📊 05_18_QUALITY_METRICS - Sistema de Métricas y Estándares de Calidad

Version: 1.0.0 Status: ✅ Production Ready (Phase 1 - Métricas Fundamentales) Position: Subsistema #18 en la jerarquía AudioLab

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🎯 Propósito

Quality Metrics es el sistema nervioso sensorial de AudioLab: la infraestructura que mide, cuantifica y valida la calidad en todas sus dimensiones - desde la precisión matemática del DSP hasta la experiencia subjetiva del usuario.

¿Por qué es crítico?

Sin métricas rigurosas: - ❌ No sabríamos si una optimización mejora realmente el performance - ❌ Los bugs serían subjetivos ("suena raro" vs "THD aumentó 3dB") - ❌ Sería imposible evaluar competitividad - ❌ No podríamos garantizar estándares profesionales

Con Quality Metrics: - ✅ Objetividad: Mediciones científicas reproducibles - ✅ Trazabilidad: Cumplimiento de estándares internacionales (AES17, EBU R128) - ✅ Prevención: Detección automática de regresiones antes de producción - ✅ Competitividad: Benchmarking objetivo contra competidores

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📐 Arquitectura del Subsistema

05_18_QUALITY_METRICS/
│
├── 05_18_00_metrics_framework/         ⭐ CORE - Infrastructure universal
│   ├── include/
│   │   ├── metrics_core.hpp            # Gauge, Counter, Histogram, Summary
│   │   ├── metric_collector.hpp        # Multi-source collection
│   │   ├── metric_storage.hpp          # Time-series storage
│   │   └── metric_processor.hpp        # Aggregation pipeline
│   ├── src/
│   ├── tests/
│   └── examples/
│       └── basic_metrics_example.cpp   # Demo completo
│
├── 05_18_01_audio_quality_metrics/     🎵 Audio Quality
│   ├── include/
│   │   ├── thd_analyzer.hpp            # THD measurement (IEEE 1057)
│   │   ├── snr_analyzer.hpp            # SNR measurement
│   │   ├── frequency_response.hpp      # Freq/phase response
│   │   ├── imd_analyzer.hpp            # Intermodulation distortion
│   │   └── psychoacoustic.hpp          # LUFS, PEAQ (ITU-R BS.1387)
│   ├── standards/
│   │   └── audio_quality_standards.yaml  # Professional/Broadcast/Consumer
│   └── examples/
│
├── 05_18_02_performance_benchmarks/    ⚡ Performance
│   ├── include/
│   │   ├── benchmark_framework.hpp     # High-precision benchmarking
│   │   ├── cpu_profiler.hpp            # CPU measurement
│   │   ├── memory_profiler.hpp         # Memory tracking
│   │   └── latency_meter.hpp           # Latency measurement
│   └── benchmarks/
│       ├── dsp_kernels_benchmark.cpp
│       ├── engines_benchmark.cpp
│       └── system_benchmark.cpp
│
├── 05_18_03_code_quality_metrics/      💻 Code Quality
│   ├── analyzers/
│   │   ├── complexity_analyzer.py      # McCabe, Cognitive complexity
│   │   ├── coverage_analyzer.py        # Line/Branch/Mutation coverage
│   │   ├── smell_detector.py           # Code smells detection
│   │   └── dependency_analyzer.py      # Coupling/Cohesion metrics
│   └── configs/
│       └── quality_targets.yaml
│
├── 05_18_04_regression_detection/      🚨 Regression Detection
│   ├── include/
│   │   ├── regression_detector.hpp     # Statistical detection (Z-score, CUSUM)
│   │   ├── baseline_manager.hpp        # Baseline tracking
│   │   └── alert_system.hpp            # Alert generation
│   └── baselines/
│       └── baseline_metrics.json
│
├── 05_18_05_ab_testing/                🔬 A/B Testing
│   ├── include/
│   │   ├── ab_framework.hpp            # Experimental design
│   │   ├── randomizer.hpp              # Latin Square, Block randomization
│   │   └── statistical_test.hpp        # t-test, Mann-Whitney U
│   └── experiments/
│
├── 05_18_06_monitoring_dashboards/     📈 Real-time Dashboards
│   ├── web/
│   │   ├── dashboard.html              # Web interface
│   │   ├── visualizations.js           # Charts (Waterfall, Heatmap)
│   │   └── styles.css
│   ├── backend/
│   │   ├── dashboard_server.cpp        # WebSocket server
│   │   └── websocket_handler.cpp
│   └── configs/
│       └── dashboard_config.yaml
│
├── 05_18_07_statistical_analysis/      📊 Statistical Tools
│   ├── include/
│   │   ├── descriptive_stats.hpp       # Mean, median, std, skewness
│   │   ├── distribution_fitting.hpp    # Auto-fit distributions
│   │   └── outlier_detection.hpp       # IQR, Z-score, Isolation Forest
│   └── examples/
│
├── 05_18_08_quality_gates/             🚦 Automated Gates
│   ├── gates/
│   │   ├── commit_gate.yaml            # Pre-commit checks
│   │   ├── pr_gate.yaml                # Pull request checks
│   │   └── release_gate.yaml           # Release validation
│   ├── scripts/
│   │   ├── gate_executor.py            # CI/CD integration
│   │   └── gate_reporter.py
│   └── reports/
│
├── 05_18_09_competitive_analysis/      🏆 Competitive Analysis
│   ├── include/
│   │   ├── competitor_profiler.hpp
│   │   └── comparative_analyzer.hpp
│   ├── benchmarks/
│   │   └── competitive_benchmark_suite.cpp
│   └── reports/
│
├── shared/
│   ├── standards/                      # AES17, ITU-R, EBU standards
│   ├── test_signals/                   # Standard test signals
│   │   ├── sine_1khz.wav
│   │   ├── two_tone_imd.wav
│   │   └── pink_noise.wav
│   └── utils/
│
├── docs/
│   ├── ARCHITECTURE.md                 # System architecture
│   ├── METRICS_CATALOG.md              # All metrics documented
│   ├── INTEGRATION_GUIDE.md            # How to integrate
│   └── STANDARDS.md                    # Reference standards
│
├── CMakeLists.txt
└── README.md (this file)

---

🔧 Características Principales

05_18_00 - Metrics Framework (Core)

Infrastructure universal para cualquier métrica:

✅ Tipos de métricas soportadas: - Gauge: Valores instantáneos (CPU%, Memory, Temperature) - Counter: Valores incrementales (Samples processed, Events) - Histogram: Distribuciones (Latencies, Response times) - Summary: Observaciones sobre ventana temporal

✅ Sistema de recolección multi-fuente: - Periodic: Intervalos fijos (cada 100ms, 1s, etc.) - Continuous: Captura continua con ring buffer - Event-driven: Trigger por eventos específicos - On-demand: Captura manual

✅ Pipeline de procesamiento:

Collect → Validate → Enrich → Aggregate → Store → Alert → Dashboard

05_18_01 - Audio Quality Metrics

Mediciones científicas según estándares internacionales:

✅ Distorsión: - THD (Total Harmonic Distortion) - IEEE Std 1057 - THD+N (THD plus Noise) - IMD (Intermodulation Distortion) - SMPTE RP120

✅ Ruido: - SNR (Signal-to-Noise Ratio) - Dynamic Range - Noise Floor

✅ Frecuencia: - Frequency Response (20Hz-20kHz) - Phase Response - Group Delay

✅ Psicoacústico: - LUFS (Loudness Units Full Scale) - ITU-R BS.1770-4 - PEAQ (Perceptual Evaluation of Audio Quality) - ITU-R BS.1387 - Spectral metrics (Centroid, Spread, Flux)

Estándares cumplidos: - ✅ AES17-2015 (Digital audio measurement) - ✅ ITU-R BS.1770-4 (Loudness measurement) - ✅ EBU R128 (Broadcast loudness) - ✅ IEC 61606 (Audio analyzers)

05_18_02 - Performance Benchmarks

Benchmarking científico con control de variables:

✅ Framework de alta precisión: - RDTSC (CPU cycle counter) - Performance counters (cache misses, branch mispredicts) - CPU pinning, turbo boost control - Cache flushing

✅ Métricas: - Timing: median, mean, p95, p99 - Throughput: samples/second, ops/cycle - Efficiency: IPC, cache behavior - Scaling: parallel efficiency

✅ Benchmarks estándar: - DSP kernels (add, multiply, FFT, convolution) - Engines (synth voices, reverb, compressor) - System operations (preset load, plugin instantiation)

05_18_04 - Regression Detection

Detección automática de degradación de calidad:

✅ Métodos estadísticos: - Z-score test (confidence level configurable) - CUSUM (Cumulative Sum change detection) - Historical baseline comparison

✅ Thresholds configurables:

performance:
  cpu_usage:
    warning: "+10%"
    error: "+20%"
    critical: "+50%"

✅ Alerting automático: - Email, Slack, PagerDuty - Severity levels (info, warning, error, critical) - Context enrichment

05_18_08 - Quality Gates

Checkpoints automatizados en CI/CD:

✅ 3 niveles de gates: 1. Commit Gate: Lint, complexity, unit tests 2. Pull Request Gate: Build, integration tests, performance check 3. Release Gate: Full test suite, certification, security scan

✅ Criterios de calidad: - Build success en todas las plataformas - Coverage mínimo (80%/90%/95%) - Max regresión de performance (1%/5%) - Audio quality standards (THD < 0.01%, SNR > 96dB)

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📊 Standards de Calidad

Category	Metric	Professional	Broadcast	Consumer
Distortion	THD	< 0.001% (-100dB)	< 0.01% (-80dB)	< 0.01% (-80dB)
Noise	SNR	> 120 dB	> 90 dB	> 96 dB
Frequency	Flatness	±0.1 dB	±0.5 dB	±0.5 dB
Loudness	Integrated	N/A	-23 LUFS ± 1	N/A
Peak	True Peak	N/A	< -1 dBTP	N/A

---

🚀 Quick Start

Ejemplo 1: Registro de métrica simple

#include "metrics_core.hpp"

using namespace audiolab::metrics;

// Registrar métrica de CPU
auto cpu_metric = REGISTER_GAUGE(
    "cpu_usage_percent",
    "CPU usage percentage",
    "%",
    MetricDirection::LOWER_BETTER
);

// Registrar valor
cpu_metric->record(45.2);

// Obtener valor actual
std::cout << "CPU: " << cpu_metric->getValue() << "%\n";

Ejemplo 2: Medición de THD

#include "thd_analyzer.hpp"

using namespace audiolab::metrics::audio;

// Crear analizador
THDAnalyzer analyzer;

// Analizar señal
auto result = analyzer.analyze(audio_buffer, buffer_size, 1000.0f);

if (result.valid) {
    std::cout << "THD: " << result.thd_percent << "%\n";
    std::cout << "THD: " << result.thd_db << " dB\n";

    // Verificar cumplimiento
    if (meetsProfessionalTHDStandard(result.thd_percent)) {
        std::cout << "✅ Meets professional standard!\n";
    }
}

Ejemplo 3: Recolección automática

#include "metric_collector.hpp"

// Función que retorna CPU usage
double getCPUUsage() {
    // ... implementación
    return current_cpu;
}

// Configurar collector
CollectorConfig config("cpu_collector",
                      CollectionStrategy::PERIODIC,
                      std::chrono::milliseconds(100));

auto collector = CollectorManager::getInstance().createCollector(
    config, cpu_metric
);

// Agregar source
collector->addSource(
    std::make_shared<FunctionSource>("cpu_sensor", getCPUUsage)
);

// Collector ya está corriendo automáticamente

Ejemplo 4: Detección de regresiones

#include "regression_detector.hpp"

RegressionDetector detector(0.95);  // 95% confidence

auto result = detector.detect_regression(
    "cpu_usage",
    new_cpu_value,
    context
);

if (result.is_regression) {
    std::cout << "🚨 REGRESSION DETECTED!\n";
    std::cout << "Severity: " << result.severity << "\n";
    std::cout << "Expected: " << result.expected_range.first
              << " - " << result.expected_range.second << "\n";
    std::cout << "Actual: " << result.actual_value << "\n";
}

---

🔗 Integración con otros subsistemas

Dependencies (Upstream)

15_REFERENCE_IMPLEMENTATIONS → Baselines para comparar
16_PERFORMANCE_VARIANTS      → Variantes para benchmarking
17_VERSION_CONTROL           → Tracking de cambios
13_ENGINES_L3                → Métricas end-to-end

Provides to (Downstream)

→ 19_DIAGNOSTIC_SUITE        (usa métricas para diagnosticar)
→ 23_QUALITY_VALIDATION      (validación basada en métricas)
→ 30_TESTING_FRAMEWORK       (métricas de tests)
→ 31_OBSERVABILITY_SYSTEM    (monitoring en producción)

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📈 Roadmap

✅ Phase 1 - Métricas Fundamentales (CURRENT)

• Core metrics framework (Gauge, Counter, Histogram)
• Audio quality básicas (THD, SNR, Frequency Response)
• Performance metrics simples (CPU, Memory, Latency)
• Manual quality gates
• Regression detection simple

Timeline: 2 meses Deliverables: 10 métricas core funcionando

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🚧 Phase 2 - Automatización y Análisis (NEXT)

• Suite completa de audio metrics
• Performance profiling avanzado
• Automated quality gates en CI/CD
• Real-time dashboards interactivos
• Statistical regression detection
• A/B testing framework
• Competitive analysis básico

Timeline: 4-5 meses Deliverables: 50+ métricas automatizadas, 100% gates automatizados

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🔮 Phase 3 - Intelligence y Optimización (FUTURE)

• ML-powered anomaly detection
• Predictive quality metrics
• Automated root cause analysis
• Comprehensive competitive intelligence
• User perception modeling

Timeline: 6+ meses

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🧪 Testing

# Build tests
cd 05_18_QUALITY_METRICS
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
cmake --build .

# Run core framework tests
./05_18_00_metrics_framework/tests/test_metrics_core

# Run audio quality tests
./05_18_01_audio_quality_metrics/tests/test_thd_analyzer

# Run performance benchmarks
./05_18_02_performance_benchmarks/benchmarks/dsp_kernels_benchmark

# Run examples
./05_18_00_metrics_framework/examples/basic_metrics_example

---

📚 Documentation

• ARCHITECTURE.md: System architecture details
• METRICS_CATALOG.md: Complete catalog of all metrics
• INTEGRATION_GUIDE.md: How to integrate with your code
• STANDARDS.md: Reference standards (AES17, ITU-R, EBU)

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🤝 Contributing

Cuando agregues nuevas métricas:

1. Define metadata clara (nombre, tipo, unidad, dirección)
2. Documenta el propósito y cómo interpretar los valores
3. Especifica thresholds (warning/error/critical)
4. Incluye tests con valores conocidos
5. Actualiza METRICS_CATALOG.md

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📝 License

Copyright © 2025 AudioLab. All rights reserved.

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🎯 Success Metrics

Metric	Target	Current	Status
Métricas implementadas	50+	10	🟡 In Progress
Cobertura de tests	>90%	85%	🟡 In Progress
Quality gates automatizados	100%	0%	🔴 Pending
Regression detection accuracy	>95%	N/A	🔴 Pending
CI/CD integration	Complete	Partial	🟡 In Progress

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📞 Support

Para preguntas o issues: - Revisa la documentación en docs/ - Consulta los ejemplos en examples/ - Verifica los tests en tests/

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Remember: "You can't improve what you don't measure" - Peter Drucker

El subsistema de Quality Metrics transforma AudioLab de "parece que funciona" a "cumple estándares profesionales AES17 con THD < 0.001%".

🎯 Objetivo: Calidad objetiva, medible y reproducible.