Formal vs Simulation: Exhaustive Proof and Stimulus-Based Search: Interview Drills

Interview drills

Interview Drills for Formal vs Simulation: Exhaustive Proof and Stimulus-Based Search is anchored on non-vacuous closure rate, counterexample turnaround, and residual-risk trend by requirement class. Convert outcomes into assumption-aware, evidence-backed actions.

PROMPT
You observe regression in non-vacuous closure rate, counterexample turnaround, and residual-risk trend by requirement class for Formal vs Simulation: Exhaustive Proof and Stimulus-Based Search. Explain root cause and signoff decision.

STRONG ANSWER
1. Defines requirement context and first divergence.
2. Explains mechanism: Simulation validates behavior for sampled traces created by directed and constrained-random stimulus, so its confidence depends on test quality, scenario coverage, and seed diversity.
3. Requests proving artifact: closure packet for Formal vs Simulation: Exhaustive Proof and Stimulus-Based Search: assumptions audit, proof status matrix, and replay-ready divergence trace
4. Proposes bounded fix + owner + rollback-safe validation.

WEAK ANSWER
Gives generic formal advice without model boundaries, proof quality, or ownership.

Formal deep dive

FPV foundations are reliable only when assumptions, reset semantics, and requirement intent are explicitly modeled and audited.

Concept diagram

FPV FOUNDATION LOOP

requirements -> property set -> assumptions and reset model -> prove/fail traces -> closure audit

Metric graph

FOUNDATION HEALTH

vacuous passes         ████
reachable proofs       ███████
inconclusive backlog   █████
reopened properties    ███

Metrics and artifacts to collect

• assumption traceability matrix

• vacuity and reachability status

• proof core relevance summary

• counterexample classification trend

Mini case study

A green-looking run was invalidated after legal-mode covers failed, exposing assumptions that removed realistic traffic.

Debug branches

• Validate requirement-to-property mapping before tuning runtime.

• Check legal scenario reachability after every assumption change.

• Classify first divergence as model issue or RTL bug.

Senior review question

Ask: which requirement intent is proven, under which assumptions, and what residual risk remains?

Key takeaways

• Tie each proof claim to assumption boundaries and reachability evidence.

• Prefer minimal reversible fixes and preserve legal behavior visibility.

Common pitfalls

• Treating runtime reduction as proof-quality improvement without audits.

• Declaring closure while critical covers remain unreachable.

• Using broad waivers instead of first-divergence root-cause ownership.

Principal formal review addendum

Formal vs Simulation: Exhaustive Proof and Stimulus-Based Search should be reviewed as a requirement-evidence workflow, not a single status report.

Use non-vacuous closure rate, counterexample turnaround time, and requirement-level residual risk trend as the monitoring lens and formal closure packet: assumptions audit, proof status matrix, counterexample classification, and requirement traceability as closure proof.

Formal foundations are strongest when assumptions, reset semantics, and requirement intent are all explicit and reviewable. Strong teams preserve legal reachability while improving convergence.