aa99111a87
Phase A½. The move from feature to product development: for every assessment, answer "how sure are we that this answer is COMPLETE?" — different from confidence. The product never claims full coverage; it makes its own knowledge state transparent and auditable. Shows what we do NOT know and why. - compliance/completeness/: assess_completeness(identified, corpus_status, uncertain, assumptions, assessed_obligations) -> CompletenessReport. Separates IDENTIFIED from ASSESSED (validated corpus AND determined applicability) and justifies every gap. Two kinds of open: corpus gap (future_corpus) and applicability uncertainty (query_required + deciding question, e.g. Data Act / generates_usage_data). - The metric is COUNTS, never a single percentage: "Identifiziert N · bewertet M · offen K · Unsicherheiten U · Begründung ja" + an honest audit statement. - ADR-007: auditable honesty; phase order A factory -> A½ Completeness -> B new domains; the transparency selling point. Deterministic, no LLM; corpus status + obligation count injected. - reference suite: "Regulatory Completeness" section runs an industrial-dishwasher assessment (assessed CRA/MaschinenVO; open EMV/Environmental=future_corpus, Data Act=query_required) and notes Environmental flips open->validated automatically once the corpus lands. 11 completeness tests (54 with adjacent modules), mypy --strict clean (15 files), check-loc 0. Product code with no app caller + ADR/reference = non-runtime -> no deploy (ADR-001). Freeze-safe. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
142 lines
6.7 KiB
Python
142 lines
6.7 KiB
Python
# ruff: noqa
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# mypy: ignore-errors
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"""Rendering helpers for the Reference Scenario Suite generator.
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Holds the shared mutable output buffers (OUT, ROLLUP) and the small markdown helpers so the
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generator script (`generate.py`) stays under the LOC budget. Not product code; not imported by
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the app — only by the generator (run via `PYTHONPATH=. python3 reference_scenarios/generate.py`).
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"""
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from __future__ import annotations
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from typing import List, Tuple
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Row = Tuple[str, str, str]
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OUT: List[str] = []
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ROLLUP: List[str] = []
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def w(s: str = "") -> None:
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OUT.append(s)
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def coverage_table(rows: List[Row]) -> None:
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w("**Architecture Coverage**")
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w("")
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w("| Layer | Status | Hinweis |")
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w("|---|---|---|")
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for layer, status, note in rows:
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w("| %s | **%s** | %s |" % (layer, status, note))
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ROLLUP.append(status)
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w("")
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def reg_map_block(rmap) -> None:
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w("**Expected Regulatory Map**")
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w("")
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w("> " + rmap.executive_summary)
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w("")
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for v in rmap.applicable_regulations:
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obs = ", ".join(o.obligation_id for o in v.obligations) or v.obligations_note
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w("- **%s** (%s) — Pflichten: %s" % (v.regulation_id, v.name, obs))
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for u in rmap.uncertain_regulations:
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w("- _unsicher_ %s — fehlt: %s" % (u.regulation_id, ", ".join(u.missing_facts) or "-"))
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for ov in rmap.overlaps:
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w("- Overlap %s: %s" % (ov.overlap_group_id, ", ".join(ov.shared_obligations)))
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for ev, ids in rmap.shared_evidence.items():
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w("- 1 Nachweis `%s` => %d Pflichten" % (ev, len(ids)))
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w("")
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def unsupported_block(rmap) -> None:
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w("**Expected Unsupported Domains**")
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w("")
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if not rmap.unsupported_domains:
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w("- keine — alle getriggerten Domaenen sind im Korpus")
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for d in rmap.unsupported_domains:
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w("- `%s` (Trigger: %s) -> %s" % (d.domain, d.trigger, d.note))
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w("")
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def interp_status(verdict_value: str) -> str:
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return "PARTIAL" if verdict_value in ("uncertain", "unsupported") else "PASS"
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def knowledge_intake_section(base_dir) -> None:
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"""Render the Knowledge Intake section (kept here so generate.py stays under the LOC budget)."""
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import os
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import yaml
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from compliance.knowledge_intake import (
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DocumentDescriptor, assess_document_impact, build_knowledge_index,
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)
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def _load(sub):
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d = os.path.join(base_dir, "..", "knowledge", sub)
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return [yaml.safe_load(open(os.path.join(d, f), encoding="utf-8"))
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for f in sorted(os.listdir(d)) if f.endswith(".yaml")]
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idx = build_knowledge_index(
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_load("transition_patterns"), _load("implementation_playbooks"),
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_load("reference_transition_scenarios"), obligation_index={"CRA": ["cra_obl_1", "cra_obl_2"]})
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docs = [
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DocumentDescriptor(document_id="ENISA CRA SBOM-FAQ", regulations=["CRA"], keywords=["sbom", "vulnerability"], document_type="faq"),
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DocumentDescriptor(document_id="EU Umwelt-Leitfaden", regulations=["UmweltVO"], keywords=["wastewater"], document_type="guidance"),
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DocumentDescriptor(document_id="Marketing-Blog", keywords=["newsletter"], document_type="blog"),
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]
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w("## Knowledge Intake — Impact zuerst, Extraktion später")
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w("")
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w('_Vor dem Parser: ein neues Dokument NUR einordnen und seinen Impact auf den bestehenden Wissensbestand bestimmen. „Von N Dokumenten verändern wenige tatsächlich unser Wissen." Deterministisch, keine Extraktion, kein LLM._')
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w("")
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w("| Dokument | Impact | betrifft | Empfehlung |")
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w("|---|---|---|---|")
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for d in docs:
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kp = assess_document_impact(d, idx)
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touch = "neue Domäne" if kp.new_domain else "%dC·%dPB·%dRTS·%dObl" % (
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len(kp.affected_capabilities), len(kp.affected_playbooks),
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len(kp.affected_reference_scenarios), len(kp.affected_obligations))
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w("| %s | **%s** | %s | %s |" % (d.document_id, kp.impact_level.value, touch, kp.recommendation.split(" —")[0]))
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w("")
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w("**Beispiel-Knowledge-Package** (`%s`): %s" % (docs[0].document_id, assess_document_impact(docs[0], idx).impact_summary))
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w("")
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w('_So entsteht bei jedem neuen Dokument eine Impact-Analyse statt „200 Seiten PDF" — Targeted Updating statt Schreiben._')
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w("")
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coverage_table([
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("Knowledge Intake (Klassifikation+Impact)", "PASS", "%d Regelwerke / %d Capabilities im Index" % (len(idx.regulations), len(idx.capability_regulations))),
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("Impact-Triage (HIGH/LOW/NONE/new_domain)", "PASS", "3 Beispiel-Dokumente korrekt eingeordnet"),
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("Regelwerk-ID-Normalisierung", "TODO", "CRA vs Cyber Resilience Act vereinheitlichen"),
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])
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def completeness_section() -> None:
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"""Render the Regulatory Completeness section (kept here so generate.py stays under the LOC budget)."""
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from compliance.completeness import assess_completeness
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rep = assess_completeness(
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identified_regulations=["CRA", "MaschinenVO", "EMV", "Environmental", "DataAct"],
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corpus_status={"CRA": "validated", "MaschinenVO": "validated", "EMV": "unsupported",
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"Environmental": "unsupported", "DataAct": "validated"},
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uncertain=[{"regulation": "DataAct", "deciding_question": "generates_usage_data", "reason": "generates_usage_data = unbekannt"}],
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assumptions=[{"key": "Funkmodul", "value": "nein"}, {"key": "personenbezogene Nutzungsdaten", "value": "nein"}],
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assessed_obligations=128)
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w("## Regulatory Completeness — was wir bewerten konnten, und was bewusst nicht")
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w("")
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w('_Interne Qualitätsmaschine (KEIN Confidence-Score): trennt IDENTIFIZIERT von BEWERTET und begründet jede Lücke. Keine Prozentzahl — auditierbar und ehrlich: „Wir zeigen auch, was wir noch nicht wissen und warum."_')
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w("")
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w("**%s**" % rep.completeness_summary)
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w("")
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w("> %s" % rep.audit_statement)
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w("")
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w("- **Bewertet:** %s (%d Pflichten)" % (", ".join(rep.assessed_regulations), rep.assessed_obligations))
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w("- **Offen (jeweils begründet):**")
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for e in rep.exclusions:
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dq = (" → Rückfrage: `%s`" % e.deciding_question) if e.deciding_question else ""
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w(" - `%s` — %s `[%s]`%s" % (e.subject, e.reason, e.resolution, dq))
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w("- **Annahmen:** %s" % ", ".join("%s=%s" % (a.key, a.value) for a in rep.assumptions))
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w("")
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w("_Sobald der Umwelt-Korpus (ISO 14001 etc.) landet, kippt `Environmental` automatisch von offen auf bewertet — die Completeness Engine dokumentiert den Fortschritt je Domäne._")
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w("")
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coverage_table([
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("Regulatory Completeness (auditierbar)", "PASS", rep.completeness_summary),
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("Begründete Ausschlüsse (Korpus/Anwendbarkeit)", "PASS", "%d Ausschlüsse, alle mit Grund" % len(rep.exclusions)),
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("Fortschritts-Doku je Domäne", "PASS", "Environmental offen→validated bei Korpus-Landung"),
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])
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