Model Checking Foundations in Real Flows: Debug Playbook

Debug playbook

Debug Playbook for Model Checking Foundations in Real Flows is anchored on First-pass property closure rate and median time-to-counterexample by property class.. Convert outcomes into assumption-aware, evidence-backed actions.

1. Freeze assumptions, RTL hash, and engine metadata.

2. Locate first divergence cycle and classify source.

3. Classify mechanism: model mismatch, weak property, setup issue, or RTL defect.

4. Apply one focused reproducer and one bounded fix.

5. Re-run sibling properties and critical covers before closure.

Review memo template

FORMAL REVIEW MEMO - Proof Engines & Convergence / Model Checking Foundations in Real Flows

1. Symptom
   - Failing metric: First-pass property closure rate and median time-to-counterexample by property class.
   - Trigger context: <mode/reset/env assumptions>
   - First divergence boundary: <model/property/rtl>

2. Mechanism hypothesis
   - Candidate mechanism: Formal model checking explores the state-transition graph implied by RTL and assumptions, proving or falsifying assertions without testbench stimulus. Modern tools blend SAT and SMT reasoning with symbolic exploration, switching among engines based on structure, cone-of-influence depth, and arithmetic complexity. Effective setup starts with clean reset semantics, explicit environment assumptions, and non-vacuous safety properties so engine effort is focused on reachable behavior rather than unconstrained noise.
   - Competing hypotheses: weak property, over-constraint, setup mismatch, rtl bug
   - Missing evidence: <trace, vacuity report, cover status>

3. Proposed action
   - Smallest reversible change: <assumption/property/rtl>
   - Expected movement: <closure quality, runtime, bug isolation>
   - Regression risk: hidden legal behavior, false pass, schedule churn

4. Signoff
   - Required artifact: Property readiness checklist including reset assumptions, vacuity status, and engine-selection notes.
   - Required owners: formal verification owner, rtl owner, methodology owner, verification lead
   - Final decision: close, bounded closure, rollback, or escalate

Formal deep dive

Convergence requires engine strategy, invariant quality, and model realism to move together with measurable progress.

Concept diagram

CONVERGENCE DECISION FLOW

property bucket -> engine strategy -> helper invariants -> convergence audit -> closure

Metric graph

CONVERGENCE BURNDOWN

open hard properties    ███████
inconclusive aging      █████
closed with audit       ████████

Metrics and artifacts to collect

• engine effectiveness by property class

• induction and helper-lemma success ratio

• stalled-property aging dashboard

• runtime vs closure-quality movement

Mini case study

A stalled set closed only after case-splitting by mode and auditing fairness assumptions for realism.

Debug branches

• Bucket properties by structure and intent before tuning.

• Inspect proof core stability, not runtime alone.

• Reject speed gains that reduce legal reachability.

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.

Debug ladder

Sequence: reproduce -> classify -> isolate first divergence -> patch -> revalidate sibling properties.

Avoid mixing assumption and RTL fixes in the same experiment.