Module behavioral_cloning 

Behavioral Cloning of Backends

This module provides functionality to learn from recorded traffic and create realistic mock behavior that captures the “personality” of the real backend.

Features

• Sequence Learning: Discover and model multi-step flows from real traffic
• Probabilistic Outcomes: Model probabilities of errors, latency, and edge cases per endpoint
• Rare Edge Amplification: Option to increase rare error frequency for testing

Example Usage

use mockforge_core::behavioral_cloning::{
    SequenceLearner, ProbabilisticModel, EdgeAmplifier,
};

async fn example(database: &impl mockforge_core::behavioral_cloning::TraceQueryProvider) -> mockforge_core::Result<()> {
    // Learn sequences from recorded traffic
    let sequences = SequenceLearner::discover_sequences_from_traces(database).await?;

    // Build probability models for endpoints
    let model = ProbabilisticModel::build_probability_model(
        database,
        "/api/users",
        "GET"
    ).await?;

    // Sample a status code based on learned distribution
    let status_code = model.sample_status_code();

    // Amplify rare errors for testing
    let amplifier = EdgeAmplifier::new();
    amplifier.apply_amplification(&mut model.clone(), 0.5)?; // 50% frequency
    Ok(())
}

Re-exports

pub use edge_amplifier::EdgeAmplifier;

pub use probabilistic_model::ProbabilisticModel;

pub use sequence_learner::SequenceLearner;

pub use sequence_learner::TraceQueryProvider;

pub use sequence_learner::TraceRequest;

pub use types::AmplificationScope;

pub use types::BehavioralSequence;

pub use types::EdgeAmplificationConfig;

pub use types::EndpointProbabilityModel;

pub use types::ErrorPattern;

pub use types::LatencyDistribution;

pub use types::PayloadVariation;

pub use types::SequenceStep;

Modules

edge_amplifier

Rare edge case amplification

probabilistic_model

Probabilistic outcome modeling

sequence_learner

Sequence learning from recorded traffic

types

Behavioral cloning type definitions