Detecting Unintended State Loss Scenarios: Reports and Metrics

Reports and metrics

Reports and Metrics for Detecting Unintended State Loss Scenarios is anchored on Escaped state-loss incident rate per power mode and observability coverage of non-retained critical state.. Convert observations into mechanism-backed and owner-bound actions.

A useful report explains why behavior moved, not only that behavior moved.

Evidence matrix

EVIDENCE MATRIX - Detecting Unintended State Loss Scenarios

+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| Evidence                    | Tells you                      | Does not prove                 | Next action               |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| transition timeline traces  | first failing LP phase         | complete root-cause ownership  | correlate with intent map |
| UPF-aware assertion logs    | contract violations by phase   | silicon product impact         | map to scenario severity  |
| corruption/X classification | actionable vs noisy failures   | legal transition completeness  | replay key mode corners   |
| save/restore snapshots      | state integrity movement       | isolation correctness          | pair with crossing checks |
| before-after regressions    | mitigation movement quality    | long-tail stability            | run full matrix           |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+

• Track Escaped state-loss incident rate per power mode and observability coverage of non-retained critical state. on representative low-power scenarios.

• Include branch, seed, mode, and configuration metadata in every report.

• Correlate observed symptoms with phase and boundary assumptions.

• Call out contradictory evidence explicitly.

Low-power verification deep dive

Retention closure requires proving end-to-end state lifecycle through save, off, and restore windows.

Concept diagram

RETENTION LIFECYCLE

save request -> state capture -> power off -> power on -> restore -> traffic resume

Metric graph

RETENTION STABILITY

restore mismatch       █████
save timing defects    ████
stable wake cycles     ███████

Metrics and artifacts to collect

• retention save/restore timing report

• pre/post state diff matrix

• multi-cycle retention stress summary

• state-loss bug trend by mode

Mini case study

A corruption issue persisted until retention checks compared multi-cycle state snapshots rather than single wake events.

Debug branches

• Track save acknowledgement against actual state capture.

• Validate restore completion before functional traffic resumes.

• Run repeated sleep/wake cycles to expose drift.

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.

Report interpretation

Read metric movement with phase and boundary context.

A report is actionable only when it isolates first failing transition boundary.