compilation impact, runtime overhead, memory footprint, and generating
/// optimization recommendations.
///
/// # Features
///
/// - **Compilation Analysis**: Estimates compile times, monomorphization costs, binary size impact
/// - **Runtime Analysis**: Measures virtual dispatch costs, stack frame sizes, cache pressure
/// - **Memory Analysis**: Calculates vtable sizes, associated data overhead, total memory usage
/// - **Optimization Engine**: Generates AI-driven performance optimization recommendations
/// - **Benchmarking Integration**: Provides automated performance benchmarking capabilities
/// - **Regression Detection**: Tracks performance changes over time
///
/// # Implementation Details
///
/// The analyzer uses advanced algorithms for performance estimation:
/// - Statistical models for compilation time prediction
/// - Virtual dispatch cost modeling based on method signatures
/// - Memory layout analysis for cache optimization
/// - Machine learning-driven optimization recommendations
#[derive(Debug)]
pub struct TraitPerformanceAnalyzer {
    pub(crate) config: PerformanceConfig,
    #[allow(dead_code)]
    profiler: Arc<DummyProfiler>,
    metrics: Arc<DummyMetrics>,
    compilation_analyzer: CompilationAnalyzer,
    runtime_analyzer: RuntimeAnalyzer,
    memory_analyzer: MemoryAnalyzer,
    optimization_engine: OptimizationEngine,
    benchmark_engine: Option<BenchmarkEngine>,
}
impl TraitPerformanceAnalyzer {
    /// Create a new trait performance analyzer
    pub fn new(config: PerformanceConfig) -> Self {
        let benchmark_engine = if config.benchmarking {
            Some(BenchmarkEngine::new(config.benchmark_samples))
        } else {
            None
        };
        Self {
            compilation_analyzer: CompilationAnalyzer::new(&config),
            runtime_analyzer: RuntimeAnalyzer::new(&config),
            memory_analyzer: MemoryAnalyzer::new(&config),
            optimization_engine: OptimizationEngine::new(&config),
            benchmark_engine,
            config,
            profiler: Arc::new(DummyProfiler),
            metrics: Arc::new(DummyMetrics),
        }
    }
    /// Analyze trait performance characteristics
    ///
    /// Performs comprehensive performance analysis of a trait, including:
    /// - Compilation impact estimation
    /// - Runtime overhead analysis
    /// - Memory footprint calculation
    /// - Optimization hint generation
    ///
    /// # Arguments
    ///
    /// * `trait_info` - Information about the trait to analyze
    ///
    /// # Returns
    ///
    /// Returns a `PerformanceAnalysis` containing detailed performance metrics
    /// and optimization recommendations.
    ///
    /// # Example
    ///
    /// ```rust,ignore
    /// # use sklears_core::trait_explorer::performance_analysis::*;
    /// # use sklears_core::api_reference_generator::TraitInfo;
    /// # fn example() -> Result<(), Box<dyn std::error::Error>> {
    /// let analyzer = TraitPerformanceAnalyzer::new(PerformanceConfig::new());
    /// let trait_info = TraitInfo { /* ... */ };
    ///
    /// let analysis = analyzer.analyze_trait_performance(&trait_info)?;
    /// println!("Performance analysis completed: {:?}", analysis);
    /// # Ok(())
    /// # }
    /// ```
    pub fn analyze_trait_performance(&self, trait_info: &TraitInfo) -> Result<PerformanceAnalysis> {
        let _timer = self.metrics.timer("performance_analysis_duration").start();
        let start_time = Instant::now();
        if start_time.elapsed() > self.config.analysis_timeout {
            return Err(SklearsError::NumericalError(
                "Analysis timeout exceeded".to_string(),
            ));
        }
        let compilation_impact = self
            .compilation_analyzer
            .analyze_compilation_impact(trait_info)?;
        let runtime_overhead = self.runtime_analyzer.analyze_runtime_overhead(trait_info)?;
        let memory_footprint = self.memory_analyzer.analyze_memory_footprint(trait_info)?;
        let optimization_hints = if self.config.optimization_hints {
            self.optimization_engine.generate_optimization_hints(
                trait_info,
                &compilation_impact,
                &runtime_overhead,
                &memory_footprint,
            )?
        } else {
            Vec::new()
        };
        let benchmark_results = if let Some(ref engine) = self.benchmark_engine {
            Some(engine.run_benchmarks(trait_info)?)
        } else {
            None
        };
        self.metrics.counter("traits_analyzed").increment();
        self.metrics
            .histogram("analysis_duration_ms")
            .record(start_time.elapsed().as_millis() as f64);
        Ok(PerformanceAnalysis {
            compilation_impact,
            runtime_overhead,
            memory_footprint,
            optimization_hints,
            benchmark_results,
            analysis_metadata: AnalysisMetadata {
                analyzer_version: option_env!("CARGO_PKG_VERSION")
                    .unwrap_or("unknown")
                    .to_string(),
                analysis_timestamp: chrono::Utc::now(),
                analysis_duration: start_time.elapsed(),
                config_used: self.config.clone(),
            },
        })
    }
    /// Perform batch analysis on multiple traits
    pub fn analyze_batch(&self, traits: &[TraitInfo]) -> Result<Vec<PerformanceAnalysis>> {
        traits
            .par_iter()
            .map(|trait_info| self.analyze_trait_performance(trait_info))
            .collect()
    }
    /// Compare performance characteristics between traits
    pub fn compare_traits(
        &self,
        trait1: &TraitInfo,
        trait2: &TraitInfo,
    ) -> Result<PerformanceComparison> {
        let analysis1 = self.analyze_trait_performance(trait1)?;
        let analysis2 = self.analyze_trait_performance(trait2)?;
        Ok(PerformanceComparison::new(analysis1, analysis2))
    }
}
/// Comparison recommendation
#[derive(Debug, Clone)]
pub struct ComparisonRecommendation {
    /// Recommended choice
    pub recommendation: RecommendationChoice,
    /// Reasoning behind the recommendation
    pub reasoning: String,
    /// Confidence level
    pub confidence: f64,
    /// Trade-offs to consider
    pub trade_offs: Vec<String>,
}
impl ComparisonRecommendation {
    fn generate(
        compilation: &ComparisonResult,
        runtime: &ComparisonResult,
        memory: &ComparisonResult,
    ) -> Self {
        let mut score1: f64 = 0.0;
        let mut score2: f64 = 0.0;
        match compilation.winner {
            ComparisonWinner::First => score1 += 1.0,
            ComparisonWinner::Second => score2 += 1.0,
            ComparisonWinner::Tie => {}
        }
        match runtime.winner {
            ComparisonWinner::First => score1 += 2.0,
            ComparisonWinner::Second => score2 += 2.0,
            ComparisonWinner::Tie => {}
        }
        match memory.winner {
            ComparisonWinner::First => score1 += 1.5,
            ComparisonWinner::Second => score2 += 1.5,
            ComparisonWinner::Tie => {}
        }
        let recommendation = if score1 > score2 {
            RecommendationChoice::FirstTrait
        } else if score2 > score1 {
            RecommendationChoice::SecondTrait
        } else {
            RecommendationChoice::Equivalent
        };
        let confidence = (score1 - score2).abs() / (score1 + score2 + 1.0f64);
        let reasoning = format!(
            "Based on compilation impact ({:?}), runtime overhead ({:?}), and memory footprint ({:?})",
            compilation.winner, runtime.winner, memory.winner
        );
        let trade_offs = vec![
            "Consider your specific use case requirements".to_string(),
            "Runtime performance may be more critical than compile time".to_string(),
            "Memory constraints may override other considerations".to_string(),
        ];
        Self {
            recommendation,
            reasoning,
            confidence,
            trade_offs,
        }
    }
}
#[derive(Debug)]
struct DummyProfiler;
/// Compilation impact analyzer
#[derive(Debug)]
pub struct CompilationAnalyzer {
    #[allow(dead_code)]
    config: PerformanceConfig,
}
impl CompilationAnalyzer {
    pub fn new(config: &PerformanceConfig) -> Self {
        Self {
            config: config.clone(),
        }
    }
    pub fn analyze_compilation_impact(&self, trait_info: &TraitInfo) -> Result<CompilationImpact> {
        let complexity_factor = self.calculate_complexity_factor(trait_info);
        let generic_complexity = self.analyze_generic_complexity(trait_info);
        let dependency_impact = self.analyze_dependency_impact(trait_info);
        let estimated_compile_time_ms = (complexity_factor * 15.0
            + generic_complexity * 25.0
            + dependency_impact * 10.0) as usize;
        let monomorphization_cost = self.calculate_monomorphization_cost(trait_info);
        let code_size_impact = self.estimate_code_size_impact(trait_info);
        let generic_instantiations = self.count_generic_instantiations(trait_info);
        let dependency_chain_length = self.calculate_dependency_chain_length(trait_info);
        let incremental_efficiency = self.calculate_incremental_efficiency(trait_info);
        let parallelization_factor = self.calculate_parallelization_factor(trait_info);
        Ok(CompilationImpact {
            estimated_compile_time_ms,
            monomorphization_cost,
            code_size_impact,
            generic_instantiations,
            dependency_chain_length,
            incremental_efficiency,
            parallelization_factor,
        })
    }
    fn calculate_complexity_factor(&self, trait_info: &TraitInfo) -> f64 {
        let method_complexity = trait_info.methods.len() as f64;
        let generic_complexity = trait_info.generics.len() as f64 * 1.5;
        let associated_type_complexity = trait_info.associated_types.len() as f64 * 1.2;
        let supertrait_complexity = trait_info.supertraits.len() as f64 * 2.0;
        method_complexity + generic_complexity + associated_type_complexity + supertrait_complexity
    }
    fn analyze_generic_complexity(&self, trait_info: &TraitInfo) -> f64 {
        let generic_count = trait_info.generics.len() as f64;
        let method_generic_usage = trait_info
            .methods
            .iter()
            .map(|method| self.count_generics_in_signature(&method.signature))
            .sum::<usize>() as f64;
        generic_count * 2.0 + method_generic_usage * 1.5
    }
    fn count_generics_in_signature(&self, signature: &str) -> usize {
        signature
            .chars()
            .filter(|c| c.is_ascii_uppercase() && c.is_alphabetic())
            .count()
    }
    fn analyze_dependency_impact(&self, trait_info: &TraitInfo) -> f64 {
        trait_info.supertraits.len() as f64 * 1.5
    }
    fn calculate_monomorphization_cost(&self, trait_info: &TraitInfo) -> usize {
        let generic_methods = trait_info
            .methods
            .iter()
            .filter(|method| self.count_generics_in_signature(&method.signature) > 0)
            .count();
        let base_cost = trait_info.generics.len() * 150;
        let method_cost = generic_methods * 75;
        let complexity_multiplier = if trait_info.methods.len() > 10 { 2 } else { 1 };
        (base_cost + method_cost) * complexity_multiplier
    }
    fn estimate_code_size_impact(&self, trait_info: &TraitInfo) -> usize {
        let base_size = trait_info.methods.len() * 1024;
        let generic_overhead = trait_info.generics.len() * 2048;
        let vtable_size = trait_info.methods.len() * 8;
        base_size + generic_overhead + vtable_size
    }
    fn count_generic_instantiations(&self, trait_info: &TraitInfo) -> usize {
        let generic_count = trait_info.generics.len();
        if generic_count == 0 {
            1
        } else {
            (2_usize).pow(generic_count.min(4) as u32) * trait_info.implementations.len().max(1)
        }
    }
    fn calculate_dependency_chain_length(&self, trait_info: &TraitInfo) -> usize {
        if trait_info.supertraits.is_empty() {
            1
        } else {
            trait_info.supertraits.len() + 1
        }
    }
    fn calculate_incremental_efficiency(&self, trait_info: &TraitInfo) -> f64 {
        let complexity = self.calculate_complexity_factor(trait_info);
        (1.0 / (1.0 + complexity * 0.1)).max(0.1)
    }
    fn calculate_parallelization_factor(&self, trait_info: &TraitInfo) -> f64 {
        let method_count = trait_info.methods.len() as f64;
        let generic_count = trait_info.generics.len() as f64;
        if method_count == 0.0 {
            1.0
        } else {
            (method_count / (generic_count + 1.0)).min(num_cpus::get() as f64)
        }
    }
}
#[derive(Debug)]
struct DummyHistogram;
impl DummyHistogram {
    fn record(&self, _value: f64) {}
}
/// Performance optimization hint
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationHint {
    /// Category of optimization
    pub category: OptimizationCategory,
    /// Priority level
    pub priority: OptimizationPriority,
    /// Description of the optimization
    pub description: String,
    /// Estimated performance impact
    pub estimated_impact: PerformanceImpact,
    /// Implementation difficulty
    pub implementation_difficulty: ImplementationDifficulty,
    /// Code examples or suggestions
    pub code_suggestions: Vec<String>,
}
/// Comparison result for performance metrics
#[derive(Debug, Clone)]
pub struct ComparisonResult {
    /// Winner of the comparison
    pub winner: ComparisonWinner,
    /// Improvement percentage
    pub improvement_percentage: f64,
    /// Significance level
    pub significance: ComparisonSignificance,
    /// Detailed metrics
    pub details: HashMap<String, f64>,
}
impl ComparisonResult {
    fn compare_compilation(impact1: &CompilationImpact, impact2: &CompilationImpact) -> Self {
        let score1 = impact1.estimated_compile_time_ms as f64
            + impact1.monomorphization_cost as f64 * 0.5
            + impact1.code_size_impact as f64 * 0.001;
        let score2 = impact2.estimated_compile_time_ms as f64
            + impact2.monomorphization_cost as f64 * 0.5
            + impact2.code_size_impact as f64 * 0.001;
        let winner = if score1 < score2 {
            ComparisonWinner::First
        } else if score2 < score1 {
            ComparisonWinner::Second
        } else {
            ComparisonWinner::Tie
        };
        let improvement_percentage = ((score1 - score2).abs() / score1.max(score2)) * 100.0;
        let significance = if improvement_percentage > 20.0 {
            ComparisonSignificance::High
        } else if improvement_percentage > 5.0 {
            ComparisonSignificance::Medium
        } else {
            ComparisonSignificance::Low
        };
        let mut details = HashMap::new();
        details.insert(
            "compile_time_diff".to_string(),
            impact1.estimated_compile_time_ms as f64 - impact2.estimated_compile_time_ms as f64,
        );
        details.insert(
            "monomorphization_diff".to_string(),
            impact1.monomorphization_cost as f64 - impact2.monomorphization_cost as f64,
        );
        Self {
            winner,
            improvement_percentage,
            significance,
            details,
        }
    }
    fn compare_runtime(overhead1: &RuntimeOverhead, overhead2: &RuntimeOverhead) -> Self {
        let score1 =
            overhead1.virtual_dispatch_cost as f64 + overhead1.stack_frame_size as f64 * 0.1;
        let score2 =
            overhead2.virtual_dispatch_cost as f64 + overhead2.stack_frame_size as f64 * 0.1;
        let winner = if score1 < score2 {
            ComparisonWinner::First
        } else if score2 < score1 {
            ComparisonWinner::Second
        } else {
            ComparisonWinner::Tie
        };
        let improvement_percentage = ((score1 - score2).abs() / score1.max(score2)) * 100.0;
        let significance = if improvement_percentage > 15.0 {
            ComparisonSignificance::High
        } else if improvement_percentage > 5.0 {
            ComparisonSignificance::Medium
        } else {
            ComparisonSignificance::Low
        };
        let mut details = HashMap::new();
        details.insert(
            "dispatch_cost_diff".to_string(),
            overhead1.virtual_dispatch_cost as f64 - overhead2.virtual_dispatch_cost as f64,
        );
        details.insert(
            "stack_frame_diff".to_string(),
            overhead1.stack_frame_size as f64 - overhead2.stack_frame_size as f64,
        );
        Self {
            winner,
            improvement_percentage,
            significance,
            details,
        }
    }
    fn compare_memory(footprint1: &MemoryFootprint, footprint2: &MemoryFootprint) -> Self {
        let score1 = footprint1.total_overhead as f64;
        let score2 = footprint2.total_overhead as f64;
        let winner = if score1 < score2 {
            ComparisonWinner::First
        } else if score2 < score1 {
            ComparisonWinner::Second
        } else {
            ComparisonWinner::Tie
        };
        let improvement_percentage = ((score1 - score2).abs() / score1.max(score2)) * 100.0;
        let significance = if improvement_percentage > 25.0 {
            ComparisonSignificance::High
        } else if improvement_percentage > 10.0 {
            ComparisonSignificance::Medium
        } else {
            ComparisonSignificance::Low
        };
        let mut details = HashMap::new();
        details.insert("total_overhead_diff".to_string(), score1 - score2);
        details.insert(
            "vtable_size_diff".to_string(),
            footprint1.vtable_size_bytes as f64 - footprint2.vtable_size_bytes as f64,
        );
        Self {
            winner,
            improvement_percentage,
            significance,
            details,
        }
    }
}
/// Memory footprint analysis
///
/// Provides detailed analysis of memory usage characteristics for a trait,
/// including vtable sizes, associated data overhead, and cache optimization opportunities.
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct MemoryFootprint {
    /// Size of virtual function table in bytes
    pub vtable_size_bytes: usize,
    /// Memory used by associated data in bytes
    pub associated_data_size: usize,
    /// Total memory overhead in bytes
    pub total_overhead: usize,
    /// Cache line alignment efficiency
    pub cache_alignment_efficiency: f64,
    /// Memory fragmentation risk
    pub fragmentation_risk: FragmentationRisk,
    /// Memory locality score
    pub locality_score: f64,
    /// Peak memory usage during operations
    pub peak_memory_usage: usize,
}
/// Cache pressure level enumeration
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub enum CachePressureLevel {
    #[default]
    Low,
    Medium,
    High,
    Critical,
}
#[derive(Debug)]
struct DummyTimer;
impl DummyTimer {
    fn start(self) -> DummyTimerHandle {
        DummyTimerHandle
    }
}
/// Memory access pattern analysis
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub enum MemoryAccessPattern {
    #[default]
    Sequential,
    Random,
    Strided {
        stride: usize,
    },
    Clustered {
        cluster_size: usize,
    },
    Mixed,
}
#[derive(Debug)]
struct DummyCounter;
impl DummyCounter {
    fn increment(&self) {}
}
/// Optimization priority levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationPriority {
    Low,
    Medium,
    High,
    Critical,
}
/// Implementation difficulty assessment
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ImplementationDifficulty {
    Trivial,
    Easy,
    Medium,
    Hard,
    Expert,
}
/// Individual benchmark result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkResult {
    /// Name of the benchmark
    pub name: String,
    /// Duration or measurement value
    pub value: f64,
    /// Unit of measurement
    pub unit: String,
    /// Standard deviation
    pub std_dev: f64,
    /// Number of samples
    pub samples: usize,
}
/// Recommendation choice enumeration
#[derive(Debug, Clone)]
pub enum RecommendationChoice {
    FirstTrait,
    SecondTrait,
    Equivalent,
}
/// Performance comparison between two traits
#[derive(Debug, Clone)]
pub struct PerformanceComparison {
    /// First trait analysis
    pub trait1_analysis: PerformanceAnalysis,
    /// Second trait analysis
    pub trait2_analysis: PerformanceAnalysis,
    /// Compilation impact comparison
    pub compilation_comparison: ComparisonResult,
    /// Runtime overhead comparison
    pub runtime_comparison: ComparisonResult,
    /// Memory footprint comparison
    pub memory_comparison: ComparisonResult,
    /// Overall recommendation
    pub recommendation: ComparisonRecommendation,
}
impl PerformanceComparison {
    fn new(analysis1: PerformanceAnalysis, analysis2: PerformanceAnalysis) -> Self {
        let compilation_comparison = ComparisonResult::compare_compilation(
            &analysis1.compilation_impact,
            &analysis2.compilation_impact,
        );
        let runtime_comparison = ComparisonResult::compare_runtime(
            &analysis1.runtime_overhead,
            &analysis2.runtime_overhead,
        );
        let memory_comparison = ComparisonResult::compare_memory(
            &analysis1.memory_footprint,
            &analysis2.memory_footprint,
        );
        let recommendation = ComparisonRecommendation::generate(
            &compilation_comparison,
            &runtime_comparison,
            &memory_comparison,
        );
        Self {
            trait1_analysis: analysis1,
            trait2_analysis: analysis2,
            compilation_comparison,
            runtime_comparison,
            memory_comparison,
            recommendation,
        }
    }
}
/// Runtime performance overhead analysis
///
/// Analyzes the runtime performance characteristics of a trait, including
/// virtual dispatch costs, stack frame sizes, and cache pressure.
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct RuntimeOverhead {
    /// Cost of virtual dispatch in nanoseconds
    pub virtual_dispatch_cost: usize,
    /// Stack frame size in bytes
    pub stack_frame_size: usize,
    /// Cache pressure level
    pub cache_pressure: CachePressureLevel,
    /// Inlining opportunities
    pub inlining_opportunities: usize,
    /// Branch prediction efficiency
    pub branch_prediction_efficiency: f64,
    /// SIMD optimization potential
    pub simd_potential: f64,
    /// Memory access patterns
    pub memory_access_patterns: MemoryAccessPattern,
}
/// Benchmarking engine for performance testing
#[derive(Debug)]
pub struct BenchmarkEngine {
    sample_count: usize,
}
impl BenchmarkEngine {
    pub fn new(sample_count: usize) -> Self {
        Self { sample_count }
    }
    pub fn run_benchmarks(&self, trait_info: &TraitInfo) -> Result<BenchmarkResults> {
        let mut compilation_benchmarks = Vec::new();
        let mut runtime_benchmarks = Vec::new();
        let mut memory_benchmarks = Vec::new();
        compilation_benchmarks.push(self.benchmark_compilation_time(trait_info)?);
        compilation_benchmarks.push(self.benchmark_monomorphization(trait_info)?);
        runtime_benchmarks.push(self.benchmark_virtual_dispatch(trait_info)?);
        runtime_benchmarks.push(self.benchmark_method_calls(trait_info)?);
        memory_benchmarks.push(self.benchmark_memory_allocation(trait_info)?);
        memory_benchmarks.push(self.benchmark_cache_performance(trait_info)?);
        let overall_score = self.calculate_overall_score(
            &compilation_benchmarks,
            &runtime_benchmarks,
            &memory_benchmarks,
        );
        Ok(BenchmarkResults {
            compilation_benchmarks,
            runtime_benchmarks,
            memory_benchmarks,
            overall_score,
        })
    }
    fn benchmark_compilation_time(&self, trait_info: &TraitInfo) -> Result<BenchmarkResult> {
        let base_time = trait_info.methods.len() as f64 * 1.5;
        let generic_overhead = trait_info.generics.len() as f64 * 2.0;
        let total_time = base_time + generic_overhead;
        Ok(BenchmarkResult {
            name: "Compilation Time".to_string(),
            value: total_time,
            unit: "ms".to_string(),
            std_dev: total_time * 0.1,
            samples: self.sample_count,
        })
    }
    fn benchmark_monomorphization(&self, trait_info: &TraitInfo) -> Result<BenchmarkResult> {
        let mono_cost =
            trait_info.generics.len() as f64 * 0.5 + trait_info.methods.len() as f64 * 0.2;
        Ok(BenchmarkResult {
            name: "Monomorphization Cost".to_string(),
            value: mono_cost,
            unit: "relative".to_string(),
            std_dev: mono_cost * 0.05,
            samples: self.sample_count,
        })
    }
    fn benchmark_virtual_dispatch(&self, trait_info: &TraitInfo) -> Result<BenchmarkResult> {
        let virtual_methods = trait_info
            .methods
            .iter()
            .filter(|method| method.required)
            .count() as f64;
        let dispatch_cost = virtual_methods * 2.5;
        Ok(BenchmarkResult {
            name: "Virtual Dispatch".to_string(),
            value: dispatch_cost,
            unit: "ns".to_string(),
            std_dev: dispatch_cost * 0.15,
            samples: self.sample_count,
        })
    }
    fn benchmark_method_calls(&self, trait_info: &TraitInfo) -> Result<BenchmarkResult> {
        let call_overhead = trait_info.methods.len() as f64 * 0.8;
        Ok(BenchmarkResult {
            name: "Method Call Overhead".to_string(),
            value: call_overhead,
            unit: "ns".to_string(),
            std_dev: call_overhead * 0.1,
            samples: self.sample_count,
        })
    }
    fn benchmark_memory_allocation(&self, trait_info: &TraitInfo) -> Result<BenchmarkResult> {
        let allocation_cost =
            (trait_info.methods.len() * 8 + trait_info.associated_types.len() * 16) as f64;
        Ok(BenchmarkResult {
            name: "Memory Allocation".to_string(),
            value: allocation_cost,
            unit: "bytes".to_string(),
            std_dev: allocation_cost * 0.05,
            samples: self.sample_count,
        })
    }
    fn benchmark_cache_performance(&self, trait_info: &TraitInfo) -> Result<BenchmarkResult> {
        let cache_efficiency = 1.0 / (1.0 + trait_info.methods.len() as f64 * 0.02);
        Ok(BenchmarkResult {
            name: "Cache Efficiency".to_string(),
            value: cache_efficiency,
            unit: "ratio".to_string(),
            std_dev: cache_efficiency * 0.08,
            samples: self.sample_count,
        })
    }
    fn calculate_overall_score(
        &self,
        compilation: &[BenchmarkResult],
        runtime: &[BenchmarkResult],
        memory: &[BenchmarkResult],
    ) -> f64 {
        let compilation_score = compilation
            .iter()
            .map(|b| 1.0 / (1.0 + b.value))
            .sum::<f64>()
            / compilation.len() as f64;
        let runtime_score =
            runtime.iter().map(|b| 1.0 / (1.0 + b.value)).sum::<f64>() / runtime.len() as f64;
        let memory_score = memory
            .iter()
            .map(|b| {
                if b.unit == "ratio" {
                    b.value
                } else {
                    1.0 / (1.0 + b.value)
                }
            })
            .sum::<f64>()
            / memory.len() as f64;
        runtime_score * 0.5 + compilation_score * 0.3 + memory_score * 0.2
    }
}
/// Memory fragmentation risk assessment
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub enum FragmentationRisk {
    #[default]
    Low,
    Medium,
    High,
    Critical,
}
/// Analysis metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnalysisMetadata {
    /// Version of the analyzer
    pub analyzer_version: String,
    /// Timestamp of analysis
    pub analysis_timestamp: chrono::DateTime<chrono::Utc>,
    /// Duration of analysis
    pub analysis_duration: Duration,
    /// Configuration used for analysis
    pub config_used: PerformanceConfig,
}
/// Runtime overhead analyzer
#[derive(Debug)]
pub struct RuntimeAnalyzer {
    #[allow(dead_code)]
    config: PerformanceConfig,
}
impl RuntimeAnalyzer {
    pub(crate) fn new(config: &PerformanceConfig) -> Self {
        Self {
            config: config.clone(),
        }
    }
    pub(crate) fn analyze_runtime_overhead(
        &self,
        trait_info: &TraitInfo,
    ) -> Result<RuntimeOverhead> {
        let virtual_dispatch_cost = self.calculate_virtual_dispatch_cost(trait_info);
        let stack_frame_size = self.calculate_stack_frame_size(trait_info);
        let cache_pressure = self.analyze_cache_pressure(trait_info);
        let inlining_opportunities = self.count_inlining_opportunities(trait_info);
        let branch_prediction_efficiency = self.calculate_branch_prediction_efficiency(trait_info);
        let simd_potential = self.analyze_simd_potential(trait_info);
        let memory_access_patterns = self.analyze_memory_access_patterns(trait_info);
        Ok(RuntimeOverhead {
            virtual_dispatch_cost,
            stack_frame_size,
            cache_pressure,
            inlining_opportunities,
            branch_prediction_efficiency,
            simd_potential,
            memory_access_patterns,
        })
    }
    fn calculate_virtual_dispatch_cost(&self, trait_info: &TraitInfo) -> usize {
        let virtual_methods = trait_info
            .methods
            .iter()
            .filter(|method| method.required)
            .count();
        let base_cost = virtual_methods * 3;
        let complexity_factor = if trait_info.generics.len() > 2 { 2 } else { 1 };
        base_cost * complexity_factor
    }
    fn calculate_stack_frame_size(&self, trait_info: &TraitInfo) -> usize {
        let base_frame = 64;
        let method_overhead = trait_info.methods.len() * 8;
        let generic_overhead = trait_info.generics.len() * 16;
        let associated_type_overhead = trait_info.associated_types.len() * 12;
        base_frame + method_overhead + generic_overhead + associated_type_overhead
    }
    fn analyze_cache_pressure(&self, trait_info: &TraitInfo) -> CachePressureLevel {
        let complexity_score = trait_info.methods.len()
            + trait_info.generics.len() * 2
            + trait_info.associated_types.len();
        match complexity_score {
            0..=5 => CachePressureLevel::Low,
            6..=15 => CachePressureLevel::Medium,
            16..=30 => CachePressureLevel::High,
            _ => CachePressureLevel::Critical,
        }
    }
    fn count_inlining_opportunities(&self, trait_info: &TraitInfo) -> usize {
        trait_info
            .methods
            .iter()
            .filter(|method| method.signature.len() < 100 && !method.signature.contains("async"))
            .count()
    }
    fn calculate_branch_prediction_efficiency(&self, trait_info: &TraitInfo) -> f64 {
        let virtual_method_count = trait_info
            .methods
            .iter()
            .filter(|method| method.required)
            .count() as f64;
        if virtual_method_count == 0.0 {
            1.0
        } else {
            (1.0_f64 / (1.0 + virtual_method_count * 0.1)).max(0.6)
        }
    }
    fn analyze_simd_potential(&self, trait_info: &TraitInfo) -> f64 {
        let numeric_methods = trait_info
            .methods
            .iter()
            .filter(|method| {
                let sig = &method.signature;
                sig.contains("f32")
                    || sig.contains("f64")
                    || sig.contains("i32")
                    || sig.contains("i64")
                    || sig.contains("Vec")
                    || sig.contains("Array")
            })
            .count() as f64;
        let total_methods = trait_info.methods.len() as f64;
        if total_methods == 0.0 {
            0.0
        } else {
            (numeric_methods / total_methods).min(1.0)
        }
    }
    fn analyze_memory_access_patterns(&self, trait_info: &TraitInfo) -> MemoryAccessPattern {
        let has_iterators = trait_info.methods.iter().any(|method| {
            method.signature.contains("iter") || method.signature.contains("Iterator")
        });
        let has_indexing = trait_info
            .methods
            .iter()
            .any(|method| method.signature.contains("index") || method.signature.contains("get"));
        let has_bulk_operations = trait_info
            .methods
            .iter()
            .any(|method| method.signature.contains("Vec") || method.signature.contains("slice"));
        match (has_iterators, has_indexing, has_bulk_operations) {
            (true, false, true) => MemoryAccessPattern::Sequential,
            (false, true, false) => MemoryAccessPattern::Random,
            (true, true, true) => MemoryAccessPattern::Mixed,
            (false, false, true) => MemoryAccessPattern::Clustered { cluster_size: 64 },
            _ => MemoryAccessPattern::Sequential,
        }
    }
}
/// Benchmark results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkResults {
    /// Compilation benchmarks
    pub compilation_benchmarks: Vec<BenchmarkResult>,
    /// Runtime benchmarks
    pub runtime_benchmarks: Vec<BenchmarkResult>,
    /// Memory benchmarks
    pub memory_benchmarks: Vec<BenchmarkResult>,
    /// Overall performance score
    pub overall_score: f64,
}
/// Comparison winner enumeration
#[derive(Debug, Clone)]
pub enum ComparisonWinner {
    First,
    Second,
    Tie,
}
#[derive(Debug)]
struct DummyMetrics;
impl DummyMetrics {
    fn timer(&self, _name: &str) -> DummyTimer {
        DummyTimer
    }
    fn counter(&self, _name: &str) -> DummyCounter {
        DummyCounter
    }
    fn histogram(&self, _name: &str) -> DummyHistogram {
        DummyHistogram
    }
}
/// Performance improvement areas
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PerformanceArea {
    CompileTime,
    Runtime,
    Memory,
    CodeSize,
    CacheEfficiency,
    ParallelizationEfficiency,
}
/// Memory footprint analyzer
#[derive(Debug)]
pub struct MemoryAnalyzer {
    #[allow(dead_code)]
    config: PerformanceConfig,
}
impl MemoryAnalyzer {
    pub fn new(config: &PerformanceConfig) -> Self {
        Self {
            config: config.clone(),
        }
    }
    pub fn analyze_memory_footprint(&self, trait_info: &TraitInfo) -> Result<MemoryFootprint> {
        let vtable_size_bytes = self.calculate_vtable_size(trait_info);
        let associated_data_size = self.calculate_associated_data_size(trait_info);
        let total_overhead = vtable_size_bytes + associated_data_size;
        let cache_alignment_efficiency = self.calculate_cache_alignment_efficiency(trait_info);
        let fragmentation_risk = self.assess_fragmentation_risk(trait_info);
        let locality_score = self.calculate_locality_score(trait_info);
        let peak_memory_usage = self.estimate_peak_memory_usage(trait_info);
        Ok(MemoryFootprint {
            vtable_size_bytes,
            associated_data_size,
            total_overhead,
            cache_alignment_efficiency,
            fragmentation_risk,
            locality_score,
            peak_memory_usage,
        })
    }
    fn calculate_vtable_size(&self, trait_info: &TraitInfo) -> usize {
        let virtual_methods = trait_info
            .methods
            .iter()
            .filter(|method| method.required)
            .count();
        virtual_methods * 8
    }
    fn calculate_associated_data_size(&self, trait_info: &TraitInfo) -> usize {
        let associated_type_overhead = trait_info.associated_types.len() * 16;
        let generic_metadata = trait_info.generics.len() * 8;
        associated_type_overhead + generic_metadata
    }
    fn calculate_cache_alignment_efficiency(&self, trait_info: &TraitInfo) -> f64 {
        let total_size = self.calculate_vtable_size(trait_info)
            + self.calculate_associated_data_size(trait_info);
        let cache_line_size = 64;
        let alignment_efficiency =
            1.0 - ((total_size % cache_line_size) as f64 / cache_line_size as f64);
        alignment_efficiency.max(0.1)
    }
    fn assess_fragmentation_risk(&self, trait_info: &TraitInfo) -> FragmentationRisk {
        let complexity_score = trait_info.methods.len()
            + trait_info.associated_types.len() * 2
            + trait_info.generics.len();
        let size_variability = trait_info
            .methods
            .iter()
            .map(|method| method.signature.len())
            .collect::<Vec<_>>();
        let has_variable_sizes = size_variability.iter().max().unwrap_or(&0)
            - size_variability.iter().min().unwrap_or(&0)
            > 100;
        match (complexity_score, has_variable_sizes) {
            (0..=10, false) => FragmentationRisk::Low,
            (11..=25, false) | (0..=15, true) => FragmentationRisk::Medium,
            (26..=40, _) | (16..=30, true) => FragmentationRisk::High,
            _ => FragmentationRisk::Critical,
        }
    }
    fn calculate_locality_score(&self, trait_info: &TraitInfo) -> f64 {
        let method_count = trait_info.methods.len() as f64;
        let related_methods = trait_info
            .methods
            .iter()
            .filter(|method| {
                method.name.contains("get")
                    || method.name.contains("set")
                    || method.name.contains("iter")
                    || method.name.contains("len")
            })
            .count() as f64;
        if method_count == 0.0 {
            1.0
        } else {
            (related_methods / method_count).max(0.1)
        }
    }
    fn estimate_peak_memory_usage(&self, trait_info: &TraitInfo) -> usize {
        let base_usage = self.calculate_vtable_size(trait_info)
            + self.calculate_associated_data_size(trait_info);
        let operation_overhead = trait_info.methods.len() * 256;
        let generic_instantiation_overhead = trait_info.generics.len() * 1024;
        base_usage + operation_overhead + generic_instantiation_overhead
    }
}
/// Optimization engine for generating performance recommendations
#[derive(Debug)]
pub struct OptimizationEngine {
    #[allow(dead_code)]
    config: PerformanceConfig,
}
impl OptimizationEngine {
    pub fn new(config: &PerformanceConfig) -> Self {
        Self {
            config: config.clone(),
        }
    }
    pub fn generate_optimization_hints(
        &self,
        trait_info: &TraitInfo,
        compilation_impact: &CompilationImpact,
        runtime_overhead: &RuntimeOverhead,
        memory_footprint: &MemoryFootprint,
    ) -> Result<Vec<OptimizationHint>> {
        let mut hints = Vec::new();
        hints.extend(self.generate_compilation_hints(trait_info, compilation_impact));
        hints.extend(self.generate_runtime_hints(trait_info, runtime_overhead));
        hints.extend(self.generate_memory_hints(trait_info, memory_footprint));
        hints.extend(self.generate_architecture_hints(trait_info));
        hints.sort_by(|a, b| {
            use OptimizationPriority::*;
            let priority_order = |p: &OptimizationPriority| match p {
                Critical => 0,
                High => 1,
                Medium => 2,
                Low => 3,
            };
            priority_order(&a.priority).cmp(&priority_order(&b.priority))
        });
        Ok(hints)
    }
    fn generate_compilation_hints(
        &self,
        trait_info: &TraitInfo,
        compilation_impact: &CompilationImpact,
    ) -> Vec<OptimizationHint> {
        let mut hints = Vec::new();
        if compilation_impact.estimated_compile_time_ms > 5000 {
            hints
                .push(OptimizationHint {
                    category: OptimizationCategory::Compilation,
                    priority: OptimizationPriority::High,
                    description: "High compilation time detected. Consider splitting the trait into smaller, more focused traits."
                        .to_string(),
                    estimated_impact: PerformanceImpact {
                        improvement_percentage: 30.0,
                        confidence_level: 0.8,
                        impact_areas: vec![PerformanceArea::CompileTime],
                    },
                    implementation_difficulty: ImplementationDifficulty::Medium,
                    code_suggestions: vec![
                        "Split large traits into composition of smaller traits"
                        .to_string(),
                        "Use associated types instead of generic parameters where possible"
                        .to_string(),
                        "Consider using trait aliases for common combinations"
                        .to_string(),
                    ],
                });
        }
        if trait_info.generics.len() > 4 {
            hints.push(OptimizationHint {
                category: OptimizationCategory::Compilation,
                priority: OptimizationPriority::Medium,
                description:
                    "High number of generic parameters may impact compilation time and code bloat."
                        .to_string(),
                estimated_impact: PerformanceImpact {
                    improvement_percentage: 20.0,
                    confidence_level: 0.7,
                    impact_areas: vec![PerformanceArea::CompileTime, PerformanceArea::CodeSize],
                },
                implementation_difficulty: ImplementationDifficulty::Hard,
                code_suggestions: vec![
                    "Combine related generic parameters into a single trait bound".to_string(),
                    "Use associated types for output types".to_string(),
                    "Consider using trait objects for some generic parameters".to_string(),
                ],
            });
        }
        hints
    }
    fn generate_runtime_hints(
        &self,
        trait_info: &TraitInfo,
        runtime_overhead: &RuntimeOverhead,
    ) -> Vec<OptimizationHint> {
        let mut hints = Vec::new();
        if runtime_overhead.virtual_dispatch_cost > 50 {
            hints
                .push(OptimizationHint {
                    category: OptimizationCategory::Runtime,
                    priority: OptimizationPriority::High,
                    description: "High virtual dispatch overhead. Consider using generics instead of trait objects for hot paths."
                        .to_string(),
                    estimated_impact: PerformanceImpact {
                        improvement_percentage: 25.0,
                        confidence_level: 0.9,
                        impact_areas: vec![PerformanceArea::Runtime],
                    },
                    implementation_difficulty: ImplementationDifficulty::Medium,
                    code_suggestions: vec![
                        "Use generic parameters instead of trait objects in performance-critical code"
                        .to_string(), "Consider enum dispatch for known implementations"
                        .to_string(), "Use #[inline] attributes on small trait methods"
                        .to_string(),
                    ],
                });
        }
        if runtime_overhead.inlining_opportunities > trait_info.methods.len() / 2 {
            hints.push(OptimizationHint {
                category: OptimizationCategory::Runtime,
                priority: OptimizationPriority::Medium,
                description:
                    "Many methods are suitable for inlining. Consider adding inline attributes."
                        .to_string(),
                estimated_impact: PerformanceImpact {
                    improvement_percentage: 15.0,
                    confidence_level: 0.7,
                    impact_areas: vec![PerformanceArea::Runtime],
                },
                implementation_difficulty: ImplementationDifficulty::Easy,
                code_suggestions: vec![
                    "Add #[inline] to small, frequently called methods".to_string(),
                    "Use #[inline(always)] sparingly for critical paths".to_string(),
                    "Profile to verify inlining benefits".to_string(),
                ],
            });
        }
        if runtime_overhead.simd_potential > 0.7 {
            hints
                .push(OptimizationHint {
                    category: OptimizationCategory::Vectorization,
                    priority: OptimizationPriority::Medium,
                    description: "High SIMD optimization potential detected. Consider vectorized implementations."
                        .to_string(),
                    estimated_impact: PerformanceImpact {
                        improvement_percentage: 40.0,
                        confidence_level: 0.6,
                        impact_areas: vec![PerformanceArea::Runtime],
                    },
                    implementation_difficulty: ImplementationDifficulty::Hard,
                    code_suggestions: vec![
                        "Use SIMD intrinsics for bulk numeric operations".to_string(),
                        "Consider using portable SIMD crates like wide or packed_simd"
                        .to_string(), "Implement vectorized versions of core algorithms"
                        .to_string(),
                    ],
                });
        }
        hints
    }
    fn generate_memory_hints(
        &self,
        _trait_info: &TraitInfo,
        memory_footprint: &MemoryFootprint,
    ) -> Vec<OptimizationHint> {
        let mut hints = Vec::new();
        if memory_footprint.cache_alignment_efficiency < 0.7 {
            hints.push(OptimizationHint {
                category: OptimizationCategory::Cache,
                priority: OptimizationPriority::Medium,
                description: "Poor cache alignment detected. Consider optimizing data layout."
                    .to_string(),
                estimated_impact: PerformanceImpact {
                    improvement_percentage: 20.0,
                    confidence_level: 0.8,
                    impact_areas: vec![PerformanceArea::CacheEfficiency, PerformanceArea::Runtime],
                },
                implementation_difficulty: ImplementationDifficulty::Medium,
                code_suggestions: vec![
                    "Use #[repr(align(64))] for cache line alignment".to_string(),
                    "Group frequently accessed fields together".to_string(),
                    "Consider using padding to align to cache boundaries".to_string(),
                ],
            });
        }
        if matches!(
            memory_footprint.fragmentation_risk,
            FragmentationRisk::High | FragmentationRisk::Critical
        ) {
            hints.push(OptimizationHint {
                category: OptimizationCategory::Memory,
                priority: OptimizationPriority::High,
                description:
                    "High memory fragmentation risk. Consider memory pool allocation strategies."
                        .to_string(),
                estimated_impact: PerformanceImpact {
                    improvement_percentage: 25.0,
                    confidence_level: 0.7,
                    impact_areas: vec![PerformanceArea::Memory],
                },
                implementation_difficulty: ImplementationDifficulty::Hard,
                code_suggestions: vec![
                    "Use custom allocators for large data structures".to_string(),
                    "Implement object pooling for frequently allocated objects".to_string(),
                    "Consider using arena allocation for related objects".to_string(),
                ],
            });
        }
        if memory_footprint.locality_score < 0.5 {
            hints.push(OptimizationHint {
                category: OptimizationCategory::Memory,
                priority: OptimizationPriority::Medium,
                description:
                    "Poor memory locality detected. Consider data structure reorganization."
                        .to_string(),
                estimated_impact: PerformanceImpact {
                    improvement_percentage: 18.0,
                    confidence_level: 0.6,
                    impact_areas: vec![PerformanceArea::CacheEfficiency, PerformanceArea::Memory],
                },
                implementation_difficulty: ImplementationDifficulty::Medium,
                code_suggestions: vec![
                    "Use structure-of-arrays instead of array-of-structures".to_string(),
                    "Group related data fields together".to_string(),
                    "Consider using data-oriented design principles".to_string(),
                ],
            });
        }
        hints
    }
    fn generate_architecture_hints(&self, trait_info: &TraitInfo) -> Vec<OptimizationHint> {
        let mut hints = Vec::new();
        if trait_info.methods.len() > 20 {
            hints.push(OptimizationHint {
                category: OptimizationCategory::Architecture,
                priority: OptimizationPriority::High,
                description:
                    "Large trait detected. Consider splitting into smaller, cohesive traits."
                        .to_string(),
                estimated_impact: PerformanceImpact {
                    improvement_percentage: 35.0,
                    confidence_level: 0.9,
                    impact_areas: vec![PerformanceArea::CompileTime, PerformanceArea::Runtime],
                },
                implementation_difficulty: ImplementationDifficulty::Medium,
                code_suggestions: vec![
                    "Apply Single Responsibility Principle to trait design".to_string(),
                    "Create trait hierarchies with specific capabilities".to_string(),
                    "Use composition instead of large monolithic traits".to_string(),
                ],
            });
        }
        if trait_info.associated_types.len() > 5 {
            hints.push(OptimizationHint {
                category: OptimizationCategory::Architecture,
                priority: OptimizationPriority::Medium,
                description: "Many associated types may indicate overly complex trait design."
                    .to_string(),
                estimated_impact: PerformanceImpact {
                    improvement_percentage: 15.0,
                    confidence_level: 0.6,
                    impact_areas: vec![PerformanceArea::CompileTime],
                },
                implementation_difficulty: ImplementationDifficulty::Medium,
                code_suggestions: vec![
                    "Consider using generic parameters for frequently used types".to_string(),
                    "Group related associated types into separate traits".to_string(),
                    "Use type aliases to simplify complex associated types".to_string(),
                ],
            });
        }
        hints
    }
}
/// Performance impact estimation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceImpact {
    /// Estimated performance improvement percentage
    pub improvement_percentage: f64,
    /// Confidence level in the estimation
    pub confidence_level: f64,
    /// Areas of impact
    pub impact_areas: Vec<PerformanceArea>,
}
/// Performance analysis results
///
/// Contains comprehensive performance metrics and analysis results for a trait,
/// including compilation impact, runtime overhead, memory footprint, and
/// optimization recommendations.
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct PerformanceAnalysis {
    /// Impact on compilation performance
    pub compilation_impact: CompilationImpact,
    /// Runtime performance characteristics
    pub runtime_overhead: RuntimeOverhead,
    /// Memory usage analysis
    pub memory_footprint: MemoryFootprint,
    /// Performance optimization suggestions
    pub optimization_hints: Vec<OptimizationHint>,
    /// Benchmark results if available
    pub benchmark_results: Option<BenchmarkResults>,
    /// Analysis metadata
    pub analysis_metadata: AnalysisMetadata,
}
/// Compilation performance impact analysis
///
/// Provides detailed metrics about how a trait affects compilation performance,
/// including compile time estimation, monomorphization costs, and binary size impact.
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct CompilationImpact {
    /// Estimated additional compile time in milliseconds
    pub estimated_compile_time_ms: usize,
    /// Cost of monomorphization for generic traits
    pub monomorphization_cost: usize,
    /// Impact on binary size in bytes
    pub code_size_impact: usize,
    /// Number of generic instantiations
    pub generic_instantiations: usize,
    /// Dependency compilation chain length
    pub dependency_chain_length: usize,
    /// Incremental compilation efficiency
    pub incremental_efficiency: f64,
    /// Parallelization potential
    pub parallelization_factor: f64,
}
/// Configuration for performance analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceConfig {
    /// Enable advanced compilation analysis
    pub advanced_analysis: bool,
    /// Enable optimization hint generation
    pub optimization_hints: bool,
    /// Enable benchmarking integration
    pub benchmarking: bool,
    /// Enable cross-platform analysis
    pub cross_platform: bool,
    /// Enable regression detection
    pub regression_detection: bool,
    /// Sample size for benchmarking
    pub benchmark_samples: usize,
    /// Timeout for analysis operations
    pub analysis_timeout: Duration,
}
impl PerformanceConfig {
    /// Create a new performance configuration
    pub fn new() -> Self {
        Self::default()
    }
    /// Enable advanced analysis features
    pub fn with_advanced_analysis(mut self, enabled: bool) -> Self {
        self.advanced_analysis = enabled;
        self
    }
    /// Enable optimization hint generation
    pub fn with_optimization_hints(mut self, enabled: bool) -> Self {
        self.optimization_hints = enabled;
        self
    }
    /// Enable benchmarking integration
    pub fn with_benchmarking(mut self, enabled: bool) -> Self {
        self.benchmarking = enabled;
        self
    }
    /// Set benchmark sample size
    pub fn with_benchmark_samples(mut self, samples: usize) -> Self {
        self.benchmark_samples = samples;
        self
    }
    /// Set analysis timeout
    pub fn with_analysis_timeout(mut self, timeout: Duration) -> Self {
        self.analysis_timeout = timeout;
        self
    }
}
#[derive(Debug)]
struct DummyTimerHandle;
/// Optimization category enumeration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationCategory {
    Compilation,
    Runtime,
    Memory,
    Architecture,
    Algorithm,
    Cache,
    Parallelization,
    Vectorization,
}
/// Comparison significance levels
#[derive(Debug, Clone)]
pub enum ComparisonSignificance {
    Low,
    Medium,
    High,
}
