Power Bug Triage: Expanded Case Study

Extended case study

A regression tied to Power Bug Triage appears after power-intent or PMU sequence updates.

Background

Previous baseline was stable. New low-power behavior improved one mode but introduced unstable corner behavior in transition-heavy tests.

Symptoms observed

• illegal transition count, corruption incidence, and reproducibility of low-power regressions across fixed seeds worsens under stressed transition sequences

• same testcase can pass in functional mode but fail in power-aware mode

• teams disagree whether issue is intent, RTL, firmware, or checker noise

Investigation timeline

1. Hour 0: freeze test seed, intent revision, RTL commit, and PMU configuration tags.

2. Hour 1: collect transition timeline and assertion failures around first symptom.

3. Hour 2: classify failure mode and narrow candidate boundaries.

4. Hour 3: create smallest reproducer with explicit phase and crossing visibility.

5. Hour 4: apply one reversible fix and rerun focused LPV tests.

6. Hour 5: run broader regression subset for blast-radius confidence.

7. Hour 6: publish closure packet and update guardrail checks.

Root cause

Root cause traced to Power Bug Triage: Low-power bug triage must start from first power-intent divergence, not the final scoreboard mismatch, because symptoms can appear thousands of cycles after the causative transition.

Fix and validation

• Make transition and control ownership explicit at the failing boundary.

• Add one targeted checker or assertion for recurring failure signature.

• Prove fix with before-after artifacts under fixed mode sequencing.

Lessons learned

• Treat low-power boundaries as protocol contracts, not optional hints.

• Prefer bounded fixes over multi-axis edits during triage.

• Convert each escaped bug class into a lasting guardrail.

CASE STUDY - Power Bug Triage
escape risk / debug latency / closure confidence trend

Low-power verification deep dive

Signoff confidence comes from triage discipline, reproducible proof, and explicit residual-risk decisions.

Concept diagram

LPV SIGNOFF LADDER

reproduce -> classify -> isolate boundary -> bounded fix -> replay -> signoff decision

Metric graph

SIGNOFF CONFIDENCE

open ambiguous failures  ██████
reproducible closures    ███████
residual-risk unknowns   ███

Metrics and artifacts to collect

• X-prop triage classification report

• bug root-cause closure packet

• regression stability and recurrence trend

• signoff checklist completion matrix

Mini case study

A signoff block cleared after the team replaced broad waivers with boundary-specific evidence and replay criteria.

Debug branches

• Classify X behavior before broad waiving.

• Capture one definitive artifact packet per closure claim.

• Define residual risk and rollback path at signoff.

Senior review question

Ask: what exact low-power transition boundary failed first, and which artifact proves the closure claim reproducibly?

Key takeaways

• Tie each LPV claim to a concrete transition boundary and one proving artifact.

• Prefer minimal reversible fixes with explicit owner and rollback criteria.

Common pitfalls

• Treating power-aware failures as random before boundary classification.

• Waiving X-prop failures before proving impact and root cause.

• Declaring closure without deterministic replay across key modes.

Principal LPV review addendum

Power Bug Triage should be reviewed as a transition integrity system, not just isolated checks.

Use illegal transition count, corruption incidence, and reproducibility of low-power regressions across fixed seeds as alarm and LPV evidence packet: transition timeline, assertion outcomes, and before-after replay summary as proof.

LPV debug and signoff require disciplined triage: classify X behavior, isolate root cause, and close with reproducible evidence. Closure quality comes from reproducible evidence and explicit owners.