Benjamin Admin
77459d06d6
Two reviewed knowledge decisions (2026-06-28) + the deferred cosmetic counter, before #59. 1. ISO13485 removed from the incident_management hypothesis. ISO 13485 CAPA / quality-safety incident handling is NOT security incident management — the mapping was too broad and would seed false hypotheses for the empirical loop. A dedicated manage_quality_and_safety_incidents capability can come later IF a target needs it; not forced now. (ISO27001/TISAX/IEC62443 keep incident_management.) 2. patch_policy_doc -> secure_signed_update_distribution stays `partial`, but the curated rationale is sharpened: "indicates update governance, does not evidence signed distribution" (a patch policy is not proof of SIGNED distribution). New optional SignalMapping.rationale field carries the curated note. (github_actions_ci -> SDL and dependency_scanning -> vuln-mgmt reviewed and APPROVED as-is.) 3. Cosmetic (folded in since we touched the file): the silent-intake summary now counts detected and indications SEPARATELY ("N automatisch erkannt, M Indikation(en)") instead of lumping partial signals into "automatisch erkannt" — consistent with the three-state model just shipped. Tests: ISO13485 no longer resolves to incident_management; summary counts split correctly. 29 onboarding tests pass, mypy --strict clean, demo runs, check-loc 0. Runtime-visible (hypothesis resolution + summary text) -> deploy + smoke.
94 lines
4.9 KiB
Python
94 lines
4.9 KiB
Python
"""Certification Capability Hypotheses — capability-centric library + empirical confidence.
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Pins the reuse design (one capability, many supporting certs -> ~40-60 hypotheses, not ~300), the
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automatic multi-certification merge, the empirical (computed) confidence loop, and the Welt-1 guarantee
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that capabilities NO cert suggests (SBOM, signed updates, CVD) are never inferred -> they stay in the
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delta and get asked. Then the Advisor consumes the resolved library end-to-end.
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"""
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from __future__ import annotations
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import os
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import yaml
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from compliance.onboarding import (
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CapabilityHypothesis,
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Observation,
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ObservationType,
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OnboardingInput,
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advisor_start,
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empirical_confidence,
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empirical_distribution,
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inferred_hypotheses,
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resolve_for_certifications,
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)
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from compliance.transition_reasoning import TargetRequirement
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_DIR = os.path.dirname(__file__)
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_LIB = [CapabilityHypothesis(**h) for h in yaml.safe_load(
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open(os.path.join(_DIR, "..", "knowledge", "certification_hypotheses", "hypotheses.yaml"), encoding="utf-8"))["hypotheses"]]
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def test_library_is_capability_centric_and_reuses_certs():
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# the shared core is small (reuse, not 30-per-cert) and document control is supported by many certs
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doc = next(h for h in _LIB if h.capability == "document_and_change_control")
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assert len(doc.supported_by) >= 4
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assert len(_LIB) <= 60 # whole library, not ~300
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def test_multi_certification_merges_automatically():
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# a company with ISO9001 + ISO14001 + TISAX gets the UNION of their hypotheses, deduped
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merged = inferred_hypotheses(["ISO9001", "ISO14001", "TISAX"], _LIB)
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caps = {h.capability for h in merged}
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assert "document_and_change_control" in caps # ISO9001 + TISAX
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assert "information_security_management" in caps # TISAX
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assert "environmental_management_documentation" in caps # ISO14001
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# SBOM / signed updates are suggested by NO certificate -> never inferred
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assert "sbom_creation" not in caps and "secure_signed_update_distribution" not in caps
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def test_observations_are_richer_than_binary_and_review_gated():
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# the learning unit is the QUESTION; an answer can be partial with a scope note, not just yes/no
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raw = [Observation(hypothesis_id="HYP-supplier", observation_type=ObservationType.CONFIRMED)]
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assert empirical_confidence(raw) is None # unreviewed -> does NOT calibrate (review gate)
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obs = [
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Observation(hypothesis_id="HYP-supplier", observation_type=ObservationType.CONFIRMED, reviewed=True),
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Observation(hypothesis_id="HYP-supplier", observation_type=ObservationType.PARTIAL,
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scope_note="nur kritische Lieferanten", reviewed=True),
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Observation(hypothesis_id="HYP-supplier", observation_type=ObservationType.REFUTED, reviewed=True),
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Observation(hypothesis_id="HYP-supplier", observation_type=ObservationType.NOT_APPLICABLE, reviewed=True),
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]
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dist = empirical_distribution(obs) # a DISTRIBUTION, not a single percentage
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assert dist["confirmed"] == 1 and dist["partial"] == 1 and dist["refuted"] == 1 and dist["not_applicable"] == 1
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# confidence = (confirmed + 0.5*partial) / (confirmed+partial+refuted); n.a. excluded from the base
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assert empirical_confidence(obs) == 0.5
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def test_resolve_adapts_to_advisor_input():
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res = resolve_for_certifications(["ISO27001", "ISO9001"], _LIB)
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assert "incident_management" in res["ISO27001"]
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assert "document_and_change_control" in res["ISO9001"]
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def test_iso13485_does_not_suggest_security_incident_management():
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# ISO 13485 CAPA / quality-safety incident handling is NOT security incident management -> too broad,
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# removed from the incident_management hypothesis (review decision 2026-06-28).
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res = resolve_for_certifications(["ISO13485"], _LIB)
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assert "incident_management" not in res.get("ISO13485", [])
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inc = next(h for h in _LIB if h.capability == "incident_management")
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assert "ISO13485" not in inc.supported_by
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def test_advisor_consumes_the_library_end_to_end():
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cra = yaml.safe_load(open(os.path.join(_DIR, "..", "knowledge", "transition_patterns",
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"transition_pattern_iso27001_to_cra_maschinenvo_v1.yaml"), encoding="utf-8"))
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req = [TargetRequirement(capability_id=a["capability"]) for a in cra["likely_covered"]]
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req += [TargetRequirement(capability_id=d["capability"], expected_evidence=d.get("expected_evidence", []))
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for d in cra["delta_requirements"]]
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inp = OnboardingInput(company="x", certifications=["ISO27001", "TISAX", "ISO9001", "ISO14001"], target=["CRA"])
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hyp = resolve_for_certifications(inp.certifications, _LIB) # library -> advisor input
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res = advisor_start(inp, hyp, req, target_id="CRA", corpus_status={"CRA": "validated"})
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assert res.inferred_assumptions and res.next_best_questions
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assert any(r.certification == "ISO14001" for r in res.rejected_assumptions) # not relevant to CRA
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