pmat 3.11.0

//! Performance Testing per SPECIFICATION.md Section 30
//! 
//! This module implements comprehensive performance testing that validates
//! the performance characteristics defined in SPECIFICATION.md Section 1.4.
//! Tests are designed to fail if performance degrades beyond acceptable thresholds.

use std::time::{Duration, Instant};
use std::path::PathBuf;
use tempfile::tempdir;
use std::fs;
use pmat::cli::handlers::complexity_handlers;
use pmat::services::complexity::ComplexityAnalysisService;
use anyhow::Result;

/// Performance characteristics from SPECIFICATION.md Section 1.4
#[derive(Debug, Clone)]
pub struct PerformanceTargets {
    /// Startup latency targets
    pub startup_cold_ms: u64,     // 127ms max
    pub startup_hot_ms: u64,      // 4ms max
    
    /// Analysis throughput targets  
    pub loc_per_sec_st: u64,      // 487,000 LOC/s single-threaded
    pub loc_per_sec_mt: u64,      // 3,921,000 LOC/s multi-threaded
    
    /// Memory usage targets
    pub base_rss_mb: u64,         // 47MB base
    pub per_kloc_kb: u64,         // 312KB per KLOC
}

impl Default for PerformanceTargets {
    fn default() -> Self {
        Self {
            startup_cold_ms: 127,
            startup_hot_ms: 4,
            loc_per_sec_st: 487_000,
            loc_per_sec_mt: 3_921_000,
            base_rss_mb: 47,
            per_kloc_kb: 312,
        }
    }
}

/// Generate test code with specified lines of code
fn generate_test_code(lines: usize) -> String {
    let mut code = String::with_capacity(lines * 50);
    code.push_str("// Generated test code for performance testing\n");
    code.push_str("use std::collections::HashMap;\n\n");
    code.push_str("pub struct TestStruct {\n");
    code.push_str("    data: HashMap<String, i32>,\n");
    code.push_str("}\n\n");
    
    for i in 0..lines.saturating_sub(10) {
        code.push_str(&format!("pub fn test_function_{i}() -> i32 {{\n"));
        code.push_str(&format!("    let mut sum = 0;\n"));
        code.push_str(&format!("    for j in 0..{i} {{\n"));
        code.push_str(&format!("        sum += j * {i};\n"));
        code.push_str(&format!("    }}\n"));
        code.push_str(&format!("    sum\n"));
        code.push_str(&format!("}}\n\n"));
    }
    
    code
}

/// Test cold startup performance (first-time initialization)
#[test]
pub async fn test_cold_startup_performance() -> Result<()> {
    let targets = PerformanceTargets::default();
    let start = Instant::now();
    
    // Simulate cold startup by creating a new analysis service
    let _service = ComplexityAnalysisService::new();
    
    let duration = start.elapsed();
    let duration_ms = duration.as_millis() as u64;
    
    assert!(
        duration_ms <= targets.startup_cold_ms,
        "Cold startup took {}ms, expected ≤{}ms (SPECIFICATION.md target)",
        duration_ms, targets.startup_cold_ms
    );
    
    println!("✅ Cold startup: {}ms (target: ≤{}ms)", duration_ms, targets.startup_cold_ms);
    Ok(())
}

/// Test hot startup performance (subsequent initializations)
#[test]
pub async fn test_hot_startup_performance() -> Result<()> {
    let targets = PerformanceTargets::default();
    
    // Warm up
    let _service1 = ComplexityAnalysisService::new();
    
    // Measure hot startup
    let start = Instant::now();
    let _service2 = ComplexityAnalysisService::new();
    let duration = start.elapsed();
    let duration_ms = duration.as_millis() as u64;
    
    assert!(
        duration_ms <= targets.startup_hot_ms,
        "Hot startup took {}ms, expected ≤{}ms (SPECIFICATION.md target)",
        duration_ms, targets.startup_hot_ms
    );
    
    println!("✅ Hot startup: {}ms (target: ≤{}ms)", duration_ms, targets.startup_hot_ms);
    Ok(())
}

/// Test single-threaded analysis throughput
#[tokio::test]
async fn test_single_threaded_throughput() -> Result<()> {
    let targets = PerformanceTargets::default();
    let test_lines = 10_000; // 10K LOC test
    
    // Create test file
    let temp_dir = tempdir()?;
    let test_file = temp_dir.path().join("test.rs");
    let test_code = generate_test_code(test_lines);
    fs::write(&test_file, &test_code)?;
    
    // Measure analysis time
    let start = Instant::now();
    
    complexity_handlers::handle_analyze_complexity(
        &temp_dir.path().to_path_buf(),
        None, // single file
        20,   // cyclomatic threshold
        15,   // cognitive threshold
        &pmat::cli::enums::ComplexityOutputFormat::Json,
        Some(test_file.clone()),
        None, // include
        None, // exclude
        None, // output
        false, // fail_on_violation
    ).await?;
    
    let duration = start.elapsed();
    let actual_throughput = (test_lines as f64) / duration.as_secs_f64();
    
    assert!(
        actual_throughput >= targets.loc_per_sec_st as f64 * 0.8, // 80% of target
        "Single-threaded throughput: {:.0} LOC/s, expected ≥{} LOC/s",
        actual_throughput, targets.loc_per_sec_st
    );
    
    println!("✅ Single-threaded throughput: {:.0} LOC/s (target: ≥{} LOC/s)", 
             actual_throughput, targets.loc_per_sec_st);
    
    Ok(())
}

/// Test analysis performance with realistic project size
#[tokio::test]
async fn test_realistic_project_analysis() -> Result<()> {
    let test_lines = 50_000; // 50K LOC project
    
    // Create test project structure
    let temp_dir = tempdir()?;
    let src_dir = temp_dir.path().join("src");
    fs::create_dir(&src_dir)?;
    
    // Create multiple files to simulate realistic project
    for i in 0..10 {
        let file_path = src_dir.join(format!("module_{}.rs", i));
        let file_code = generate_test_code(test_lines / 10);
        fs::write(&file_path, &file_code)?;
    }
    
    let start = Instant::now();
    
    complexity_handlers::handle_analyze_complexity(
        &temp_dir.path().to_path_buf(),
        None, // all files
        20,   // cyclomatic threshold
        15,   // cognitive threshold
        &pmat::cli::enums::ComplexityOutputFormat::Summary,
        None, // project root
        None, // include
        None, // exclude
        None, // output
        false, // fail_on_violation
    ).await?;
    
    let duration = start.elapsed();
    let actual_throughput = (test_lines as f64) / duration.as_secs_f64();
    
    // More lenient threshold for multi-file analysis due to I/O overhead
    let min_throughput = 100_000; // 100K LOC/s
    assert!(
        actual_throughput >= min_throughput as f64,
        "Multi-file analysis throughput: {:.0} LOC/s, expected ≥{} LOC/s",
        actual_throughput, min_throughput
    );
    
    println!("✅ Multi-file analysis: {:.0} LOC/s, duration: {:?}", 
             actual_throughput, duration);
    
    Ok(())
}

/// Test memory usage patterns during analysis
#[tokio::test]
async fn test_memory_usage_patterns() -> Result<()> {
    let test_lines = 20_000; // 20K LOC test
    
    // Create test file
    let temp_dir = tempdir()?;
    let test_file = temp_dir.path().join("memory_test.rs");
    let test_code = generate_test_code(test_lines);
    fs::write(&test_file, &test_code)?;
    
    // Get initial memory usage (approximate)
    let initial_memory = get_memory_usage_mb();
    
    // Run analysis
    complexity_handlers::handle_analyze_complexity(
        &temp_dir.path().to_path_buf(),
        None,
        20,
        15,
        &pmat::cli::enums::ComplexityOutputFormat::Json,
        Some(test_file),
        None,
        None,
        None,
        false,
    ).await?;
    
    let final_memory = get_memory_usage_mb();
    let memory_used = final_memory.saturating_sub(initial_memory);
    
    // Memory usage should be reasonable for 20K LOC
    let expected_memory_mb = 10; // Conservative estimate
    assert!(
        memory_used <= expected_memory_mb,
        "Memory usage: {}MB for {}K LOC, expected ≤{}MB",
        memory_used, test_lines / 1000, expected_memory_mb
    );
    
    println!("✅ Memory usage: {}MB for {}K LOC", memory_used, test_lines / 1000);
    
    Ok(())
}

/// Test performance regression detection
#[tokio::test]
async fn test_performance_regression_detection() -> Result<()> {
    const ITERATIONS: usize = 5;
    let test_lines = 5_000; // Smaller test for multiple iterations
    
    // Create test file
    let temp_dir = tempdir()?;
    let test_file = temp_dir.path().join("regression_test.rs");
    let test_code = generate_test_code(test_lines);
    fs::write(&test_file, &test_code)?;
    
    let mut durations = Vec::with_capacity(ITERATIONS);
    
    // Run multiple iterations to detect performance variance
    for _ in 0..ITERATIONS {
        let start = Instant::now();
        
        complexity_handlers::handle_analyze_complexity(
            &temp_dir.path().to_path_buf(),
            None,
            20,
            15,
            &pmat::cli::enums::ComplexityOutputFormat::Json,
            Some(test_file.clone()),
            None,
            None,
            None,
            false,
        ).await?;
        
        durations.push(start.elapsed());
    }
    
    // Calculate statistics
    let avg_duration = durations.iter().sum::<Duration>() / ITERATIONS as u32;
    let max_duration = durations.iter().max().unwrap();
    let min_duration = durations.iter().min().unwrap();
    
    // Performance should be consistent (max ≤ 2x min)
    let variance_ratio = max_duration.as_millis() as f64 / min_duration.as_millis() as f64;
    assert!(
        variance_ratio <= 2.0,
        "High performance variance: min={}ms, max={}ms, ratio={:.2}",
        min_duration.as_millis(), max_duration.as_millis(), variance_ratio
    );
    
    println!("✅ Performance consistency: avg={}ms, min={}ms, max={}ms", 
             avg_duration.as_millis(), min_duration.as_millis(), max_duration.as_millis());
    
    Ok(())
}

/// Test large file handling performance
#[tokio::test]
async fn test_large_file_performance() -> Result<()> {
    let test_lines = 100_000; // 100K LOC single file
    
    // Create large test file
    let temp_dir = tempdir()?;
    let test_file = temp_dir.path().join("large_file.rs");
    let test_code = generate_test_code(test_lines);
    fs::write(&test_file, &test_code)?;
    
    let start = Instant::now();
    
    complexity_handlers::handle_analyze_complexity(
        &temp_dir.path().to_path_buf(),
        None,
        20,
        15,
        &pmat::cli::enums::ComplexityOutputFormat::Summary,
        Some(test_file),
        None,
        None,
        None,
        false,
    ).await?;
    
    let duration = start.elapsed();
    
    // Large files should still be processed reasonably quickly
    let max_duration_secs = 5; // 5 seconds max for 100K LOC
    assert!(
        duration.as_secs() <= max_duration_secs,
        "Large file analysis took {}s, expected ≤{}s for 100K LOC",
        duration.as_secs(), max_duration_secs
    );
    
    let throughput = (test_lines as f64) / duration.as_secs_f64();
    println!("✅ Large file performance: {:.0} LOC/s, duration: {:?}", throughput, duration);
    
    Ok(())
}

/// Approximate memory usage in MB (platform-specific)
fn get_memory_usage_mb() -> u64 {
    #[cfg(target_os = "linux")]
    {
        use std::fs;
        if let Ok(status) = fs::read_to_string("/proc/self/status") {
            for line in status.lines() {
                if line.starts_with("VmRSS:") {
                    if let Some(kb_str) = line.split_whitespace().nth(1) {
                        if let Ok(kb) = kb_str.parse::<u64>() {
                            return kb / 1024; // Convert KB to MB
                        }
                    }
                }
            }
        }
    }
    
    // Fallback for other platforms or if reading fails
    0
}

/// Performance test configuration
pub struct PerformanceTestConfig {
    pub enable_regression_tests: bool,
    pub enable_memory_tests: bool,
    pub enable_throughput_tests: bool,
    pub test_iterations: usize,
}

impl Default for PerformanceTestConfig {
    fn default() -> Self {
        Self {
            enable_regression_tests: true,
            enable_memory_tests: true,
            enable_throughput_tests: true,
            test_iterations: 3,
        }
    }
}

/// Run comprehensive performance test suite
pub async fn run_performance_test_suite(config: PerformanceTestConfig) -> Result<()> {
    println!("🏃 Running PMAT Performance Test Suite (SPECIFICATION.md Section 30)");
    println!("================================================================");
    
    if config.enable_throughput_tests {
        println!("\n📊 Throughput Tests:");
        test_single_threaded_throughput().await?;
        test_realistic_project_analysis().await?;
        test_large_file_performance().await?;
    }
    
    if config.enable_regression_tests {
        println!("\n🔍 Regression Tests:");
        test_performance_regression_detection().await?;
    }
    
    if config.enable_memory_tests {
        println!("\n💾 Memory Tests:");
        test_memory_usage_patterns().await?;
    }
    
    println!("\n✅ All performance tests passed!");
    println!("Performance characteristics meet SPECIFICATION.md Section 1.4 requirements");
    
    Ok(())
}

#[cfg(test)]
mod performance_specification_tests {
    use super::*;
    
    /// Integration test that runs the full performance suite
    #[tokio::test]
    async fn test_specification_performance_suite() -> Result<()> {
        let config = PerformanceTestConfig::default();
        run_performance_test_suite(config).await
    }
}