init: resume 작성 하네스 설계
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DongHyeonka
@@ -0,0 +1,341 @@
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from __future__ import annotations
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from datetime import datetime, timezone
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import pytest
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from resume_harness.models import (
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CandidateProfile,
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ContactInfo,
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DraftClaim,
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DraftSection,
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EvidenceCategory,
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EvidenceItem,
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EvidenceMap,
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EvidenceMatch,
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EvidenceMatchType,
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EvidenceSource,
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JobAnalysis,
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JobRequirement,
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QualityCategory,
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QualityFinding,
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QualitySeverity,
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RequirementCategory,
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RequirementKind,
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ResumeDraft,
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SectionType,
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)
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from resume_harness.quality import (
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apply_deterministic_score_caps,
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compute_coverage,
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compute_evaluation_policy_fingerprint,
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compute_weighted_overall,
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)
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NOW = datetime(2026, 7, 1, 12, tzinfo=timezone.utc)
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def test_deterministic_blocker_caps_inflated_subjective_score() -> None:
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scores = {
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QualityCategory.EVIDENCE: 100,
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QualityCategory.JOB_ALIGNMENT: 95,
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QualityCategory.COMPLETENESS: 99,
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QualityCategory.KOREAN_LANGUAGE: 95,
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QualityCategory.READABILITY: 95,
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QualityCategory.FORMATTING: 95,
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QualityCategory.CONSISTENCY: 95,
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QualityCategory.PRIVACY: 100,
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}
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blocker = QualityFinding(
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finding_id="thin-summary",
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code="CONTENT.THIN_SUMMARY",
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severity=QualitySeverity.ERROR,
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category=QualityCategory.COMPLETENESS,
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message="핵심 요약이 지나치게 얇다.",
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)
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capped = apply_deterministic_score_caps(scores, [blocker])
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assert capped[QualityCategory.COMPLETENESS] == 59
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assert capped[QualityCategory.EVIDENCE] == 100
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assert compute_weighted_overall(capped) < compute_weighted_overall(scores)
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def make_profile(
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content: str = "Python API를 개선했다.",
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*,
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keywords: list[str] | None = None,
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) -> CandidateProfile:
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return CandidateProfile(
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candidate_id="candidate-1",
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name="김하네스",
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contact=ContactInfo(email="harness@example.com"),
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facts=[
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EvidenceItem(
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evidence_id="ev-1",
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category=EvidenceCategory.PROJECT,
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content=content,
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source=EvidenceSource.PORTFOLIO,
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verification_status="document_verified",
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keywords=keywords or [],
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)
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],
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updated_at=NOW,
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)
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def test_requirement_coverage_is_priority_weighted_and_excludes_context() -> None:
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analysis = JobAnalysis(
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analysis_id="analysis-1",
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posting_id="posting-1",
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target_role="백엔드 엔지니어",
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summary="직무 기준",
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requirements=[
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JobRequirement(
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requirement_id="req-covered",
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text="Python API 경험",
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kind=RequirementKind.REQUIRED,
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category=RequirementCategory.SKILL,
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priority=5,
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source_quote="Python API 경험",
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classification_quote="필수 요건\nPython API 경험",
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),
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JobRequirement(
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requirement_id="req-missing",
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text="Kafka 운영 경험",
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kind=RequirementKind.PREFERRED,
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category=RequirementCategory.SKILL,
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priority=3,
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source_quote="Kafka 운영 경험",
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classification_quote="우대 요건\nKafka 운영 경험",
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),
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JobRequirement(
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requirement_id="req-context",
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text="글로벌 서비스 조직",
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kind=RequirementKind.CONTEXT,
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category=RequirementCategory.OTHER,
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priority=5,
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source_quote="글로벌 서비스 조직",
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),
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],
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analysed_at=NOW,
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)
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draft = ResumeDraft(
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draft_id="draft-1",
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candidate_id="candidate-1",
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posting_id="posting-1",
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title="백엔드 이력서",
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sections=[
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DraftSection(
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section_id="projects",
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section_type=SectionType.PROJECTS,
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heading="프로젝트",
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claims=[
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DraftClaim(
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claim_id="claim-1",
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text="Python API 개선",
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evidence_ids=["ev-1"],
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requirement_ids=["req-covered"],
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)
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],
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order=0,
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)
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],
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generated_at=NOW,
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)
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evidence_map = EvidenceMap(
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map_id="map-1",
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posting_id="posting-1",
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analysis_id="analysis-1",
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matches=[
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EvidenceMatch(
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requirement_id="req-covered",
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evidence_ids=["ev-1"],
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match_type=EvidenceMatchType.DIRECT,
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relevance_score=1,
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rationale="직접 근거",
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),
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EvidenceMatch(
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requirement_id="req-missing",
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match_type=EvidenceMatchType.GAP,
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relevance_score=0,
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gap_reason="근거 없음",
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),
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EvidenceMatch(
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requirement_id="req-context",
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match_type=EvidenceMatchType.GAP,
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relevance_score=0,
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gap_reason="평가 제외 맥락",
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),
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],
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generated_at=NOW,
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)
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coverage = compute_coverage(draft, analysis, evidence_map, make_profile())
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assert coverage.evidence == 1.0
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assert coverage.requirements == pytest.approx(5 / 8)
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def test_overall_score_is_a_deterministic_weighted_sum() -> None:
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scores = {
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QualityCategory.EVIDENCE: 100,
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QualityCategory.JOB_ALIGNMENT: 92,
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QualityCategory.COMPLETENESS: 90,
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QualityCategory.KOREAN_LANGUAGE: 92,
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QualityCategory.READABILITY: 92,
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QualityCategory.FORMATTING: 90,
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QualityCategory.CONSISTENCY: 95,
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QualityCategory.PRIVACY: 100,
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}
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assert compute_weighted_overall(scores) == 94.15
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with pytest.raises(ValueError, match="missing weighted"):
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compute_weighted_overall({QualityCategory.EVIDENCE: 100})
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def test_evaluation_policy_fingerprint_changes_with_judge_prompt() -> None:
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first = compute_evaluation_policy_fingerprint(
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system_prompt="system-v1", evaluator_prompt="judge-v1"
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)
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second = compute_evaluation_policy_fingerprint(
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system_prompt="system-v1", evaluator_prompt="judge-v2"
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)
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assert first != second
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def test_requirement_coverage_ignores_unrelated_claim_text() -> None:
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analysis = JobAnalysis(
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analysis_id="analysis-1",
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posting_id="posting-1",
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target_role="백엔드 엔지니어",
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summary="직무 기준",
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requirements=[
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JobRequirement(
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requirement_id="req-python",
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text="Python 개발 경험",
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kind=RequirementKind.REQUIRED,
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category=RequirementCategory.SKILL,
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source_quote="Python 개발 경험",
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classification_quote="필수\nPython 개발 경험",
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)
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],
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analysed_at=NOW,
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)
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draft = ResumeDraft(
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draft_id="draft-unrelated",
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candidate_id="candidate-1",
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posting_id="posting-1",
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title="백엔드 이력서",
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sections=[
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DraftSection(
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section_id="experience",
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section_type=SectionType.EXPERIENCE,
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heading="경험",
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claims=[
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DraftClaim(
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claim_id="claim-unrelated",
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text="고객 인터뷰를 수행했다",
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evidence_ids=["ev-1"],
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requirement_ids=["req-python"],
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)
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],
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order=0,
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)
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],
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generated_at=NOW,
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)
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evidence_map = EvidenceMap(
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map_id="map-unrelated",
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posting_id="posting-1",
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analysis_id="analysis-1",
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matches=[
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EvidenceMatch(
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requirement_id="req-python",
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evidence_ids=["ev-1"],
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match_type=EvidenceMatchType.DIRECT,
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relevance_score=1,
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rationale="잘못된 연결",
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)
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],
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generated_at=NOW,
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)
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assert (
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compute_coverage(
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draft,
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analysis,
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evidence_map,
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make_profile("고객 인터뷰를 수행했다."),
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).requirements
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== 0.0
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)
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def test_requirement_coverage_rechecks_actual_evidence_semantics() -> None:
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requirement = JobRequirement(
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requirement_id="req-support",
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text="고객 상담 경험",
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kind=RequirementKind.REQUIRED,
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category=RequirementCategory.EXPERIENCE,
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priority=5,
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source_quote="고객 상담 경험",
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classification_quote="필수 요건\n고객 상담 경험",
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)
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analysis = JobAnalysis(
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analysis_id="analysis-support",
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posting_id="posting-1",
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target_role="고객 상담원",
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summary="고객 상담 직무 기준",
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requirements=[requirement],
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analysed_at=NOW,
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)
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draft = ResumeDraft(
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draft_id="draft-support",
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candidate_id="candidate-1",
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posting_id="posting-1",
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title="고객 상담 이력서",
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sections=[
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DraftSection(
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section_id="experience",
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section_type=SectionType.EXPERIENCE,
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heading="경험",
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claims=[
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DraftClaim(
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claim_id="claim-support",
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text="고객 상담 경험",
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evidence_ids=["ev-1"],
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requirement_ids=["req-support"],
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)
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],
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order=0,
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)
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],
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generated_at=NOW,
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)
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evidence_map = EvidenceMap(
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map_id="map-support",
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posting_id="posting-1",
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analysis_id="analysis-support",
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matches=[
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EvidenceMatch(
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requirement_id="req-support",
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evidence_ids=["ev-1"],
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match_type=EvidenceMatchType.DIRECT,
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relevance_score=1,
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rationale="고객 단어가 같다.",
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)
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],
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generated_at=NOW,
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)
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coverage = compute_coverage(
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draft,
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analysis,
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evidence_map,
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make_profile("고객 명단을 정리했다."),
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)
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assert coverage.requirements == 0.0
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