Benjamin Admin
1a9439d013
The real bottleneck is domain MODELLING. Phase B is organized as one program with sub-programs per domain, each run through the SAME 7-stage production line. No new runtime framework, no new module (ADR-009, Freeze v1.0) — only program data + a derived reporting view. - Customer enters by INDUSTRY, not regulation: Industry -> Domain Model -> Requirement Sources -> Requirements -> Capabilities -> ... -> Completeness. - 7-stage checklist identical for every domain (Domain Model / Requirement Sources / Capability Registry / Transition Patterns / Playbooks / Reference Scenarios / Completeness) with per-stage ownership. README generalized to the framework. - Each domain lists typical_requirement_sources + typical_certifications -> pre-onboarding capability HYPOTHESIS (the ETO insight; feeds Company 2A as inferred, never confirmed). - Backlog v1 (by customer value): 1 Industrial Automation, 2 Environmental, 3 Automotive, 4 Medical, 5 Energy. Five domain-definition shells (environmental restructured to the unified shape, law-first preserved). - Per-domain KPI is DERIVED from the real corpus (computed-not-stored; sources modelled / transition patterns / playbooks / reference scenarios), NOT a curated number. Reference suite renders maturity bars: Industrial Automation 43% (3/7 sources) leads, Environmental 0% (work ahead). Backlog (value) and KPI (corpus state) are deliberately separated. - ADR-009: Domain Knowledge Program framework. Honest known refinement: regulation-ID normalization (CRA vs Cyber Resilience Act) aliased in the KPI. 7 program-contract tests (backlog order + industry-first + derived-not-stored), check-loc 0. Knowledge data + ADR + reference harness = non-runtime -> no deploy (ADR-001). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
72 lines
2.8 KiB
Python
72 lines
2.8 KiB
Python
"""Characterization tests for the Domain Knowledge Program v1 backlog (data, not code).
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Pins the program FRAMEWORK contract: a ranked backlog of domain definitions, each entered by INDUSTRY
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with its typical requirement sources + a pre-onboarding capability hypothesis (typical_certifications).
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Industrial Automation is rank 1. Environmental stays law-first. If a future edit reorders the backlog,
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drops a source list, or reverts environmental to an ISO-first framing, these tests fail.
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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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_DIR = os.path.join(os.path.dirname(__file__), "..", "knowledge", "programs")
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def _programs():
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out = {}
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for f in sorted(os.listdir(_DIR)):
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if f.endswith(".yaml"):
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with open(os.path.join(_DIR, f), encoding="utf-8") as h:
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p = yaml.safe_load(h)
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out[p["id"]] = p
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return out
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def test_five_domains_ranked_backlog():
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ranks = sorted(p["backlog_rank"] for p in _programs().values())
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assert ranks == [1, 2, 3, 4, 5]
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def test_industrial_automation_is_rank_1():
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progs = _programs()
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rank1 = [p for p in progs.values() if p["backlog_rank"] == 1]
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assert len(rank1) == 1 and rank1[0]["id"] == "PROG-industrial-automation"
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assert {"CRA", "MaschinenVO"} <= set(rank1[0]["typical_requirement_sources"])
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def test_every_domain_entered_by_industry_with_sources_and_hypothesis():
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for p in _programs().values():
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assert p.get("industry") and p.get("customer_entry") # industry-first entry
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assert p["typical_requirement_sources"] # stage 2 defined
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assert p["typical_certifications"] # pre-onboarding capability hypothesis (ETO)
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def test_no_stored_stage_status_progress_is_derived():
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# the 7-stage progress is computed-not-stored: program shells must NOT hard-code stage status
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for p in _programs().values():
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assert "stages" not in p
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def test_environmental_stays_law_first():
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env = _programs()["PROG-environmental"]
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assert "ISO 14001 ist KEIN Umweltrecht" in env["principle"]
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assert set(env["typical_requirement_sources"]) == {"water", "chemicals", "emissions", "energy", "waste", "product_responsibility"}
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def test_automotive_and_medical_present():
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progs = _programs()
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assert "TISAX" in progs["PROG-automotive"]["typical_requirement_sources"]
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assert "MDR" in progs["PROG-medical"]["typical_requirement_sources"]
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def test_readme_documents_seven_stage_checklist():
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with open(os.path.join(_DIR, "README.md"), encoding="utf-8") as h:
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readme = h.read()
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for stage in ["Domain Model", "Requirement Sources", "Capability Registry",
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"Transition Patterns", "Playbooks", "Reference Scenarios", "Completeness"]:
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assert stage in readme
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assert "Industrial Automation" in readme # backlog #1 documented
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