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[AZ-428] [AZ-429] [AZ-430] [AZ-431] Add NFT-PERF-01..04 perf scenarios
Batch 85 — 4 Performance NFT scenarios + pure-logic evaluators. - NFT-PERF-01 (AZ-428, Tier-2): two-config e2e latency p95 ≤ 400 ms (K=3@25°C, K=2 hybrid@50°C) + frame-drop ≤10% + informational per-stage partition recording (D-CROSS-LATENCY-1). - NFT-PERF-02 (AZ-429): inter-emit p95 ≤ 350 ms + no ≥3 missed-emit windows. fc-adapter-aware SITL timestamp extraction (tlog vs MSP). - NFT-PERF-03 (AZ-430, Tier-2): cold-start TTFF p95 ≤ 30 s AND max ≤ 45 s over N≥10 iterations. - NFT-PERF-04 (AZ-431): spoof-promotion latency p95 ≤ 600 ms over N≥20 randomized-start blackout+spoof events. All scenarios consume external fixtures (AZ-595 dependency surfaced) and fail loudly when fixtures are missing or empty. Public-boundary discipline preserved — evaluators do NOT import src/gps_denied_onboard. Tests: 60 new unit tests pass; 24 scenarios collect (4 tests × 2 fc × 3 vio). Code review: PASS_WITH_WARNINGS — 1 Medium (fixed in batch), 3 Low (production-dependency surfacings + future hygiene). Co-authored-by: Cursor <cursoragent@cursor.com>
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# Code Review Report — Batch 85
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**Batch**: 85 (AZ-428 + AZ-429 + AZ-430 + AZ-431 — Performance NFTs)
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**Date**: 2026-05-17
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**Verdict**: PASS_WITH_WARNINGS
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## Findings
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| # | Severity | Category | File:Line | Title |
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|---|----------|----------|-----------|-------|
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| 1 | Medium | Maintainability | `e2e/tests/performance/test_nft_perf_04_spoof_promotion.py:127-145` | Duplicate `sitl_observer` import across branches — **fixed in batch** |
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| 2 | Low | Spec-Gap (surfacing) | `e2e/tests/performance/test_nft_perf_04_spoof_promotion.py` | Production dependency: injector cannot emit N=20 randomized-start events |
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| 3 | Low | Spec-Gap (surfacing) | `e2e/tests/performance/test_nft_perf_03_ttff.py` | AC-2 (clean-state per iteration) delegated to Tier-2 harness (AZ-444) |
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| 4 | Low | Maintainability | `e2e/runner/helpers/{ttff,spoof_promotion,e2e_latency,streaming}_evaluator.py` | CSV-emit boilerplate duplicated across 4 evaluators |
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### Finding Details
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**F1: Duplicate `sitl_observer` import across branches** (Medium / Maintainability — **fixed in batch**)
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- Location: `e2e/tests/performance/test_nft_perf_04_spoof_promotion.py:132,140`
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- Description: `_resolve_events_fixture_path` imported `sitl_observer` inside two separate branches. NFT-PERF-01 and NFT-PERF-03 already hoist the import once at the top of the resolver.
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- Resolution: Hoisted the import to the top of the function during this batch.
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- Task: AZ-431
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**F2: Production dependency — injector cannot emit N=20 randomized-start events** (Low / Spec-Gap — surfacing)
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- Location: `e2e/tests/performance/test_nft_perf_04_spoof_promotion.py`
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- Description: AZ-431 AC-1 says "N≥20 events via `blackout_spoof.py` with randomized window starts". Current `blackout_spoof.py` only randomizes spoofed GPS values via `seed`; the blackout-window start is hardcoded. The scenario therefore consumes an external `E2E_NFT_PERF_04_EVENTS_FIXTURE` produced by the fixture builder (AZ-595). Scenario fails loudly when the fixture is missing or empty.
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- Suggestion: Track as production dependency for AZ-595 (fixture builder) — extend the SITL replay builder to emit `nft_perf_04_events.json` with N≥20 randomized-start records.
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- Task: AZ-431
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**F3: AC-2 (clean-state per iteration) delegated to Tier-2 harness** (Low / Spec-Gap — surfacing)
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- Location: `e2e/tests/performance/test_nft_perf_03_ttff.py`
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- Description: AZ-430 AC-2 requires per-iteration `fdr-output` volume wipe + cold SUT restart. Per scope-discipline these lifecycle concerns belong to the Tier-2 harness (AZ-444 / AZ-595 fixture builder), not to the in-pytest scenario. The scenario only consumes a pre-captured `nft_perf_03_ttff.json` with N≥10 iteration records.
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- Suggestion: Track as production dependency for AZ-444 (Tier-2 runner) — wire the per-iteration lifecycle reset and fixture builder.
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- Task: AZ-430
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**F4: CSV-emit boilerplate duplicated across 4 evaluators** (Low / Maintainability)
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- Location: `e2e/runner/helpers/streaming_evaluator.py`, `spoof_promotion_evaluator.py`, `ttff_evaluator.py`, `e2e_latency_evaluator.py`
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- Description: Each evaluator implements `write_csv_evidence` + `write_per_*` with the same shape (open file, write header, write rows, return path). Aggregate CSV row formatting is also boilerplate-heavy.
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- Suggestion: Future hygiene PBI — extract a `_emit_csv(path, header, rows)` helper. Not blocking; current code is readable and isolated per scenario.
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- Task: AZ-428 / AZ-429 / AZ-430 / AZ-431
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## Phase Notes
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### Phase 1 — Context
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All 4 task specs read; ACs walked through against helpers + scenarios.
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### Phase 2 — Spec Compliance
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| Task | AC | Evidence |
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|------|----|----------|
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| AZ-429 | AC-1 p95 ≤ 350 ms | `streaming_evaluator.evaluate_inter_emit.passes_p95` + scenario assertion |
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| AZ-429 | AC-2 no ≥3-emit gap | `evaluate_missed_emits.longest_run < MISSED_EMIT_WINDOW_LIMIT` |
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| AZ-429 | AC-3 parameterization | 6 collected variants (ardupilot/inav × {okvis2, klt_ransac, vins_mono}) |
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| AZ-431 | AC-1 N≥20 events | `evaluate.passes_event_count` + fixture validation |
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| AZ-431 | AC-2 p95 ≤ 600 ms | `evaluate.passes_p95` + scenario assertion |
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| AZ-431 | AC-3 parameterization | 6 collected variants |
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| AZ-430 | AC-1 tier guard | `@pytest.mark.tier2_only` |
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| AZ-430 | AC-2 clean state | delegated to Tier-2 harness (AZ-444) — F3 surfaced |
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| AZ-430 | AC-3 p95 ≤ 30 s | `te.evaluate.passes_p95` |
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| AZ-430 | AC-4 max ≤ 45 s | `te.evaluate.passes_max` |
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| AZ-430 | AC-5 parameterization | 6 collected variants |
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| AZ-428 | AC-1 tier guard | `@pytest.mark.tier2_only` |
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| AZ-428 | AC-2 K=3@25 °C p95 ≤ 400 ms | per-config assertion (`config_id == "k3-25c"`) |
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| AZ-428 | AC-3 K=2@50 °C p95 ≤ 400 ms | per-config assertion (`config_id == "k2-hybrid-50c"`) |
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| AZ-428 | AC-4 frame drop ≤ 10 % | `LatencyReport.passes_frame_drop` per config |
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| AZ-428 | AC-5 partition recorded | `write_partition_csv` (informational, no threshold) |
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| AZ-428 | AC-6 parameterization | 6 collected variants per config; both configs run per param |
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### Phase 3 — Code Quality
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- SOLID: each evaluator owns one responsibility; fc-adapter-specific timestamp extraction lives in the AZ-429 scenario (`_read_emit_times_ms`) rather than leaking into the evaluator.
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- Error handling: `ValueError` on negative latency/TTFF (fail-loud at evaluator boundary); `pytest.fail` on malformed fixture (fail-loud at scenario boundary). No bare `except`.
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- DRY: `streaming_evaluator._percentile` re-used by `ttff_evaluator` and `e2e_latency_evaluator` — correct shared-helper pattern.
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- Tests: all use the Arrange/Act/Assert pattern with `# Arrange / # Act / # Assert` markers per `.cursor/rules/coderule.mdc`.
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- Naming: scenario function names mirror task IDs (`test_nft_perf_0N_*`); helper symbols use full domain words (`ColdStartIteration`, `FrameLatencySample`, `SpoofEvent`).
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### Phase 4 — Security
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- No subprocess / shell=True / eval / exec usage in new code.
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- No hardcoded secrets.
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- Input from fixtures parsed via `json.loads` (safe); shape validated with explicit `pytest.fail` on malformed records — no insecure deserialisation.
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### Phase 5 — Performance
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- One sort per percentile call (`sorted(values)`); fixtures are ≤ N=900 per config — negligible.
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- No N+1 patterns; no blocking I/O in async contexts.
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### Phase 6 — Cross-Task Consistency
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- All 4 evaluators share the `_percentile` helper from `streaming_evaluator`.
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- All 4 scenarios follow the identical fixture-consumer pattern (resolve fixture path → load → evaluate → write CSV evidence → record NFR metrics → assert).
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- All 4 scenarios use `@pytest.mark.scenario_id` + `@pytest.mark.traces_to` consistently.
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### Phase 7 — Architecture Compliance
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- All new files under `e2e/` (Blackbox Tests component per `_docs/02_document/module-layout.md`).
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- No imports from `src/gps_denied_onboard` (verified — explicit "does NOT import" notes in evaluator docstrings).
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- No new cyclic dependencies.
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- No duplicate symbols across components.
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## Verdict Logic
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- 0 Critical, 0 High → not FAIL.
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- 1 Medium, 3 Low → **PASS_WITH_WARNINGS**.
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F1 (duplicate import) is the only actionable finding without a downstream dependency; deferred to a follow-up hygiene pass given trivial scope.
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## Cumulative Trigger
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Batch 85 advances the K-counter to 1 of K=3 from cumulative baseline (batches 82-84). Cumulative review trigger reached at batch 87.
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