Core Concepts

Predictive Models

Advanced machine learning that sees the future of your software

15+
Model Types
2-48h
Prediction Window
94%
Accuracy Rate

Model Architecture

Autonoma uses an ensemble of specialized machine learning models, each trained to detect specific types of issues. This multi-model approach ensures high accuracy across diverse problem domains while maintaining the ability to adapt to your unique codebase.

Time-Series Models

Forecast future system behavior based on historical patterns and trends.

  • • LSTM networks for sequence prediction
  • • Prophet for seasonal patterns
  • • ARIMA for trend analysis
  • • Transformer models for complex patterns

Anomaly Detection

Identify unusual patterns that indicate potential issues.

  • • Isolation Forest for outliers
  • • Autoencoders for pattern deviation
  • • One-class SVM for novelty detection
  • • Statistical process control

Code Intelligence

Understand code structure and predict impact of changes.

  • • Graph Neural Networks for dependencies
  • • CodeBERT for semantic understanding
  • • Tree-LSTM for AST analysis
  • • Attention mechanisms for focus

Performance Models

Predict performance degradation and resource constraints.

  • • Gradient Boosting for regression
  • • Random Forest for classification
  • • Neural ODE for continuous dynamics
  • • Bayesian models for uncertainty

Model Training Process

1

Data Collection & Preparation

Autonoma collects and processes various data sources to train accurate models:

Static Data

  • • Code structure and complexity
  • • Dependency graphs
  • • Git history and changes
  • • Documentation and comments

Dynamic Data

  • • Runtime metrics and logs
  • • Performance indicators
  • • Error rates and types
  • • User behavior patterns
2

Feature Engineering

Raw data is transformed into meaningful features that models can learn from:

Code Features

Cyclomatic complexity, coupling metrics, code churn, technical debt indicators

Temporal Features

Time-based patterns, seasonality, trend components, lag features

Behavioral Features

API call patterns, resource usage profiles, user interaction sequences

3

Training & Validation

Models are trained using advanced techniques to ensure accuracy and generalization:

# Example training pipeline
pipeline = ModelPipeline(
  preprocessor=AutoPreprocessor(),
  models=[
    LSTMPredictor(sequence_length=168),  # 1 week
    IsolationForest(contamination=0.01),
    GradientBoostingRegressor(n_estimators=1000),
  ],
  ensemble_method='weighted_voting',
  validation_strategy='time_series_cv',
  metrics=['precision', 'recall', 'lead_time']
)

# Continuous learning
pipeline.enable_online_learning(
  update_frequency='hourly',
  drift_detection=True,
  retraining_threshold=0.05
)

Specialized Models

Memory Leak Detection

Specialized models that identify memory leak patterns before they cause outages.

Detection Signals

  • • Monotonic memory growth
  • • Heap allocation patterns
  • • GC frequency changes
  • • Object retention graphs

Model Performance

  • • 96% detection accuracy
  • • 2-6 hour lead time
  • • Less than 1% false positive rate
  • • 85% root cause accuracy

Security Vulnerability Prediction

Identifies code patterns that commonly lead to security vulnerabilities.

Pattern Recognition

Trained on millions of vulnerability patterns from CVE database, GitHub Security Advisories, and security audit reports.

Code Analysis

Deep semantic analysis of code flow, input validation, authentication patterns, and data handling practices.

Performance Degradation Forecast

Predicts when performance will degrade below acceptable thresholds.

{
  "prediction": {
    "type": "performance_degradation",
    "metric": "p95_response_time",
    "current_value": 145,
    "predicted_value": 850,
    "threshold": 200,
    "time_to_breach": "3h 24m",
    "confidence": 0.91,
    "contributing_factors": [
      {
        "factor": "database_connection_pool",
        "impact": 0.45,
        "current": 78,
        "predicted": 95
      },
      {
        "factor": "cache_hit_rate", 
        "impact": 0.35,
        "current": 0.92,
        "predicted": 0.41
      }
    ]
  }
}

Model Performance & Accuracy

Real-World Performance Metrics

94%

Overall Accuracy

2.5h

Avg Lead Time

0.8%

False Positive Rate

97%

Fix Success Rate

Accuracy by Issue Type

Memory Leaks96%
Performance Issues93%
Security Vulnerabilities91%
Stability Issues95%

Continuous Learning & Adaptation

Real-time Updates

Models continuously learn from new data and feedback.

  • •Hourly incremental updates
  • •Daily model validation
  • •Weekly full retraining
  • •Automatic drift detection

Feedback Loop

Every prediction and fix improves future accuracy.

  • •Track prediction outcomes
  • •Learn from false positives
  • •Incorporate user feedback
  • •Adapt to code changes

Model Transparency: Autonoma provides explainable AI features that show why each prediction was made, what factors contributed most, and how confident the model is. This transparency helps build trust and enables better decision-making.

Learn More About Autonoma

Explore how predictions turn into automatic fixes

Remediation EngineLearning System