datasynth-audit-optimizer

Graph analysis and Monte Carlo simulation for audit FSM blueprints.

Overview

datasynth-audit-optimizer converts audit methodology blueprints from datasynth-audit-fsm into directed graphs for path analysis and stochastic simulation:

Graph conversion: Blueprint procedures → petgraph DiGraph with (procedure_id, state) nodes and transition edges
Shortest path: BFS per procedure — FSA: 27 minimum transitions, IA: 101
Constrained path: Must-visit procedures with transitive precondition expansion
Monte Carlo: N stochastic walks with outcome distribution analysis

Usage

use datasynth_audit_optimizer::shortest_path::analyze_shortest_paths;
use datasynth_audit_optimizer::monte_carlo::run_monte_carlo;
use datasynth_audit_fsm::loader::BlueprintWithPreconditions;

// Shortest path analysis
let bwp = BlueprintWithPreconditions::load_builtin_fsa().unwrap();
let report = analyze_shortest_paths(&bwp.blueprint);
println!("Min transitions: {}", report.total_minimum_transitions); // 27

// Monte Carlo simulation (100 iterations)
let mc = run_monte_carlo(&bwp, 100, 42);
println!("Avg events: {:.1}", mc.avg_events);
println!("Happy path: {}", mc.happy_path.join(" → "));

Monte Carlo Report

pub struct MonteCarloReport {
    pub iterations: usize,
    pub avg_events: f64,
    pub avg_duration_hours: f64,
    pub avg_procedures_completed: f64,
    pub bottleneck_procedures: Vec<(String, f64)>,   // top 5 by avg event count
    pub revision_hotspots: Vec<(String, f64)>,        // top 5 by avg revision count
    pub happy_path: Vec<String>,                       // most common completion order
}

Modules

Module	Purpose
`graph`	Blueprint → petgraph `DiGraph<StateNode, TransitionEdge>` conversion
`shortest_path`	BFS shortest path per procedure
`constrained`	Must-visit + precondition expansion path optimization
`monte_carlo`	Stochastic simulation with outcome distribution analysis
`report`	Human-readable report formatting

License