Benjamin Admin
80f2e2f619
Medical before Payment: the harder scientific test (safety AND security coupled, full lifecycle, deep risk/evidence demands). ISO 13485 runs through the SAME engine as ISO 27001 -> CRA, only new data, 0 runtime. The key result: IEC 81001-5-1 (health-software security) pulls in the SAME security MCAPs as the CRA, so Medical REUSES cyber capabilities (the safety/security coupling appears as capability reuse) while adding 7 genuinely new medical caps (clinical evaluation, software safety classification, ISO 14971 risk file, benefit-risk). rejected_assumptions intact. Effect on the convergence core: secure_signed_update_distribution 18 -> 24 and technical_vulnerability_management 17 -> 23, now spanning 3 domains (cyber + industrial + medical) — the core visibly GROWS, exactly the convergence signal. New 5th report: MISSING CONVERGENCE — deterministic (no ML) token-cluster detector for potential structural duplications: a name token shared by >=3 MCAPs across >=2 distinct sources is flagged for EXPERT REVIEW (never auto-merged). Surfaces e.g. the `risk` cluster (6 risk MCAPs across 6 sources) and `security`/`software`; single-source decompositions are filtered out. Complements Suspicious by looking at cross-source duplication, not single MCAPs. Also records the durable modelling rule extracted from the frequency fix: evidence is attributed to its ORIGIN; its value against a target is computed later (relevance(evidence,target)). Ledger now 8 sources, Architecture Stability 8/8 = 100%. Non-runtime -> no deploy. 29 tests pass, check-loc 0.
78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
"""Cross-Domain MCAP Convergence Analysis — impact over frequency (Phase Ω pause).
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Pins the deterministic MCAP Impact analysis and, critically, the anti-frequency-deception property:
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a capability that bridges many target TYPES / domains / journeys (secure_signed_update_distribution)
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must outrank a high-frequency single-domain management cap (conduct_internal_environmental_audits).
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Four reports (Core / Emerging / Isolated / Suspicious), pure aggregation over existing data, no runtime.
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"""
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from __future__ import annotations
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import os
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import subprocess
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import sys
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def _run():
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root = os.path.join(os.path.dirname(__file__), "..")
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r = subprocess.run(
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[sys.executable, "reference_scenarios/mcap_convergence_analysis.py"],
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cwd=root, env={**os.environ, "PYTHONPATH": "."}, capture_output=True, text=True,
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)
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assert r.returncode == 0, r.stderr
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return r.stdout
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def _section(out, header):
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start = out.index(header)
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nxt = out.find("\n## ", start + 1)
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return out[start: nxt if nxt != -1 else len(out)]
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def test_runs_end_to_end():
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out = _run()
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assert "Cross-Domain MCAP Convergence Analysis" in out
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assert "Impact = distinct Sources" in out
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def test_core_is_cross_cutting_not_frequency():
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out = _run()
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core = _section(out, "## 1. Core MCAPs")
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# the most cross-cutting capability tops the Core report; with Medical it now spans 3 domains
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assert "`secure_signed_update_distribution` | **24** |" in core
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assert "technical_vulnerability_management" in core
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# a high-frequency BUT single-domain management cap must NOT be in Core (frequency != impact)
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assert "conduct_internal_environmental_audits" not in core
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def test_all_five_reports_present():
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out = _run()
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for header in ["## 1. Core MCAPs", "## 2. Emerging MCAPs", "## 3. Isolated MCAPs",
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"## 4. Suspicious MCAPs", "## 5. Missing Convergence"]:
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assert header in out
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def test_missing_convergence_flags_cross_source_duplication():
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out = _run()
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mc = _section(out, "## 5. Missing Convergence")
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# the 'risk' token clusters several distinct risk MCAPs across many sources -> a review candidate
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assert "Token `risk`" in mc
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assert "maintain_risk_management_file_iso14971" in mc and "product_cyber_risk_assessment" in mc
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assert "KEIN Auto-Merge" in out
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# single-source decompositions (all-environmental) must be filtered out (>=2 sources required)
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assert "Token `environmental`" not in mc
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def test_isolated_and_suspicious_are_review_tools():
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out = _run()
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iso = _section(out, "## 3. Isolated MCAPs")
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assert "Review: spezialisiert ODER Konvergenz übersehen" in iso
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assert "conduct_clinical_evaluation" in iso or "cybersecurity_management_system" in iso
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susp = _section(out, "## 4. Suspicious MCAPs")
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assert "zu grob" in susp and "zu fein" in susp
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def test_abstraction_level_signal():
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out = _run()
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assert "richtigen Abstraktionsniveau" in out
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assert "Strukturkern" in out |