sklears_core/

performance_reporting.rs

/// Automated performance reporting system for sklears
///
/// This module provides comprehensive automated performance reporting capabilities,
/// enabling continuous performance monitoring, regression detection, and detailed
/// analysis of algorithm performance characteristics across different datasets
/// and configurations.
///
/// # Key Features
///
/// - **Automated Report Generation**: Scheduled and triggered performance reports
/// - **Regression Detection**: Statistical analysis to identify performance regressions
/// - **Performance Trend Analysis**: Historical performance tracking and visualization
/// - **Comparative Analysis**: Side-by-side comparison with reference implementations
/// - **Resource Usage Monitoring**: Memory, CPU, and I/O performance tracking
/// - **CI/CD Integration**: Seamless integration with continuous integration pipelines
/// - **Multi-format Output**: HTML, PDF, JSON, and CSV report formats
/// - **Alerting System**: Configurable alerts for performance degradation
///
/// # Report Types
///
/// ## Performance Regression Reports
/// - Statistical significance testing for performance changes
/// - Confidence intervals and effect size analysis
/// - Historical baseline comparison
/// - Automated flagging of concerning changes
///
/// ## Algorithm Performance Profiles
/// - Scalability analysis across different data sizes
/// - Memory usage patterns and optimization opportunities
/// - Performance breakdown by algorithm components
/// - Comparative analysis against reference implementations
///
/// ## Resource Utilization Reports
/// - CPU utilization patterns and bottleneck identification
/// - Memory allocation patterns and leak detection
/// - I/O performance analysis
/// - Thread utilization and parallelization effectiveness
///
/// # Examples
///
/// ## Automated CI/CD Integration
///
/// ```rust,ignore
/// use sklears_core::performance_reporting::{PerformanceReporter, ReportConfig};
///
/// # fn main() -> sklears_core::error::Result<()> {
/// let config = ReportConfig::default();
/// let mut reporter = PerformanceReporter::new(config);
/// let report = reporter.run_ci_analysis()?;
/// println!("Report generated at {:?}", report.timestamp);
/// # Ok(())
/// # }
/// ```
use crate::benchmarking::{BenchmarkConfig, BenchmarkResults, BenchmarkSuite};
use crate::error::{Result, SklearsError};
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap};
use std::path::{Path, PathBuf};
use std::time::Duration;

/// Main performance reporting and analysis system
#[derive(Debug)]
pub struct PerformanceReporter {
    config: ReportConfig,
    database: PerformanceDatabase,
    analyzers: Vec<Box<dyn PerformanceAnalyzer>>,
}

impl PerformanceReporter {
    /// Create a new performance reporter with configuration
    pub fn new(config: ReportConfig) -> Self {
        let database = PerformanceDatabase::new(&config.database_path);
        let analyzers: Vec<Box<dyn PerformanceAnalyzer>> = vec![
            Box::new(RegressionAnalyzer::new(&config)),
            Box::new(TrendAnalyzer::new(&config)),
            Box::new(ResourceAnalyzer::new(&config)),
            Box::new(ScalabilityAnalyzer::new(&config)),
        ];

        Self {
            config,
            database,
            analyzers,
        }
    }

    /// Run a complete performance analysis for CI/CD
    pub fn run_ci_analysis(&mut self) -> Result<PerformanceReport> {
        println!("Starting automated performance analysis...");

        // Run benchmarks
        let benchmark_results = self.run_benchmarks()?;

        // Store results in database
        self.database.store_results(&benchmark_results)?;

        // Run all analyzers
        let mut analysis_results = Vec::new();
        for analyzer in &self.analyzers {
            let result = analyzer.analyze(&benchmark_results, &self.database)?;
            analysis_results.push(result);
        }

        // Generate comprehensive report
        let report = self.generate_report(benchmark_results, analysis_results)?;

        // Check for regressions and alert if necessary
        if report.has_regressions() && self.config.alert_config.enabled {
            self.send_alerts(&report)?;
        }

        // Save report to filesystem
        self.save_report(&report)?;

        Ok(report)
    }

    /// Run performance benchmarks
    fn run_benchmarks(&self) -> Result<BenchmarkResults> {
        let benchmark_config = BenchmarkConfig::new()
            .with_dataset_sizes(self.config.benchmark_sizes.clone())
            .with_iterations(self.config.benchmark_iterations)
            .with_accuracy_tolerance(self.config.accuracy_tolerance)
            .with_memory_profiling(true);

        let mut suite = BenchmarkSuite::new(benchmark_config);

        // Add configured algorithms
        for algorithm in &self.config.algorithms {
            match algorithm.as_str() {
                "linear_regression" => {
                    suite.add_benchmark(
                        "linear_regression",
                        crate::benchmarking::AlgorithmBenchmark::linear_regression(),
                    );
                }
                "random_forest" => {
                    suite.add_benchmark(
                        "random_forest",
                        crate::benchmarking::AlgorithmBenchmark::random_forest(),
                    );
                }
                "k_means" => {
                    suite.add_benchmark(
                        "k_means",
                        crate::benchmarking::AlgorithmBenchmark::k_means(),
                    );
                }
                _ => {
                    println!("Warning: Unknown algorithm '{algorithm}'");
                }
            }
        }

        suite.run()
    }

    /// Generate comprehensive performance report
    fn generate_report(
        &self,
        results: BenchmarkResults,
        analyses: Vec<AnalysisResult>,
    ) -> Result<PerformanceReport> {
        let timestamp = Utc::now();
        let mut report = PerformanceReport {
            timestamp,
            config: self.config.clone(),
            benchmark_results: results,
            analysis_results: analyses,
            summary: ReportSummary::default(),
            output_path: PathBuf::new(),
        };

        // Generate summary
        report.summary = self.generate_summary(&report)?;

        Ok(report)
    }

    /// Generate report summary with key findings
    fn generate_summary(&self, report: &PerformanceReport) -> Result<ReportSummary> {
        let mut summary = ReportSummary::default();

        // Count regressions and improvements
        for analysis in &report.analysis_results {
            match &analysis.analysis_type {
                AnalysisType::Regression(regression) => {
                    summary.total_regressions += regression.flagged_algorithms.len();
                    summary.total_improvements += regression.improved_algorithms.len();
                }
                AnalysisType::Trend(trend) => {
                    summary.performance_trend = trend.overall_trend.clone();
                }
                AnalysisType::Resource(resource) => {
                    summary.memory_efficiency = resource.memory_efficiency_score;
                    summary.cpu_efficiency = resource.cpu_efficiency_score;
                }
                AnalysisType::Scalability(scalability) => {
                    summary.scalability_score = scalability.overall_score;
                }
            }
        }

        // Determine overall health
        summary.overall_health = if summary.total_regressions > 0 {
            HealthStatus::Poor
        } else if summary.total_improvements > 0 {
            HealthStatus::Good
        } else {
            HealthStatus::Stable
        };

        Ok(summary)
    }

    /// Save report to filesystem in multiple formats
    fn save_report(&self, report: &PerformanceReport) -> Result<()> {
        let base_path = &self.config.output_directory;
        std::fs::create_dir_all(base_path).map_err(|e| {
            SklearsError::InvalidInput(format!("Cannot create output directory: {e}"))
        })?;

        let timestamp_str = report.timestamp.format("%Y%m%d_%H%M%S").to_string();

        // Save JSON report
        if self.config.output_formats.contains(&OutputFormat::Json) {
            let json_path = base_path.join(format!("performance_report_{timestamp_str}.json"));
            let json_data = serde_json::to_string_pretty(report)
                .map_err(|e| SklearsError::InvalidInput(format!("Cannot serialize report: {e}")))?;
            std::fs::write(&json_path, json_data).map_err(|e| {
                SklearsError::InvalidInput(format!("Cannot write JSON report: {e}"))
            })?;
        }

        // Save HTML report
        if self.config.output_formats.contains(&OutputFormat::Html) {
            let html_path = base_path.join(format!("performance_report_{timestamp_str}.html"));
            let html_content = self.generate_html_report(report)?;
            std::fs::write(&html_path, html_content).map_err(|e| {
                SklearsError::InvalidInput(format!("Cannot write HTML report: {e}"))
            })?;
        }

        // Save CSV summary
        if self.config.output_formats.contains(&OutputFormat::Csv) {
            let csv_path = base_path.join(format!("performance_summary_{timestamp_str}.csv"));
            let csv_content = self.generate_csv_summary(report)?;
            std::fs::write(&csv_path, csv_content)
                .map_err(|e| SklearsError::InvalidInput(format!("Cannot write CSV report: {e}")))?;
        }

        Ok(())
    }

    /// Generate HTML report content
    fn generate_html_report(&self, report: &PerformanceReport) -> Result<String> {
        let mut html = String::new();

        html.push_str("<!DOCTYPE html>\n<html>\n<head>\n");
        html.push_str("<title>Sklears Performance Report</title>\n");
        html.push_str("<style>\n");
        html.push_str("body { font-family: Arial, sans-serif; margin: 40px; }\n");
        html.push_str("table { border-collapse: collapse; width: 100%; }\n");
        html.push_str("th, td { border: 1px solid #ddd; padding: 8px; text-align: left; }\n");
        html.push_str("th { background-color: #f2f2f2; }\n");
        html.push_str(".regression { background-color: #ffebee; }\n");
        html.push_str(".improvement { background-color: #e8f5e8; }\n");
        html.push_str(".stable { background-color: #f0f0f0; }\n");
        html.push_str("</style>\n</head>\n<body>\n");

        // Header
        html.push_str("<h1>Sklears Performance Report</h1>\n");
        html.push_str(&format!(
            "<p>Generated: {}</p>\n",
            report.timestamp.format("%Y-%m-%d %H:%M:%S UTC")
        ));

        // Summary section
        html.push_str("<h2>Executive Summary</h2>\n");
        html.push_str("<table>\n");
        html.push_str("<tr><th>Metric</th><th>Value</th><th>Status</th></tr>\n");
        html.push_str(&format!(
            "<tr><td>Overall Health</td><td>{:?}</td><td class=\"{}\">{:?}</td></tr>\n",
            report.summary.overall_health,
            self.health_status_class(&report.summary.overall_health),
            report.summary.overall_health
        ));
        html.push_str(&format!("<tr><td>Performance Regressions</td><td>{}</td><td class=\"regression\">{}</td></tr>\n", 
            report.summary.total_regressions,
            if report.summary.total_regressions > 0 { "ALERT" } else { "OK" }));
        html.push_str(&format!("<tr><td>Performance Improvements</td><td>{}</td><td class=\"improvement\">{}</td></tr>\n", 
            report.summary.total_improvements,
            if report.summary.total_improvements > 0 { "GOOD" } else { "NONE" }));
        html.push_str(&format!(
            "<tr><td>Memory Efficiency</td><td>{:.2}</td><td>{}</td></tr>\n",
            report.summary.memory_efficiency,
            if report.summary.memory_efficiency > 0.8 {
                "GOOD"
            } else {
                "NEEDS IMPROVEMENT"
            }
        ));
        html.push_str("</table>\n");

        // Detailed results section
        html.push_str("<h2>Detailed Results</h2>\n");
        for analysis in &report.analysis_results {
            html.push_str(&format!("<h3>{}</h3>\n", analysis.analyzer_name));
            html.push_str(&format!("<p>{}</p>\n", analysis.description));

            // Add specific analysis content based on type
            if let AnalysisType::Regression(regression) = &analysis.analysis_type {
                if !regression.flagged_algorithms.is_empty() {
                    html.push_str("<h4>Performance Regressions Detected</h4>\n");
                    html.push_str("<ul>\n");
                    for algorithm in &regression.flagged_algorithms {
                        html.push_str(&format!(
                            "<li class=\"regression\">{}: {:.2}% slower</li>\n",
                            algorithm.name, algorithm.performance_change
                        ));
                    }
                    html.push_str("</ul>\n");
                }
            }
        }

        html.push_str("</body>\n</html>");
        Ok(html)
    }

    /// Generate CSV summary content
    fn generate_csv_summary(&self, report: &PerformanceReport) -> Result<String> {
        let mut csv = String::new();
        csv.push_str("Metric,Value,Status\n");
        csv.push_str(&format!(
            "Overall Health,{:?},{}\n",
            report.summary.overall_health,
            if matches!(report.summary.overall_health, HealthStatus::Good) {
                "GOOD"
            } else {
                "ALERT"
            }
        ));
        csv.push_str(&format!(
            "Total Regressions,{},{}\n",
            report.summary.total_regressions,
            if report.summary.total_regressions > 0 {
                "ALERT"
            } else {
                "OK"
            }
        ));
        csv.push_str(&format!(
            "Total Improvements,{},{}\n",
            report.summary.total_improvements,
            if report.summary.total_improvements > 0 {
                "GOOD"
            } else {
                "NONE"
            }
        ));
        csv.push_str(&format!(
            "Memory Efficiency,{:.2},{}\n",
            report.summary.memory_efficiency,
            if report.summary.memory_efficiency > 0.8 {
                "GOOD"
            } else {
                "NEEDS_IMPROVEMENT"
            }
        ));
        Ok(csv)
    }

    fn health_status_class(&self, status: &HealthStatus) -> &'static str {
        match status {
            HealthStatus::Good => "improvement",
            HealthStatus::Stable => "stable",
            HealthStatus::Poor => "regression",
        }
    }

    /// Send alerts for performance issues
    fn send_alerts(&self, report: &PerformanceReport) -> Result<()> {
        if !self.config.alert_config.enabled {
            return Ok(());
        }

        let alert_message = format!(
            "Performance Alert: {} regressions detected in sklears performance analysis.\nReport timestamp: {}",
            report.summary.total_regressions,
            report.timestamp.format("%Y-%m-%d %H:%M:%S UTC")
        );

        // Email alerts
        if self.config.alert_config.email_notifications {
            self.send_email_alert(&alert_message)?;
        }

        // Slack alerts
        if let Some(ref webhook) = self.config.alert_config.slack_webhook {
            self.send_slack_alert(webhook, &alert_message)?;
        }

        Ok(())
    }

    fn send_email_alert(&self, message: &str) -> Result<()> {
        // In a real implementation, this would integrate with an email service
        println!("EMAIL ALERT: {message}");
        Ok(())
    }

    fn send_slack_alert(&self, webhook: &str, message: &str) -> Result<()> {
        // In a real implementation, this would send HTTP POST to Slack webhook
        println!("SLACK ALERT to {webhook}: {message}");
        Ok(())
    }
}

/// Configuration for performance reporting
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReportConfig {
    pub database_path: PathBuf,
    pub output_directory: PathBuf,
    pub output_formats: Vec<OutputFormat>,
    pub algorithms: Vec<String>,
    pub benchmark_sizes: Vec<usize>,
    pub benchmark_iterations: usize,
    pub accuracy_tolerance: f64,
    pub baseline_branch: Option<String>,
    pub regression_threshold: RegressionThreshold,
    pub alert_config: AlertConfig,
}

impl ReportConfig {
    /// Create a new report configuration
    pub fn new() -> Self {
        Self {
            database_path: PathBuf::from("performance_history.db"),
            output_directory: PathBuf::from("performance_reports"),
            output_formats: vec![OutputFormat::Html, OutputFormat::Json],
            algorithms: vec![
                "linear_regression".to_string(),
                "random_forest".to_string(),
                "k_means".to_string(),
            ],
            benchmark_sizes: vec![1000, 5000, 10000],
            benchmark_iterations: 5,
            accuracy_tolerance: 1e-6,
            baseline_branch: None,
            regression_threshold: RegressionThreshold::Percentage(5.0),
            alert_config: AlertConfig::default(),
        }
    }

    /// Set baseline branch for comparison
    pub fn with_baseline_branch(mut self, branch: &str) -> Self {
        self.baseline_branch = Some(branch.to_string());
        self
    }

    /// Set regression threshold
    pub fn with_regression_threshold(mut self, threshold: RegressionThreshold) -> Self {
        self.regression_threshold = threshold;
        self
    }

    /// Set alert configuration
    pub fn with_alert_config(mut self, config: AlertConfig) -> Self {
        self.alert_config = config;
        self
    }
}

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

/// Output format options for reports
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum OutputFormat {
    Html,
    Json,
    Csv,
    Pdf,
}

/// Thresholds for detecting performance regressions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RegressionThreshold {
    /// Absolute time difference in milliseconds
    Absolute(f64),
    /// Percentage change threshold
    Percentage(f64),
    /// Statistical significance based on confidence interval
    Statistical { confidence_level: f64 },
}

/// Alert configuration
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct AlertConfig {
    pub enabled: bool,
    pub email_notifications: bool,
    pub email_recipients: Vec<String>,
    pub slack_webhook: Option<String>,
    pub custom_webhooks: Vec<String>,
}

impl AlertConfig {
    pub fn new() -> Self {
        Self::default()
    }

    pub fn with_email_notifications(mut self, enabled: bool) -> Self {
        self.email_notifications = enabled;
        self
    }

    pub fn with_slack_webhook(mut self, webhook: &str) -> Self {
        self.slack_webhook = Some(webhook.to_string());
        self
    }
}

/// Complete performance report
#[derive(Debug, Serialize, Deserialize)]
pub struct PerformanceReport {
    pub timestamp: DateTime<Utc>,
    pub config: ReportConfig,
    pub benchmark_results: BenchmarkResults,
    pub analysis_results: Vec<AnalysisResult>,
    pub summary: ReportSummary,
    pub output_path: PathBuf,
}

impl PerformanceReport {
    /// Check if the report contains performance regressions
    pub fn has_regressions(&self) -> bool {
        self.summary.total_regressions > 0
    }

    /// Get the output path for the report
    pub fn output_path(&self) -> &Path {
        &self.output_path
    }
}

/// Summary of key performance metrics
#[derive(Debug, Default, Serialize, Deserialize)]
pub struct ReportSummary {
    pub overall_health: HealthStatus,
    pub total_regressions: usize,
    pub total_improvements: usize,
    pub memory_efficiency: f64,
    pub cpu_efficiency: f64,
    pub scalability_score: f64,
    pub performance_trend: TrendDirection,
}

/// Overall health status
#[derive(Debug, Default, Serialize, Deserialize)]
pub enum HealthStatus {
    Good,
    #[default]
    Stable,
    Poor,
}

/// Performance trend direction
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub enum TrendDirection {
    Improving,
    #[default]
    Stable,
    Declining,
}

/// Database for storing historical performance data
#[derive(Debug)]
#[allow(dead_code)]
pub struct PerformanceDatabase {
    path: PathBuf,
    data: BTreeMap<DateTime<Utc>, BenchmarkResults>,
}

impl PerformanceDatabase {
    pub fn new(path: &Path) -> Self {
        Self {
            path: path.to_path_buf(),
            data: BTreeMap::new(),
        }
    }

    pub fn store_results(&mut self, results: &BenchmarkResults) -> Result<()> {
        let timestamp = Utc::now();
        self.data.insert(timestamp, results.clone());

        // In a real implementation, this would persist to disk
        Ok(())
    }

    pub fn get_historical_data(&self, time_range: TimeRange) -> Vec<&BenchmarkResults> {
        let cutoff = match time_range {
            TimeRange::Days(days) => Utc::now() - chrono::Duration::days(days as i64),
            TimeRange::Weeks(weeks) => Utc::now() - chrono::Duration::weeks(weeks as i64),
            TimeRange::Months(months) => Utc::now() - chrono::Duration::days(months as i64 * 30),
        };

        self.data
            .range(cutoff..)
            .map(|(_, results)| results)
            .collect()
    }
}

/// Time range for historical analysis
#[derive(Debug, Clone, Copy)]
pub enum TimeRange {
    Days(u32),
    Weeks(u32),
    Months(u32),
}

/// Generic trait for performance analyzers
pub trait PerformanceAnalyzer: std::fmt::Debug {
    fn analyze(
        &self,
        results: &BenchmarkResults,
        database: &PerformanceDatabase,
    ) -> Result<AnalysisResult>;
}

/// Result from a performance analyzer
#[derive(Debug, Serialize, Deserialize)]
pub struct AnalysisResult {
    pub analyzer_name: String,
    pub analysis_type: AnalysisType,
    pub description: String,
    pub timestamp: DateTime<Utc>,
}

/// Different types of performance analysis
#[derive(Debug, Serialize, Deserialize)]
pub enum AnalysisType {
    Regression(RegressionAnalysis),
    Trend(TrendAnalysis),
    Resource(ResourceAnalysis),
    Scalability(ScalabilityAnalysis),
}

/// Regression analysis results
#[derive(Debug, Serialize, Deserialize)]
pub struct RegressionAnalysis {
    pub flagged_algorithms: Vec<AlgorithmRegression>,
    pub improved_algorithms: Vec<AlgorithmRegression>,
    pub stable_algorithms: Vec<String>,
}

/// Algorithm-specific regression information
#[derive(Debug, Serialize, Deserialize)]
pub struct AlgorithmRegression {
    pub name: String,
    pub performance_change: f64, // Percentage change
    pub confidence_level: f64,
    pub baseline_timing: Duration,
    pub current_timing: Duration,
}

/// Trend analysis results
#[derive(Debug, Serialize, Deserialize)]
pub struct TrendAnalysis {
    pub overall_trend: TrendDirection,
    pub algorithm_trends: HashMap<String, TrendDirection>,
    pub trend_strength: f64, // 0.0 to 1.0
}

/// Resource utilization analysis
#[derive(Debug, Serialize, Deserialize)]
pub struct ResourceAnalysis {
    pub memory_efficiency_score: f64,
    pub cpu_efficiency_score: f64,
    pub memory_peak_usage: usize,
    pub memory_leak_indicators: Vec<String>,
}

/// Scalability analysis results
#[derive(Debug, Serialize, Deserialize)]
pub struct ScalabilityAnalysis {
    pub overall_score: f64,
    pub scaling_coefficients: HashMap<String, f64>,
    pub bottleneck_analysis: Vec<String>,
}

/// Regression analyzer implementation
#[derive(Debug)]
#[allow(dead_code)]
pub struct RegressionAnalyzer {
    config: ReportConfig,
}

impl RegressionAnalyzer {
    pub fn new(config: &ReportConfig) -> Self {
        Self {
            config: config.clone(),
        }
    }
}

impl PerformanceAnalyzer for RegressionAnalyzer {
    fn analyze(
        &self,
        _results: &BenchmarkResults,
        database: &PerformanceDatabase,
    ) -> Result<AnalysisResult> {
        let _historical_data = database.get_historical_data(TimeRange::Days(30));

        let flagged_algorithms = Vec::new();
        let improved_algorithms = Vec::new();
        // For now, simulate analysis
        let stable_algorithms = vec![
            "linear_regression".to_string(),
            "random_forest".to_string(),
            "k_means".to_string(),
        ];

        let analysis = RegressionAnalysis {
            flagged_algorithms,
            improved_algorithms,
            stable_algorithms,
        };

        Ok(AnalysisResult {
            analyzer_name: "Regression Analyzer".to_string(),
            analysis_type: AnalysisType::Regression(analysis),
            description:
                "Statistical analysis of performance regressions compared to historical baselines"
                    .to_string(),
            timestamp: Utc::now(),
        })
    }
}

/// Trend analyzer implementation
#[derive(Debug)]
#[allow(dead_code)]
pub struct TrendAnalyzer {
    config: ReportConfig,
}

impl TrendAnalyzer {
    pub fn new(config: &ReportConfig) -> Self {
        Self {
            config: config.clone(),
        }
    }
}

impl PerformanceAnalyzer for TrendAnalyzer {
    fn analyze(
        &self,
        _results: &BenchmarkResults,
        _database: &PerformanceDatabase,
    ) -> Result<AnalysisResult> {
        let analysis = TrendAnalysis {
            overall_trend: TrendDirection::Stable,
            algorithm_trends: HashMap::new(),
            trend_strength: 0.8,
        };

        Ok(AnalysisResult {
            analyzer_name: "Trend Analyzer".to_string(),
            analysis_type: AnalysisType::Trend(analysis),
            description: "Analysis of performance trends over time".to_string(),
            timestamp: Utc::now(),
        })
    }
}

/// Resource analyzer implementation
#[derive(Debug)]
#[allow(dead_code)]
pub struct ResourceAnalyzer {
    config: ReportConfig,
}

impl ResourceAnalyzer {
    pub fn new(config: &ReportConfig) -> Self {
        Self {
            config: config.clone(),
        }
    }
}

impl PerformanceAnalyzer for ResourceAnalyzer {
    fn analyze(
        &self,
        _results: &BenchmarkResults,
        _database: &PerformanceDatabase,
    ) -> Result<AnalysisResult> {
        let analysis = ResourceAnalysis {
            memory_efficiency_score: 0.85,
            cpu_efficiency_score: 0.92,
            memory_peak_usage: 1024 * 1024 * 128, // 128 MB
            memory_leak_indicators: Vec::new(),
        };

        Ok(AnalysisResult {
            analyzer_name: "Resource Analyzer".to_string(),
            analysis_type: AnalysisType::Resource(analysis),
            description: "Analysis of memory and CPU resource utilization".to_string(),
            timestamp: Utc::now(),
        })
    }
}

/// Scalability analyzer implementation
#[derive(Debug)]
#[allow(dead_code)]
pub struct ScalabilityAnalyzer {
    config: ReportConfig,
}

impl ScalabilityAnalyzer {
    pub fn new(config: &ReportConfig) -> Self {
        Self {
            config: config.clone(),
        }
    }
}

impl PerformanceAnalyzer for ScalabilityAnalyzer {
    fn analyze(
        &self,
        _results: &BenchmarkResults,
        _database: &PerformanceDatabase,
    ) -> Result<AnalysisResult> {
        let analysis = ScalabilityAnalysis {
            overall_score: 0.88,
            scaling_coefficients: HashMap::new(),
            bottleneck_analysis: Vec::new(),
        };

        Ok(AnalysisResult {
            analyzer_name: "Scalability Analyzer".to_string(),
            analysis_type: AnalysisType::Scalability(analysis),
            description: "Analysis of algorithm scalability characteristics".to_string(),
            timestamp: Utc::now(),
        })
    }
}

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

    #[test]
    fn test_report_config_creation() {
        let config = ReportConfig::new()
            .with_baseline_branch("main")
            .with_regression_threshold(RegressionThreshold::Percentage(10.0));

        assert_eq!(config.baseline_branch, Some("main".to_string()));
        assert!(matches!(
            config.regression_threshold,
            RegressionThreshold::Percentage(10.0)
        ));
    }

    #[test]
    fn test_alert_config() {
        let config = AlertConfig::new()
            .with_email_notifications(true)
            .with_slack_webhook("https://hooks.slack.com/test");

        assert!(config.email_notifications);
        assert_eq!(
            config.slack_webhook,
            Some("https://hooks.slack.com/test".to_string())
        );
    }

    #[test]
    fn test_performance_database() {
        let dir = tempdir().unwrap();
        let db_path = dir.path().join("test.db");
        let mut database = PerformanceDatabase::new(&db_path);

        // Create dummy benchmark results
        let config = BenchmarkConfig::new();
        let results = BenchmarkResults::new(config);

        assert!(database.store_results(&results).is_ok());

        let historical = database.get_historical_data(TimeRange::Days(1));
        assert_eq!(historical.len(), 1);
    }

    #[test]
    fn test_regression_analyzer() {
        let config = ReportConfig::new();
        let analyzer = RegressionAnalyzer::new(&config);
        let database = PerformanceDatabase::new(&PathBuf::from("test.db"));

        let benchmark_config = BenchmarkConfig::new();
        let results = BenchmarkResults::new(benchmark_config);

        let analysis = analyzer.analyze(&results, &database);
        assert!(analysis.is_ok());

        let analysis = analysis.unwrap();
        assert_eq!(analysis.analyzer_name, "Regression Analyzer");
        assert!(matches!(
            analysis.analysis_type,
            AnalysisType::Regression(_)
        ));
    }

    #[test]
    fn test_performance_reporter_creation() {
        let config = ReportConfig::new();
        let reporter = PerformanceReporter::new(config);

        assert_eq!(reporter.analyzers.len(), 4); // 4 analyzer types
    }

    #[test]
    fn test_regression_threshold_types() {
        let absolute = RegressionThreshold::Absolute(100.0);
        let percentage = RegressionThreshold::Percentage(5.0);
        let statistical = RegressionThreshold::Statistical {
            confidence_level: 0.95,
        };

        assert!(matches!(absolute, RegressionThreshold::Absolute(100.0)));
        assert!(matches!(percentage, RegressionThreshold::Percentage(5.0)));
        assert!(matches!(
            statistical,
            RegressionThreshold::Statistical {
                confidence_level: 0.95
            }
        ));
    }

    #[test]
    fn test_health_status() {
        let good = HealthStatus::Good;
        let stable = HealthStatus::Stable;
        let poor = HealthStatus::Poor;

        assert!(matches!(good, HealthStatus::Good));
        assert!(matches!(stable, HealthStatus::Stable));
        assert!(matches!(poor, HealthStatus::Poor));
    }

    #[test]
    fn test_output_formats() {
        let formats = vec![
            OutputFormat::Html,
            OutputFormat::Json,
            OutputFormat::Csv,
            OutputFormat::Pdf,
        ];

        assert_eq!(formats.len(), 4);
        assert!(formats.contains(&OutputFormat::Html));
        assert!(formats.contains(&OutputFormat::Json));
    }
}