hangshuo652
bc1d56d1a4
P0.6: gcov infrastructure P1: extract_structure output expansion (11 new feature fields) P2: Confusion group rule engine (8 pairs + contradiction + backtrack) P3: 4-factor confidence calculation + quality gate update P4: 33+2 COBOL program type test samples (22 files, 7 categories) P5: parametrized/ test data generation engine P6: japanese_data.py lookup tables P7-10: Type-specific test suites (~159 parametrized tests) P11: Full classification pipeline (classify_program) + orchestrator integration P12: Documentation (module-interfaces, test-plan v3.0, coverage-matrix) Architecture decisions: - classification_pipeline/ merged to hina/pipeline/ - parametrized/ as independent module - japanese_data.py as root-level file - hina/__all__ only exports classify_program() Co-Authored-By: Claude <noreply@anthropic.com>
355 lines
11 KiB
Python
355 lines
11 KiB
Python
"""测试: 确信度 4 因子计算 + 质量门禁评分 + 覆盖率比较"""
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import pytest
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from hina.confidence import compute_confidence_v2
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from hina.gate import compute_quality_score, check as gate_check
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from coverage.compare_coverage import compare_coverage
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# ── compute_confidence_v2 判定阈值测试 ──
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def test_auto_judgment():
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"""确信度 >= 0.90 → auto"""
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keyword_result = {
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"base_confidence": 1.0,
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"match_count": 3,
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}
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structure_features = {"structure_match_score": 5}
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result = compute_confidence_v2(keyword_result, structure_features)
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# 1.0 × 1.0 × 1.0 × 1.0 = 1.0
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assert result["confidence"] == 1.0
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assert result["judgment"] == "auto"
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assert result["needs_review"] is False
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def test_review_judgment():
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"""确信度 0.70-0.89 → review"""
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# Need 0.70 <= confidence < 0.90
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# base=1.0, context=0.95, consistency=1.0, structure=0.7 → 0.665 → still manual
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# base=1.0, context=1.0, consistency=0.9, structure=0.85... hmm structure is discrete
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# Let's try: base=0.95, context=1.0, consistency=1.0, structure=0.7 → 0.665 (manual)
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# base=0.95, context=0.95(match=2), consistency=1.0, structure=0.7 → 0.63175 (manual)
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# base=0.95, context=1.0, consistency=0.90, structure=1.0 → 0.855 (review!)
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keyword_result = {
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"base_confidence": 0.95,
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"match_count": 3,
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}
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structure_features = {"structure_match_score": 5}
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contradictions = [
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{"type": "type_mismatch", "resolved": True},
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]
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result = compute_confidence_v2(
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keyword_result, structure_features,
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contradictions=contradictions,
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)
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# 0.95 × 1.0 × 0.90 × 1.0 = 0.855
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assert 0.70 <= result["confidence"] < 0.90
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assert result["judgment"] == "review"
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assert result["needs_review"] is True
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def test_manual_judgment():
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"""确信度 0.50-0.69 → manual"""
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keyword_result = {
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"base_confidence": 0.95,
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"match_count": 1,
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}
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structure_features = {"structure_match_score": 4}
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contradictions = [
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{"type": "type_mismatch", "resolved": True},
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]
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result = compute_confidence_v2(
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keyword_result, structure_features,
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contradictions=contradictions,
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)
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# 0.95 × 0.90 × 0.90 × 0.7 = 0.53865
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assert 0.50 <= result["confidence"] < 0.70
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assert result["judgment"] == "manual"
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assert result["needs_review"] is True
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def test_impossible_judgment():
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"""确信度 < 0.50 → impossible"""
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keyword_result = {
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"base_confidence": 0.7,
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"match_count": 0,
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}
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structure_features = {"structure_match_score": 0}
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result = compute_confidence_v2(keyword_result, structure_features)
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# 0.7 × 0.50 × 1.0 × 0.3 = 0.105
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assert result["confidence"] < 0.50
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assert result["judgment"] == "impossible"
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assert result["needs_review"] is True
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# ── 因子边界测试 ──
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def test_context_factor_match_counts():
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"""关键字匹配数对上下文因子的影响"""
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# match_count >= 3 → context_factor = 1.0
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r = compute_confidence_v2(
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{"base_confidence": 1.0, "match_count": 5},
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{"structure_match_score": 5},
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)
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assert r["context_factor"] == 1.0
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assert r["confidence"] == 1.0
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# match_count == 2 → context_factor = 0.95
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r = compute_confidence_v2(
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{"base_confidence": 1.0, "match_count": 2},
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{"structure_match_score": 5},
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)
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assert r["context_factor"] == 0.95
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assert r["confidence"] == 0.95
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# match_count == 1 → context_factor = 0.90
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r = compute_confidence_v2(
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{"base_confidence": 1.0, "match_count": 1},
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{"structure_match_score": 5},
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)
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assert r["context_factor"] == 0.90
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assert r["confidence"] == 0.90
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# match_count == 0 → context_factor = 0.50
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r = compute_confidence_v2(
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{"base_confidence": 1.0, "match_count": 0},
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{"structure_match_score": 5},
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)
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assert r["context_factor"] == 0.50
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assert r["confidence"] == 0.50
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def test_consistency_factor_contradictions():
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"""矛盾数量对一致性因子的影响"""
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# 无矛盾 → 1.0
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r = compute_confidence_v2(
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{"base_confidence": 1.0, "match_count": 3},
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{"structure_match_score": 5},
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contradictions=[],
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)
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assert r["consistency_factor"] == 1.0
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# 已解决 → 0.90
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r = compute_confidence_v2(
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{"base_confidence": 1.0, "match_count": 3},
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{"structure_match_score": 5},
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contradictions=[{"type": "t1", "resolved": True}],
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)
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assert r["consistency_factor"] == 0.90
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# 未解决 < 3 → 0.80
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r = compute_confidence_v2(
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{"base_confidence": 1.0, "match_count": 3},
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{"structure_match_score": 5},
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contradictions=[{"type": "t1", "resolved": False}],
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)
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assert r["consistency_factor"] == 0.80
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# ≥3 未解决 → 0.50
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r = compute_confidence_v2(
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{"base_confidence": 1.0, "match_count": 3},
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{"structure_match_score": 5},
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contradictions=[
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{"type": "t1", "resolved": False},
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{"type": "t2", "resolved": False},
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{"type": "t3", "resolved": True},
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],
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)
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assert r["consistency_factor"] == 0.50
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def test_structure_factor_scores():
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"""结构匹配度对结构一致性因子的影响"""
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# 5/5 → 1.0
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r = compute_confidence_v2(
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{"base_confidence": 1.0, "match_count": 3},
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{"structure_match_score": 5},
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)
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assert r["structure_factor"] == 1.0
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# 3-4/5 → 0.7
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r = compute_confidence_v2(
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{"base_confidence": 1.0, "match_count": 3},
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{"structure_match_score": 3},
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)
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assert r["structure_factor"] == 0.7
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# 1-2/5 → 0.5
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r = compute_confidence_v2(
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{"base_confidence": 1.0, "match_count": 3},
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{"structure_match_score": 1},
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)
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assert r["structure_factor"] == 0.5
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# 无法/0 → 0.3
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r = compute_confidence_v2(
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{"base_confidence": 1.0, "match_count": 3},
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{"structure_match_score": 0},
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)
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assert r["structure_factor"] == 0.3
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def test_base_confidence_default():
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"""keyword_result 未提供 base_confidence 时使用默认值 0.7"""
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r = compute_confidence_v2(
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{"match_count": 3},
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{"structure_match_score": 5},
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)
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assert r["base"] == 0.7
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# ── compute_quality_score 双模式测试 ──
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def test_quality_score_no_gcov():
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"""gcov 未启用模式: branch_rate×0.5 + paragraph_rate×0.5 + confidence×0.4"""
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static_cov = {
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"branch_rate": 0.80,
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"paragraph_rate": 0.90,
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}
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score = compute_quality_score(static_cov, gcov_coverage=None, confidence=0.5)
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# 0.80×0.5 + 0.90×0.5 + 0.5×0.4 = 0.40 + 0.45 + 0.20 = 1.05 → min(1.0, 1.05) = 1.0
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assert score == 1.0
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def test_quality_score_no_gcov_sub_max():
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"""gcov 未启用模式，确保不超过 1.0 被 clamp"""
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static_cov = {
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"branch_rate": 0.60,
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"paragraph_rate": 0.70,
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}
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score = compute_quality_score(static_cov, gcov_coverage=None, confidence=0.8)
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# 0.60×0.5 + 0.70×0.5 + 0.8×0.4 = 0.30 + 0.35 + 0.32 = 0.97
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assert score == 0.97
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def test_quality_score_with_gcov():
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"""gcov 启用模式: static_cov×0.3 + gcov_cov×0.4 + confidence×0.3"""
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static_cov = {
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"branch_rate": 0.80,
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"paragraph_rate": 0.90,
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}
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gcov_cov = {"gcov_cov": 0.75}
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score = compute_quality_score(static_cov, gcov_cov, confidence=0.5)
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# static_cov = 0.80×0.5 + 0.90×0.5 = 0.85
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# score = 0.85×0.3 + 0.75×0.4 + 0.5×0.3 = 0.255 + 0.30 + 0.15 = 0.705
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assert score == 0.705
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def test_quality_score_with_gcov_zero_confidence():
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"""gcov 启用模式，置信度为 0"""
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static_cov = {
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"branch_rate": 1.0,
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"paragraph_rate": 1.0,
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}
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gcov_cov = {"gcov_cov": 0.5}
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score = compute_quality_score(static_cov, gcov_cov, confidence=0.0)
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# static_cov = 1.0
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# score = 1.0×0.3 + 0.5×0.4 + 0.0×0.3 = 0.30 + 0.20 + 0.0 = 0.50
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assert score == 0.50
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# ── compare_coverage 基本功能测试 ──
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def test_compare_coverage_basic():
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"""compare_coverage 基本功能"""
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static = {
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"branch_rate": 0.90,
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"paragraph_rate": 0.85,
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"total_branches": 20,
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"covered_branches": 18,
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}
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dynamic = {
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"gcov_cov": 0.75,
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"covered_branches": 15,
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"total_branches": 20,
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"misleading_branches": ["BR001", "BR003"],
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}
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result = compare_coverage("TESTPROG", static, dynamic)
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assert result["program"] == "TESTPROG"
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assert result["static"]["branch_rate"] == 0.90
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assert result["static"]["paragraph_rate"] == 0.85
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assert result["dynamic"]["gcov_cov"] == 0.75
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# gap = (0.90×0.5 + 0.85×0.5) - 0.75 = 0.875 - 0.75 = 0.125
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assert result["gap"] == 0.125
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assert result["misleading_branches"] == ["BR001", "BR003"]
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def test_compare_coverage_no_gap():
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"""静态与动态完全一致时 gap 为 0"""
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static = {
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"branch_rate": 0.80,
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"paragraph_rate": 0.80,
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"total_branches": 10,
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"covered_branches": 8,
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}
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dynamic = {
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"gcov_cov": 0.80,
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"covered_branches": 8,
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"total_branches": 10,
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"misleading_branches": [],
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}
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result = compare_coverage("NOGAP", static, dynamic)
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# gap = (0.80×0.5 + 0.80×0.5) - 0.80 = 0.80 - 0.80 = 0.0
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assert result["gap"] == 0.0
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assert result["misleading_branches"] == []
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def test_compare_coverage_no_misleading():
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"""没有误导分支时的返回"""
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static = {
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"branch_rate": 0.95,
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"paragraph_rate": 1.0,
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}
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dynamic = {
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"gcov_cov": 0.90,
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"misleading_branches": [],
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}
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result = compare_coverage("CLEAN", static, dynamic)
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# gap = (0.95×0.5 + 1.0×0.5) - 0.90 = 0.975 - 0.90 = 0.075
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assert result["gap"] == 0.075
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assert result["misleading_branches"] == []
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# ── gate.check 基本功能测试 ──
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def test_gate_check_passed():
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"""质量门禁完全通过"""
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result = gate_check(
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complete_tests=[{"id": 1}],
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hina_result={},
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coverage={"branch_rate": 1.0, "paragraph_rate": 1.0},
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)
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assert result["passed"] is True
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assert len(result["issues"]) == 0
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def test_gate_check_failed_branch():
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"""分支覆盖率不足"""
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result = gate_check(
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complete_tests=[{"id": 1}],
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hina_result={},
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coverage={
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"branch_rate": 0.50,
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"paragraph_rate": 1.0,
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"uncovered_decision_ids": [1, 2],
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},
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)
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assert result["passed"] is False
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assert "decision_gaps" in result["issues"]
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def test_gate_check_no_data():
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"""无测试数据"""
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result = gate_check(
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complete_tests=[],
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hina_result={},
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coverage={"branch_rate": 1.0, "paragraph_rate": 1.0},
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)
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assert result["passed"] is False
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assert "no_data" in result["issues"]
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