81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
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"""Stratified evidence — decision D-01.
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S1 Surface how an action was performed (selectors, routes, payloads)
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S2 Realization whether it happened, and through which surface
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S3 Judgment whether that was correct
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Each stratum has a different authority. Models may write S1. Nothing but an
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independent Observer writes S2 or S3.
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See docs/TestDriverClassificationDesign.md, Part A.
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass, field, asdict
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from datetime import datetime, timezone
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from enum import Enum
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from typing import Any
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class Stratum(str, Enum):
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SURFACE = "S1"
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REALIZATION = "S2"
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JUDGMENT = "S3"
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def _now() -> str:
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return datetime.now(timezone.utc).isoformat()
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@dataclass(frozen=True, slots=True)
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class Observation:
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"""One recorded fact, attributed to a stratum and a collector.
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`collector` is never an actor for S2/S3 observations. The runner enforces
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this; see runner._assert_collector_independence.
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"""
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id: str
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stratum: Stratum
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collector: str
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step_id: str | None
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kind: str
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data: dict[str, Any]
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at: str = field(default_factory=_now)
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@dataclass(slots=True)
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class EvidencePack:
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"""Everything retained from one run.
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The pack must be sufficient to replay the run and to diagnose a finding
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without the original process. An assertion that cannot be supported from the
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pack is an EVIDENCE_FAILURE, not a defect in the system under test.
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"""
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run_id: str
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scenario_id: str
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use_case_id: str
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sut_version: str
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started_at: str = field(default_factory=_now)
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finished_at: str | None = None
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observations: list[Observation] = field(default_factory=list)
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verdicts: list[dict[str, Any]] = field(default_factory=list)
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energy_events: list[dict[str, Any]] = field(default_factory=list)
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provenance_index: dict[str, str] = field(default_factory=dict)
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def record(self, observation: Observation) -> None:
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self.observations.append(observation)
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def of_stratum(self, stratum: Stratum) -> list[Observation]:
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return [o for o in self.observations if o.stratum is stratum]
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def to_json(self) -> str:
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payload = asdict(self)
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payload["observations"] = [
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{**asdict(o), "stratum": o.stratum.value} for o in self.observations
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]
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return json.dumps(payload, indent=2, sort_keys=True, default=str)
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