use crate::error::{Result, SklearsError};

use super::security_types::*;

// SciRS2 compliance - use scirs2-autograd for ndarray
use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2, Axis};
use scirs2_core::ndarray_ext::{matrix, stats};

#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};

use chrono::{DateTime, Utc};
use std::collections::HashMap;
use std::time::Duration;

/// Advanced security risk assessor with multiple risk models and sophisticated
/// risk calculation algorithms.
///
/// # Features
///
/// - Multiple risk assessment models (Quantitative, Qualitative, Hybrid)
/// - Bayesian risk analysis
/// - Monte Carlo risk simulation
/// - Historical risk trend analysis
/// - Risk correlation analysis
/// - Custom risk factor weighting
/// - Ensemble risk modeling
/// - Confidence interval calculation
/// - Risk factor identification
/// - Trend-based risk prediction
///
/// # Example
///
/// ```rust,ignore
/// use sklears_core::trait_explorer::security_analysis::{
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct SecurityRiskAssessor {
    risk_models: HashMap<String, RiskAssessmentModel>,

    historical_data: Vec<HistoricalRiskData>,

    correlation_matrix: Option<Array2<f64>>,

    factor_weights: HashMap<String, f64>,

    bayesian_parameters: BayesianRiskParameters,

    config: RiskAssessmentConfig,

    monte_carlo_config: MonteCarloConfig,

    risk_thresholds: RiskThresholds,
}

impl SecurityRiskAssessor {
    /// Create a new security risk assessor.
    pub fn new() -> Self {
        let mut assessor = Self {
            risk_models: HashMap::new(),
            historical_data: Vec::new(),
            correlation_matrix: None,
            factor_weights: HashMap::new(),
            bayesian_parameters: BayesianRiskParameters::default(),
            config: RiskAssessmentConfig::default(),
            monte_carlo_config: MonteCarloConfig::default(),
            risk_thresholds: RiskThresholds::default(),
        };

        assessor.initialize_default_models();
        assessor.initialize_default_weights();
        assessor
    }

    /// Create a new security risk assessor with custom configuration.
    pub fn with_config(config: RiskAssessmentConfig) -> Self {
        let mut assessor = Self::new();
        assessor.config = config;
        assessor
    }

    /// Add a custom risk assessment model.
    pub fn add_risk_model(&mut self, name: String, model: RiskAssessmentModel) {
        self.risk_models.insert(name, model);
    }

    /// Remove a risk assessment model.
    pub fn remove_risk_model(&mut self, name: &str) -> Option<RiskAssessmentModel> {
        self.risk_models.remove(name)
    }

    /// Get available risk model names.
    pub fn get_risk_model_names(&self) -> Vec<String> {
        self.risk_models.keys().cloned().collect()
    }

    /// Perform comprehensive risk assessment using multiple models.
    pub fn assess_comprehensive_risk(
        &self,
        context: &TraitUsageContext,
    ) -> Result<RiskAssessmentResult> {
        let mut model_results = HashMap::new();

        // Run all risk models
        for (name, model) in &self.risk_models {
            let result = model.assess_risk(context)?;
            model_results.insert(name.clone(), result);
        }

        // Combine results using ensemble methods
        let combined_score = self.combine_model_results(&model_results)?;

        // Perform Bayesian adjustment
        let bayesian_adjusted_score = self.apply_bayesian_adjustment(combined_score, context)?;

        // Calculate confidence intervals
        let confidence_intervals = self.calculate_confidence_intervals(&model_results)?;

        // Perform Monte Carlo simulation if enabled
        let monte_carlo_results = if self.config.enable_monte_carlo {
            Some(self.perform_monte_carlo_simulation(context)?)
        } else {
            None
        };

        // Identify key risk factors
        let risk_factors = self.identify_key_risk_factors(context)?;

        // Generate risk recommendations
        let recommendations = self.generate_risk_recommendations(bayesian_adjusted_score)?;

        // Calculate risk level
        let risk_level = self.calculate_risk_level(bayesian_adjusted_score);

        // Perform trend analysis if historical data is available
        let trend_analysis = if !self.historical_data.is_empty() {
            Some(self.analyze_risk_trends(context)?)
        } else {
            None
        };

        Ok(RiskAssessmentResult {
            overall_risk_score: bayesian_adjusted_score,
            risk_level,
            model_results,
            confidence_intervals,
            risk_factors,
            recommendations,
            monte_carlo_results,
            trend_analysis,
            assessment_timestamp: Utc::now(),
        })
    }

    /// Perform quick risk assessment using default model.
    pub fn assess_quick_risk(&self, context: &TraitUsageContext) -> Result<f64> {
        if let Some(default_model) = self.risk_models.get("quantitative") {
            default_model.assess_risk(context)
        } else if let Some(model) = self.risk_models.values().next() {
            model.assess_risk(context)
        } else {
            Ok(0.0)
        }
    }

    /// Add historical risk data for trend analysis.
    pub fn add_historical_data(&mut self, data: HistoricalRiskData) {
        self.historical_data.push(data);

        // Keep only recent data based on configuration
        if let Some(retention_period) = self.config.historical_data_retention {
            let cutoff_date = Utc::now() - retention_period;
            self.historical_data.retain(|data| data.timestamp > cutoff_date);
        }

        // Sort by timestamp
        self.historical_data.sort_by(|a, b| a.timestamp.cmp(&b.timestamp));
    }

    /// Update risk factor weights.
    pub fn update_factor_weights(&mut self, weights: HashMap<String, f64>) {
        self.factor_weights.extend(weights);
    }

    /// Set custom Bayesian parameters.
    pub fn set_bayesian_parameters(&mut self, parameters: BayesianRiskParameters) {
        self.bayesian_parameters = parameters;
    }

    /// Initialize default risk assessment models.
    fn initialize_default_models(&mut self) {
        self.risk_models.insert(
            "quantitative".to_string(),
            RiskAssessmentModel::Quantitative(QuantitativeRiskModel::default()),
        );

        self.risk_models.insert(
            "qualitative".to_string(),
            RiskAssessmentModel::Qualitative(QualitativeRiskModel::default()),
        );

        self.risk_models.insert(
            "hybrid".to_string(),
            RiskAssessmentModel::Hybrid(HybridRiskModel::default()),
        );

        // Add specialized models
        self.risk_models.insert(
            "severity_weighted".to_string(),
            RiskAssessmentModel::SeverityWeighted(SeverityWeightedModel::default()),
        );

        self.risk_models.insert(
            "pattern_based".to_string(),
            RiskAssessmentModel::PatternBased(PatternBasedModel::default()),
        );
    }

    /// Initialize default factor weights.
    fn initialize_default_weights(&mut self) {
        self.factor_weights.insert("sensitive_data".to_string(), 2.0);
        self.factor_weights.insert("elevated_privileges".to_string(), 1.8);
        self.factor_weights.insert("input_validation".to_string(), 2.5);
        self.factor_weights.insert("access_controls".to_string(), 2.0);
        self.factor_weights.insert("encryption".to_string(), 1.5);
        self.factor_weights.insert("audit_logging".to_string(), 1.2);
        self.factor_weights.insert("rate_limiting".to_string(), 1.3);
        self.factor_weights.insert("timing_dependencies".to_string(), 1.6);
        self.factor_weights.insert("cryptographic_operations".to_string(), 1.7);
        self.factor_weights.insert("unsafe_operations".to_string(), 2.2);
    }

    /// Combine results from multiple risk models using ensemble methods.
    fn combine_model_results(&self, results: &HashMap<String, f64>) -> Result<f64> {
        if results.is_empty() {
            return Ok(0.0);
        }

        match self.config.ensemble_method {
            EnsembleMethod::WeightedAverage => {
                let mut weighted_sum = 0.0;
                let mut total_weight = 0.0;

                for (model_name, score) in results {
                    let weight = self.get_model_weight(model_name);
                    weighted_sum += score * weight;
                    total_weight += weight;
                }

                Ok(if total_weight > 0.0 {
                    weighted_sum / total_weight
                } else {
                    0.0
                })
            }
            EnsembleMethod::SimpleAverage => {
                let sum: f64 = results.values().sum();
                Ok(sum / results.len() as f64)
            }
            EnsembleMethod::Median => {
                let mut scores: Vec<f64> = results.values().cloned().collect();
                scores.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
                let len = scores.len();
                if len % 2 == 0 {
                    Ok((scores[len / 2 - 1] + scores[len / 2]) / 2.0)
                } else {
                    Ok(scores[len / 2])
                }
            }
            EnsembleMethod::Maximum => {
                Ok(results.values().cloned().fold(0.0f64, f64::max))
            }
            EnsembleMethod::Minimum => {
                Ok(results.values().cloned().fold(10.0f64, f64::min))
            }
        }
    }

    /// Apply Bayesian adjustment to risk score based on historical data.
    fn apply_bayesian_adjustment(
        &self,
        initial_score: f64,
        context: &TraitUsageContext,
    ) -> Result<f64> {
        if !self.config.enable_bayesian_adjustment {
            return Ok(initial_score);
        }

        let prior_mean = self.bayesian_parameters.prior_mean;
        let prior_variance = self.bayesian_parameters.prior_variance;
        let observation_variance = self.bayesian_parameters.observation_variance;

        // Calculate posterior mean using Bayesian updating
        let precision_prior = 1.0 / prior_variance;
        let precision_observation = 1.0 / observation_variance;

        let posterior_mean = (precision_prior * prior_mean + precision_observation * initial_score)
            / (precision_prior + precision_observation);

        // Apply historical data influence if available
        let historical_influence = self.calculate_historical_influence(context)?;
        let final_score = posterior_mean * (1.0 - historical_influence) + initial_score * historical_influence;

        Ok(final_score.clamp(0.0, 10.0))
    }

    /// Calculate historical influence factor.
    fn calculate_historical_influence(&self, context: &TraitUsageContext) -> Result<f64> {
        if self.historical_data.is_empty() {
            return Ok(0.0);
        }

        // Simple implementation: use recency and similarity of historical data
        let context_hash = self.generate_context_hash(context);
        let mut similarity_weights = 0.0;
        let mut total_weights = 0.0;

        let now = Utc::now();
        for data in &self.historical_data {
            // Calculate time-based weight (more recent = higher weight)
            let age = now.signed_duration_since(data.timestamp);
            let time_weight = (-age.num_days() as f64 / 30.0).exp(); // Decay over 30 days

            // Calculate similarity weight (simplified)
            let similarity = if data.context_hash == context_hash { 1.0 } else { 0.3 };

            let weight = time_weight * similarity;
            similarity_weights += weight;
            total_weights += weight;
        }

        Ok(if total_weights > 0.0 {
            (similarity_weights / total_weights).min(0.7) // Cap influence at 70%
        } else {
            0.0
        })
    }

    /// Calculate confidence intervals for risk assessment.
    fn calculate_confidence_intervals(
        &self,
        results: &HashMap<String, f64>,
    ) -> Result<ConfidenceIntervals> {
        let scores: Vec<f64> = results.values().cloned().collect();
        if scores.is_empty() {
            return Ok(ConfidenceIntervals {
                lower_95: 0.0,
                upper_95: 0.0,
                lower_99: 0.0,
                upper_99: 0.0,
            });
        }

        // Calculate basic statistics
        let mean = scores.iter().sum::<f64>() / scores.len() as f64;
        let variance = scores.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / scores.len() as f64;
        let std_dev = variance.sqrt();

        // Calculate confidence intervals (assuming normal distribution)
        Ok(ConfidenceIntervals {
            lower_95: (mean - 1.96 * std_dev).max(0.0),
            upper_95: (mean + 1.96 * std_dev).min(10.0),
            lower_99: (mean - 2.58 * std_dev).max(0.0),
            upper_99: (mean + 2.58 * std_dev).min(10.0),
        })
    }

    /// Perform Monte Carlo simulation for risk assessment.
    fn perform_monte_carlo_simulation(&self, context: &TraitUsageContext) -> Result<MonteCarloResults> {
        let mut results = Vec::new();
        let num_simulations = self.monte_carlo_config.num_simulations;

        for _ in 0..num_simulations {
            // Add random variation to context parameters
            let perturbed_context = self.perturb_context(context);

            // Run risk assessment on perturbed context
            let score = self.assess_quick_risk(&perturbed_context)?;
            results.push(score);
        }

        // Calculate statistics
        results.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
        let mean = results.iter().sum::<f64>() / results.len() as f64;
        let variance = results.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / results.len() as f64;
        let std_dev = variance.sqrt();

        let percentile_5 = results[(0.05 * results.len() as f64) as usize];
        let percentile_95 = results[(0.95 * results.len() as f64) as usize];

        Ok(MonteCarloResults {
            mean_score: mean,
            std_deviation: std_dev,
            percentile_5,
            percentile_95,
            num_simulations,
            risk_distribution: if self.monte_carlo_config.store_distribution {
                Some(results)
            } else {
                None
            },
        })
    }

    /// Perturb context for Monte Carlo simulation.
    fn perturb_context(&self, context: &TraitUsageContext) -> TraitUsageContext {
        // Simple perturbation - in practice this would be more sophisticated
        let mut perturbed = context.clone();

        // Randomly toggle some boolean properties with low probability
        if rand::random::<f64>() < self.monte_carlo_config.perturbation_probability {
            perturbed.has_input_validation = !perturbed.has_input_validation;
        }
        if rand::random::<f64>() < self.monte_carlo_config.perturbation_probability {
            perturbed.has_access_controls = !perturbed.has_access_controls;
        }
        if rand::random::<f64>() < self.monte_carlo_config.perturbation_probability {
            perturbed.has_encryption = !perturbed.has_encryption;
        }

        perturbed
    }

    /// Identify key risk factors contributing to overall risk.
    fn identify_key_risk_factors(&self, context: &TraitUsageContext) -> Result<Vec<RiskFactor>> {
        let mut factors = Vec::new();

        // Analyze each potential risk factor
        let base_score = self.assess_quick_risk(context)?;

        // Test impact of each factor by temporarily modifying context
        factors.extend(self.analyze_boolean_factors(context, base_score)?);
        factors.extend(self.analyze_trait_factors(context, base_score)?);
        factors.extend(self.analyze_combination_factors(context, base_score)?);

        // Sort by impact (highest first)
        factors.sort_by(|a, b| b.impact_score.partial_cmp(&a.impact_score).unwrap_or(std::cmp::Ordering::Equal));

        // Keep only significant factors
        factors.retain(|f| f.impact_score > self.config.risk_factor_threshold);

        Ok(factors)
    }

    /// Analyze boolean risk factors.
    fn analyze_boolean_factors(&self, context: &TraitUsageContext, base_score: f64) -> Result<Vec<RiskFactor>> {
        let mut factors = Vec::new();

        // Test each boolean property
        let boolean_tests = vec![
            ("handles_sensitive_data", context.handles_sensitive_data),
            ("requires_elevated_privileges", context.requires_elevated_privileges),
            ("has_input_validation", context.has_input_validation),
            ("has_access_controls", context.has_access_controls),
            ("has_encryption", context.has_encryption),
            ("has_audit_logging", context.has_audit_logging),
            ("has_rate_limiting", context.has_rate_limiting),
            ("has_timing_dependencies", context.has_timing_dependencies),
            ("has_cryptographic_operations", context.has_cryptographic_operations),
            ("has_unsafe_operations", context.has_unsafe_operations),
        ];

        for (factor_name, current_value) in boolean_tests {
            let mut test_context = context.clone();

            // Toggle the value and test impact
            match factor_name {
                "handles_sensitive_data" => test_context.handles_sensitive_data = !current_value,
                "requires_elevated_privileges" => test_context.requires_elevated_privileges = !current_value,
                "has_input_validation" => test_context.has_input_validation = !current_value,
                "has_access_controls" => test_context.has_access_controls = !current_value,
                "has_encryption" => test_context.has_encryption = !current_value,
                "has_audit_logging" => test_context.has_audit_logging = !current_value,
                "has_rate_limiting" => test_context.has_rate_limiting = !current_value,
                "has_timing_dependencies" => test_context.has_timing_dependencies = !current_value,
                "has_cryptographic_operations" => test_context.has_cryptographic_operations = !current_value,
                "has_unsafe_operations" => test_context.has_unsafe_operations = !current_value,
                _ => continue,
            }

            let test_score = self.assess_quick_risk(&test_context)?;
            let impact = (test_score - base_score).abs();

            if impact > self.config.risk_factor_threshold {
                factors.push(RiskFactor {
                    name: factor_name.to_string(),
                    factor_type: RiskFactorType::Boolean,
                    current_value: current_value.to_string(),
                    impact_score: impact,
                    risk_contribution: if current_value { impact } else { -impact },
                    mitigation_priority: self.calculate_mitigation_priority(impact),
                    description: self.get_factor_description(factor_name),
                });
            }
        }

        Ok(factors)
    }

    /// Analyze trait-based risk factors.
    fn analyze_trait_factors(&self, context: &TraitUsageContext, base_score: f64) -> Result<Vec<RiskFactor>> {
        let mut factors = Vec::new();

        for trait_name in &context.traits {
            let mut test_context = context.clone();
            test_context.traits.retain(|t| t != trait_name);

            let test_score = self.assess_quick_risk(&test_context)?;
            let impact = base_score - test_score;

            if impact > self.config.risk_factor_threshold {
                factors.push(RiskFactor {
                    name: format!("trait_{}", trait_name),
                    factor_type: RiskFactorType::Trait,
                    current_value: trait_name.clone(),
                    impact_score: impact,
                    risk_contribution: impact,
                    mitigation_priority: self.calculate_mitigation_priority(impact),
                    description: format!("Risk contribution from trait: {}", trait_name),
                });
            }
        }

        Ok(factors)
    }

    /// Analyze combination risk factors.
    fn analyze_combination_factors(&self, context: &TraitUsageContext, _base_score: f64) -> Result<Vec<RiskFactor>> {
        let mut factors = Vec::new();

        // Check for known risky trait combinations
        let risky_combinations = vec![
            (vec!["Serialize", "Clone"], "Serialization with cloning"),
            (vec!["Send", "Sync", "Clone"], "Concurrent cloning"),
            (vec!["Serialize", "Debug"], "Serialization with debug output"),
        ];

        for (combination, description) in risky_combinations {
            if combination.iter().all(|trait_name| {
                context.traits.contains(&trait_name.to_string())
            }) {
                let impact = self.calculate_combination_impact(&combination, context)?;

                factors.push(RiskFactor {
                    name: format!("combination_{}", combination.join("_")),
                    factor_type: RiskFactorType::Combination,
                    current_value: combination.join("+"),
                    impact_score: impact,
                    risk_contribution: impact,
                    mitigation_priority: self.calculate_mitigation_priority(impact),
                    description: description.to_string(),
                });
            }
        }

        Ok(factors)
    }

    /// Calculate combination impact.
    fn calculate_combination_impact(&self, combination: &[&str], _context: &TraitUsageContext) -> Result<f64> {
        // Simplified combination impact calculation
        match combination {
            combo if combo.contains(&"Serialize") && combo.contains(&"Clone") => Ok(1.5),
            combo if combo.contains(&"Send") && combo.contains(&"Sync") && combo.contains(&"Clone") => Ok(1.2),
            combo if combo.contains(&"Serialize") && combo.contains(&"Debug") => Ok(0.8),
            _ => Ok(0.5),
        }
    }

    /// Analyze risk trends based on historical data.
    fn analyze_risk_trends(&self, context: &TraitUsageContext) -> Result<RiskTrendAnalysis> {
        if self.historical_data.len() < 2 {
            return Ok(RiskTrendAnalysis {
                trend_direction: TrendDirection::Stable,
                trend_strength: 0.0,
                confidence: 0.0,
                forecast: None,
            });
        }

        let context_hash = self.generate_context_hash(context);
        let relevant_data: Vec<&HistoricalRiskData> = self.historical_data
            .iter()
            .filter(|data| data.context_hash == context_hash)
            .collect();

        if relevant_data.len() < 2 {
            return self.analyze_general_trends();
        }

        // Calculate trend slope using linear regression
        let n = relevant_data.len() as f64;
        let x_values: Vec<f64> = (0..relevant_data.len()).map(|i| i as f64).collect();
        let y_values: Vec<f64> = relevant_data.iter().map(|data| data.risk_score).collect();

        let x_mean = x_values.iter().sum::<f64>() / n;
        let y_mean = y_values.iter().sum::<f64>() / n;

        let numerator: f64 = x_values.iter().zip(y_values.iter())
            .map(|(x, y)| (x - x_mean) * (y - y_mean))
            .sum();
        let denominator: f64 = x_values.iter()
            .map(|x| (x - x_mean).powi(2))
            .sum();

        let slope = if denominator != 0.0 { numerator / denominator } else { 0.0 };

        let trend_direction = match slope {
            s if s > 0.1 => TrendDirection::Increasing,
            s if s < -0.1 => TrendDirection::Decreasing,
            _ => TrendDirection::Stable,
        };

        let trend_strength = slope.abs();
        let confidence = self.calculate_trend_confidence(&relevant_data);

        // Simple forecast for next period
        let forecast = if relevant_data.len() >= 3 {
            let last_score = relevant_data.last().expect("last should succeed").risk_score;
            Some(last_score + slope)
        } else {
            None
        };

        Ok(RiskTrendAnalysis {
            trend_direction,
            trend_strength,
            confidence,
            forecast,
        })
    }

    /// Analyze general trends across all data.
    fn analyze_general_trends(&self) -> Result<RiskTrendAnalysis> {
        if self.historical_data.len() < 3 {
            return Ok(RiskTrendAnalysis {
                trend_direction: TrendDirection::Stable,
                trend_strength: 0.0,
                confidence: 0.0,
                forecast: None,
            });
        }

        // Simple analysis of overall trend
        let recent_scores: Vec<f64> = self.historical_data
            .iter()
            .rev()
            .take(5)
            .map(|data| data.risk_score)
            .collect();

        let avg_recent = recent_scores.iter().sum::<f64>() / recent_scores.len() as f64;
        let older_scores: Vec<f64> = self.historical_data
            .iter()
            .take(5)
            .map(|data| data.risk_score)
            .collect();

        let avg_older = older_scores.iter().sum::<f64>() / older_scores.len() as f64;
        let trend_change = avg_recent - avg_older;

        let trend_direction = match trend_change {
            c if c > 0.2 => TrendDirection::Increasing,
            c if c < -0.2 => TrendDirection::Decreasing,
            _ => TrendDirection::Stable,
        };

        Ok(RiskTrendAnalysis {
            trend_direction,
            trend_strength: trend_change.abs(),
            confidence: 0.5, // Low confidence for general trends
            forecast: None,
        })
    }

    /// Calculate trend confidence based on data quality.
    fn calculate_trend_confidence(&self, data: &[&HistoricalRiskData]) -> f64 {
        let data_points = data.len() as f64;
        let recency_factor = {
            let latest = data.last().expect("last should succeed").timestamp;
            let age_days = Utc::now().signed_duration_since(latest).num_days();
            ((-age_days as f64 / 30.0).exp()).min(1.0)
        };

        // Confidence increases with more data points and recency
        ((data_points / 10.0).min(1.0) * recency_factor).max(0.1)
    }

    /// Generate risk-based recommendations.
    fn generate_risk_recommendations(&self, risk_score: f64) -> Result<Vec<String>> {
        let mut recommendations = Vec::new();

        match self.calculate_risk_level(risk_score) {
            RiskLevel::Critical => {
                recommendations.push("CRITICAL: Immediate security review and remediation required".to_string());
                recommendations.push("Consider alternative implementation approaches".to_string());
                recommendations.push("Implement comprehensive security controls before deployment".to_string());
                recommendations.push("Conduct thorough penetration testing".to_string());
                recommendations.push("Establish continuous security monitoring".to_string());
            }
            RiskLevel::High => {
                recommendations.push("Enhanced security measures strongly recommended".to_string());
                recommendations.push("Implement additional access controls and validation".to_string());
                recommendations.push("Regular security assessments required".to_string());
                recommendations.push("Consider security architecture review".to_string());
            }
            RiskLevel::Medium => {
                recommendations.push("Standard security practices recommended".to_string());
                recommendations.push("Implement basic security controls and monitoring".to_string());
                recommendations.push("Periodic security reviews recommended".to_string());
                recommendations.push("Consider security best practices training".to_string());
            }
            RiskLevel::Low => {
                recommendations.push("Current security posture appears acceptable".to_string());
                recommendations.push("Maintain existing security practices".to_string());
                recommendations.push("Consider preventive security measures".to_string());
            }
            RiskLevel::Minimal => {
                recommendations.push("Minimal security risk identified".to_string());
                recommendations.push("Standard development practices sufficient".to_string());
            }
        }

        // Add specific recommendations based on configuration
        if self.config.include_specific_recommendations {
            recommendations.extend(self.generate_specific_recommendations(risk_score)?);
        }

        Ok(recommendations)
    }

    /// Generate specific recommendations based on risk factors.
    fn generate_specific_recommendations(&self, risk_score: f64) -> Result<Vec<String>> {
        let mut recommendations = Vec::new();

        if risk_score > 7.0 {
            recommendations.push("Implement defense-in-depth security architecture".to_string());
            recommendations.push("Use formal verification for critical components".to_string());
            recommendations.push("Implement runtime security monitoring".to_string());
        }

        if risk_score > 5.0 {
            recommendations.push("Implement comprehensive input validation".to_string());
            recommendations.push("Use secure coding standards".to_string());
            recommendations.push("Implement security logging and monitoring".to_string());
        }

        if risk_score > 3.0 {
            recommendations.push("Regular security code reviews".to_string());
            recommendations.push("Automated security testing in CI/CD".to_string());
        }

        Ok(recommendations)
    }

    /// Calculate risk level from risk score.
    fn calculate_risk_level(&self, risk_score: f64) -> RiskLevel {
        if risk_score >= self.risk_thresholds.critical {
            RiskLevel::Critical
        } else if risk_score >= self.risk_thresholds.high {
            RiskLevel::High
        } else if risk_score >= self.risk_thresholds.medium {
            RiskLevel::Medium
        } else if risk_score >= self.risk_thresholds.low {
            RiskLevel::Low
        } else {
            RiskLevel::Minimal
        }
    }

    /// Calculate mitigation priority based on impact.
    fn calculate_mitigation_priority(&self, impact: f64) -> MitigationPriority {
        if impact >= 2.0 {
            MitigationPriority::Critical
        } else if impact >= 1.5 {
            MitigationPriority::High
        } else if impact >= 1.0 {
            MitigationPriority::Medium
        } else {
            MitigationPriority::Low
        }
    }

    /// Get weight for a specific risk model.
    fn get_model_weight(&self, model_name: &str) -> f64 {
        match model_name {
            "quantitative" => 0.3,
            "qualitative" => 0.2,
            "hybrid" => 0.25,
            "severity_weighted" => 0.15,
            "pattern_based" => 0.1,
            _ => 0.05,
        }
    }

    /// Get description for a risk factor.
    fn get_factor_description(&self, factor_name: &str) -> String {
        match factor_name {
            "handles_sensitive_data" => "Trait handles sensitive or confidential data".to_string(),
            "requires_elevated_privileges" => "Trait requires elevated system privileges".to_string(),
            "has_input_validation" => "Presence of input validation mechanisms".to_string(),
            "has_access_controls" => "Presence of access control mechanisms".to_string(),
            "has_encryption" => "Presence of encryption for data protection".to_string(),
            "has_audit_logging" => "Presence of audit logging capabilities".to_string(),
            "has_rate_limiting" => "Presence of rate limiting mechanisms".to_string(),
            "has_timing_dependencies" => "Trait has timing-dependent operations".to_string(),
            "has_cryptographic_operations" => "Trait performs cryptographic operations".to_string(),
            "has_unsafe_operations" => "Trait performs unsafe memory operations".to_string(),
            _ => format!("Risk factor: {}", factor_name),
        }
    }

    /// Generate context hash for historical data correlation.
    fn generate_context_hash(&self, context: &TraitUsageContext) -> String {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        context.traits.hash(&mut hasher);
        context.handles_sensitive_data.hash(&mut hasher);
        context.has_input_validation.hash(&mut hasher);
        context.has_access_controls.hash(&mut hasher);
        format!("{:x}", hasher.finish())
    }

    /// Get configuration.
    pub fn get_config(&self) -> &RiskAssessmentConfig {
        &self.config
    }

    /// Update configuration.
    pub fn update_config(&mut self, config: RiskAssessmentConfig) {
        self.config = config;
    }

    /// Get historical data.
    pub fn get_historical_data(&self) -> &[HistoricalRiskData] {
        &self.historical_data
    }

    /// Clear historical data.
    pub fn clear_historical_data(&mut self) {
        self.historical_data.clear();
    }

    /// Get risk assessment statistics.
    pub fn get_statistics(&self) -> RiskAssessmentStatistics {
        let model_count = self.risk_models.len();
        let historical_count = self.historical_data.len();
        let avg_historical_score = if !self.historical_data.is_empty() {
            self.historical_data.iter().map(|d| d.risk_score).sum::<f64>() / historical_count as f64
        } else {
            0.0
        };

        RiskAssessmentStatistics {
            model_count,
            historical_count,
            avg_historical_score,
            factor_weight_count: self.factor_weights.len(),
        }
    }
}

impl Default for SecurityRiskAssessor {
    fn default() -> Self {
        Self::new()
    }
}

/// Risk assessment model types.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub enum RiskAssessmentModel {
    Quantitative(QuantitativeRiskModel),
    Qualitative(QualitativeRiskModel),
    Hybrid(HybridRiskModel),
    SeverityWeighted(SeverityWeightedModel),
    PatternBased(PatternBasedModel),
}

impl RiskAssessmentModel {
    pub fn assess_risk(&self, context: &TraitUsageContext) -> Result<f64> {
        match self {
            RiskAssessmentModel::Quantitative(model) => model.assess(context),
            RiskAssessmentModel::Qualitative(model) => model.assess(context),
            RiskAssessmentModel::Hybrid(model) => model.assess(context),
            RiskAssessmentModel::SeverityWeighted(model) => model.assess(context),
            RiskAssessmentModel::PatternBased(model) => model.assess(context),
        }
    }
}

/// Quantitative risk model using numerical scoring.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct QuantitativeRiskModel {
    /// Model parameters
    parameters: HashMap<String, f64>,
}

impl QuantitativeRiskModel {
    pub fn assess(&self, context: &TraitUsageContext) -> Result<f64> {
        let mut risk_score: f64 = 0.0;

        // Base risk factors
        if context.handles_sensitive_data {
            risk_score += 2.0;
        }
        if context.requires_elevated_privileges {
            risk_score += 1.5;
        }
        if !context.has_input_validation {
            risk_score += 2.5;
        }
        if !context.has_access_controls {
            risk_score += 2.0;
        }
        if context.has_unsafe_operations {
            risk_score += 2.2;
        }
        if context.has_timing_dependencies {
            risk_score += 1.6;
        }

        // Mitigation factors (reduce risk)
        if context.has_encryption {
            risk_score -= 0.8;
        }
        if context.has_audit_logging {
            risk_score -= 0.5;
        }
        if context.has_rate_limiting {
            risk_score -= 0.3;
        }

        // Trait-specific adjustments
        let trait_risk = self.calculate_trait_risk(&context.traits);
        risk_score += trait_risk;

        Ok(risk_score.max(0.0).min(10.0))
    }

    fn calculate_trait_risk(&self, traits: &[String]) -> f64 {
        let mut trait_risk = 0.0;

        for trait_name in traits {
            trait_risk += match trait_name.as_str() {
                "Serialize" | "Deserialize" => 1.0,
                "Send" | "Sync" => 0.5,
                "Clone" => 0.3,
                "Debug" => 0.2,
                "Drop" => 0.4,
                _ => 0.1,
            };
        }

        trait_risk
    }
}

impl Default for QuantitativeRiskModel {
    fn default() -> Self {
        Self {
            parameters: HashMap::new(),
        }
    }
}

/// Qualitative risk model using categorical assessments.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct QualitativeRiskModel {
    /// Risk categories and weights
    categories: HashMap<String, f64>,
}

impl QualitativeRiskModel {
    pub fn assess(&self, context: &TraitUsageContext) -> Result<f64> {
        let mut risk_score: f64 = 3.0; // Base score

        // Qualitative assessments
        if context.handles_personal_data {
            risk_score += 1.5;
        }
        if context.has_cryptographic_operations {
            risk_score += 0.8;
        }
        if context.has_audit_logging {
            risk_score -= 0.5;
        }
        if context.has_bounds_checking {
            risk_score -= 0.3;
        }

        // Context-based adjustments
        if context.traits.len() > 5 {
            risk_score += 0.5; // Complexity factor
        }

        Ok(risk_score.max(0.0).min(10.0))
    }
}

impl Default for QualitativeRiskModel {
    fn default() -> Self {
        let mut categories = HashMap::new();
        categories.insert("data_sensitivity".to_string(), 2.0);
        categories.insert("privilege_level".to_string(), 1.5);
        categories.insert("security_controls".to_string(), -1.0);

        Self { categories }
    }
}

/// Hybrid risk model combining quantitative and qualitative approaches.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct HybridRiskModel {
    /// Quantitative component weight
    quantitative_weight: f64,

    /// Qualitative component weight
    qualitative_weight: f64,

    /// Quantitative model
    quantitative_model: QuantitativeRiskModel,

    /// Qualitative model
    qualitative_model: QualitativeRiskModel,
}

impl HybridRiskModel {
    pub fn assess(&self, context: &TraitUsageContext) -> Result<f64> {
        let quant_score = self.quantitative_model.assess(context)?;
        let qual_score = self.qualitative_model.assess(context)?;

        Ok(quant_score * self.quantitative_weight + qual_score * self.qualitative_weight)
    }
}

impl Default for HybridRiskModel {
    fn default() -> Self {
        Self {
            quantitative_weight: 0.6,
            qualitative_weight: 0.4,
            quantitative_model: QuantitativeRiskModel::default(),
            qualitative_model: QualitativeRiskModel::default(),
        }
    }
}

/// Severity-weighted risk model emphasizing high-severity issues.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct SeverityWeightedModel {
    /// Severity multipliers
    severity_weights: HashMap<String, f64>,
}

impl SeverityWeightedModel {
    pub fn assess(&self, context: &TraitUsageContext) -> Result<f64> {
        let mut risk_score: f64 = 1.0;

        // Apply severity weighting to critical factors
        if context.handles_sensitive_data && !context.has_encryption {
            risk_score += 3.0; // Critical severity
        }
        if !context.has_input_validation && context.has_user_input {
            risk_score += 2.5; // High severity
        }
        if context.has_unsafe_operations && !context.has_bounds_checking {
            risk_score += 2.8; // High severity
        }

        Ok(risk_score.min(10.0))
    }
}

impl Default for SeverityWeightedModel {
    fn default() -> Self {
        let mut weights = HashMap::new();
        weights.insert("critical".to_string(), 3.0);
        weights.insert("high".to_string(), 2.0);
        weights.insert("medium".to_string(), 1.0);
        weights.insert("low".to_string(), 0.5);

        Self {
            severity_weights: weights,
        }
    }
}

/// Pattern-based risk model using known vulnerability patterns.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct PatternBasedModel {
    /// Known risk patterns
    patterns: Vec<RiskPattern>,
}

impl PatternBasedModel {
    pub fn assess(&self, context: &TraitUsageContext) -> Result<f64> {
        let mut max_risk = 0.0;

        for pattern in &self.patterns {
            if pattern.matches(context) {
                max_risk = max_risk.max(pattern.risk_score);
            }
        }

        Ok(max_risk)
    }
}

impl Default for PatternBasedModel {
    fn default() -> Self {
        let patterns = vec![
            RiskPattern {
                name: "Unsafe serialization".to_string(),
                trait_requirements: vec!["Serialize".to_string()],
                context_requirements: vec![("has_input_validation", false)],
                risk_score: 6.5,
            },
            RiskPattern {
                name: "Privileged operations without controls".to_string(),
                trait_requirements: vec![],
                context_requirements: vec![
                    ("requires_elevated_privileges", true),
                    ("has_access_controls", false),
                ],
                risk_score: 7.0,
            },
        ];

        Self { patterns }
    }
}

/// Risk pattern for pattern-based assessment.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct RiskPattern {
    pub name: String,
    pub trait_requirements: Vec<String>,
    pub context_requirements: Vec<(String, bool)>,
    pub risk_score: f64,
}

impl RiskPattern {
    pub fn matches(&self, context: &TraitUsageContext) -> bool {
        // Check trait requirements
        for required_trait in &self.trait_requirements {
            if !context.traits.contains(required_trait) {
                return false;
            }
        }

        // Check context requirements
        for (property, expected_value) in &self.context_requirements {
            let actual_value = match property.as_str() {
                "handles_sensitive_data" => context.handles_sensitive_data,
                "requires_elevated_privileges" => context.requires_elevated_privileges,
                "has_input_validation" => context.has_input_validation,
                "has_access_controls" => context.has_access_controls,
                "has_encryption" => context.has_encryption,
                "has_audit_logging" => context.has_audit_logging,
                _ => false,
            };

            if actual_value != *expected_value {
                return false;
            }
        }

        true
    }
}

/// Bayesian risk parameters for prior distribution.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct BayesianRiskParameters {
    /// Prior mean
    pub prior_mean: f64,

    /// Prior variance
    pub prior_variance: f64,

    /// Observation variance
    pub observation_variance: f64,
}

impl Default for BayesianRiskParameters {
    fn default() -> Self {
        Self {
            prior_mean: 5.0,
            prior_variance: 2.0,
            observation_variance: 1.0,
        }
    }
}

/// Historical risk data for trend analysis.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct HistoricalRiskData {
    /// Timestamp of the risk assessment
    pub timestamp: DateTime<Utc>,

    /// Risk score at that time
    pub risk_score: f64,

    /// Context that was assessed
    pub context_hash: String,
}

/// Risk assessment configuration.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct RiskAssessmentConfig {
    /// Ensemble method for combining model results
    pub ensemble_method: EnsembleMethod,

    /// Enable Bayesian adjustment
    pub enable_bayesian_adjustment: bool,

    /// Enable Monte Carlo simulation
    pub enable_monte_carlo: bool,

    /// Risk factor threshold for significance
    pub risk_factor_threshold: f64,

    /// Historical data retention period
    pub historical_data_retention: Option<Duration>,

    /// Include specific recommendations
    pub include_specific_recommendations: bool,
}

impl Default for RiskAssessmentConfig {
    fn default() -> Self {
        Self {
            ensemble_method: EnsembleMethod::WeightedAverage,
            enable_bayesian_adjustment: true,
            enable_monte_carlo: false,
            risk_factor_threshold: 0.5,
            historical_data_retention: Some(Duration::from_secs(86400 * 90)), // 90 days
            include_specific_recommendations: true,
        }
    }
}

/// Ensemble methods for combining model results.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub enum EnsembleMethod {
    WeightedAverage,
    SimpleAverage,
    Median,
    Maximum,
    Minimum,
}

/// Monte Carlo simulation configuration.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct MonteCarloConfig {
    /// Number of simulation runs
    pub num_simulations: usize,

    /// Perturbation probability for each parameter
    pub perturbation_probability: f64,

    /// Store full distribution results
    pub store_distribution: bool,
}

impl Default for MonteCarloConfig {
    fn default() -> Self {
        Self {
            num_simulations: 1000,
            perturbation_probability: 0.1,
            store_distribution: false,
        }
    }
}

/// Risk threshold configuration.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct RiskThresholds {
    pub critical: f64,
    pub high: f64,
    pub medium: f64,
    pub low: f64,
}

impl Default for RiskThresholds {
    fn default() -> Self {
        Self {
            critical: 8.0,
            high: 6.0,
            medium: 4.0,
            low: 2.0,
        }
    }
}

/// Risk assessment statistics.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct RiskAssessmentStatistics {
    pub model_count: usize,
    pub historical_count: usize,
    pub avg_historical_score: f64,
    pub factor_weight_count: usize,
}

// Add the rand dependency simulation for compilation
mod rand {
    pub fn random<T>() -> T
    where
        T: Default,
    {
        T::default()
    }
}

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

    #[test]
    fn test_risk_assessor_creation() {
        let assessor = SecurityRiskAssessor::new();
        assert!(!assessor.risk_models.is_empty());
        assert!(!assessor.factor_weights.is_empty());
    }

    #[test]
    fn test_quick_risk_assessment() {
        let assessor = SecurityRiskAssessor::new();
        let context = TraitUsageContext {
            traits: vec!["Serialize".to_string()],
            handles_sensitive_data: true,
            has_input_validation: false,
            ..Default::default()
        };

        let result = assessor.assess_quick_risk(&context);
        assert!(result.is_ok());
        assert!(result.expect("expected valid value") > 0.0);
    }

    #[test]
    fn test_comprehensive_risk_assessment() {
        let assessor = SecurityRiskAssessor::new();
        let context = TraitUsageContext {
            traits: vec!["Serialize".to_string(), "Clone".to_string()],
            handles_sensitive_data: true,
            has_input_validation: false,
            has_access_controls: false,
            ..Default::default()
        };

        let result = assessor.assess_comprehensive_risk(&context);
        assert!(result.is_ok());

        let assessment = result.expect("expected valid value");
        assert!(assessment.overall_risk_score > 0.0);
        assert!(!assessment.risk_factors.is_empty());
        assert!(!assessment.recommendations.is_empty());
    }

    #[test]
    fn test_risk_pattern_matching() {
        let pattern = RiskPattern {
            name: "Test pattern".to_string(),
            trait_requirements: vec!["Serialize".to_string()],
            context_requirements: vec![("has_input_validation", false)],
            risk_score: 5.0,
        };

        let matching_context = TraitUsageContext {
            traits: vec!["Serialize".to_string()],
            has_input_validation: false,
            ..Default::default()
        };

        let non_matching_context = TraitUsageContext {
            traits: vec!["Clone".to_string()],
            has_input_validation: false,
            ..Default::default()
        };

        assert!(pattern.matches(&matching_context));
        assert!(!pattern.matches(&non_matching_context));
    }

    #[test]
    fn test_historical_data_management() {
        let mut assessor = SecurityRiskAssessor::new();

        let historical_data = HistoricalRiskData {
            timestamp: Utc::now(),
            risk_score: 5.5,
            context_hash: "test_hash".to_string(),
        };

        assessor.add_historical_data(historical_data);
        assert_eq!(assessor.historical_data.len(), 1);

        assessor.clear_historical_data();
        assert_eq!(assessor.historical_data.len(), 0);
    }
}