datasynth-audit-optimizer 2.2.0

Graph analysis, Monte Carlo simulation, and optimization for audit FSM blueprints
Documentation

datasynth-audit-optimizer

Graph analysis, Monte Carlo simulation, and optimization for audit FSM blueprints.

Overview

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

  • Graph conversion: Blueprint procedures to 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
  • Cross-firm benchmark comparison: Compare methodology coverage and efficiency across firms
  • ISA 600 group audit simulation: Component auditor assignment, materiality allocation, scope
  • Year-over-year engagement chains: Multi-period engagement simulation with carry-forward

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(" -> "));

Modules

Module Purpose
graph Blueprint to 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
resource_optimizer Budget/role-aware audit plan selection with coverage reporting
risk_scoping Standards/risk coverage analysis, what-if procedure removal impact
portfolio Multi-engagement simulation with shared resources and scheduling
conformance Fitness, precision, and anomaly detection statistics
overlay_fitting Iterative parameter search from target engagement profiles
discovery Blueprint inference from event logs (alpha miner variant)
calibration Anomaly injection rate auto-tuning to target detection difficulty
benchmark_comparison Cross-firm methodology comparison (coverage, efficiency, cost)
yoy_chain Year-over-year engagement chains with carry-forward findings
group_audit ISA 600 group audit simulation (components, materiality, scope)
blueprint_testing Automated blueprint validation and regression testing

License

Apache-2.0 - See LICENSE for details.