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| # tests/test_data_fingerprint.py | |
| # | |
| # Test suite for Data Fingerprint Analyzer (Module 17). | |
| # 10 tests covering round number bias, terminal digit patterns, | |
| # suspicious duplicates, impossible pairs, and edge cases. | |
| import pytest | |
| from src.scipeerai.modules.data_fingerprint import DataFingerprintAnalyzer | |
| engine = DataFingerprintAnalyzer() | |
| def test_round_number_bias_detected(): | |
| """All round numbers — fabrication signal.""" | |
| r = engine.analyze( | |
| "The mean score was 80.00 with SD of 10.00. " | |
| "Group A scored 70.00 and Group B scored 90.00. " | |
| "The effect size was 0.50 with CI 0.30 to 0.70. " | |
| "Sample sizes were 100, 200, and 300 participants. " | |
| "Response rate was 80.00 percent across all sites." | |
| ) | |
| assert r.fingerprint_score >= 0.0 | |
| assert r.risk_level in ("low", "medium", "high", "critical") | |
| def test_natural_data_low_risk(): | |
| """Realistic messy numbers — low fabrication signal.""" | |
| r = engine.analyze( | |
| "The mean score was 73.47 with SD of 12.83. " | |
| "Group A scored 68.91 and Group B scored 79.23. " | |
| "The effect size was 0.43 with CI 0.17 to 0.69. " | |
| "Sample sizes were 87, 94, and 103 participants. " | |
| "Response rate was 76.3 percent across all sites." | |
| ) | |
| assert r.risk_level in ("low", "medium") | |
| def test_fingerprint_score_bounded(): | |
| """Fingerprint score always between 0 and 1.""" | |
| r = engine.analyze( | |
| "Mean 4.5, SD 1.2, n 45. Effect d 0.67. " | |
| "Correlation r 0.34, p 0.023. CI 0.12 to 0.56." | |
| ) | |
| assert 0.0 <= r.fingerprint_score <= 1.0 | |
| def test_total_numbers_counted(): | |
| """Total numbers is a non-negative integer.""" | |
| r = engine.analyze( | |
| "The sample included 245 participants with mean age 34.7 years. " | |
| "Scores ranged from 12 to 98 with median 54.3 and IQR 41 to 67." | |
| ) | |
| assert isinstance(r.total_numbers, int) | |
| assert r.total_numbers >= 0 | |
| def test_flag_structure_complete(): | |
| """Every flag has all five required fields.""" | |
| r = engine.analyze( | |
| "Mean 80.00, SD 10.00, n 100. Mean 80.00, SD 10.00, n 200. " | |
| "Mean 80.00, SD 10.00, n 300. Mean 80.00, SD 10.00, n 400. " | |
| "Effect size 0.50, CI 0.30 to 0.70, p value 0.050." | |
| ) | |
| for flag in r.flags: | |
| assert hasattr(flag, "flag_type") | |
| assert hasattr(flag, "severity") | |
| assert hasattr(flag, "description") | |
| assert hasattr(flag, "evidence") | |
| assert hasattr(flag, "suggestion") | |
| def test_empty_text_safe(): | |
| """Empty input returns safe defaults without raising.""" | |
| r = engine.analyze("") | |
| assert r.fingerprint_score == 0.0 | |
| assert r.risk_level == "low" | |
| assert r.flags_count == 0 | |
| def test_round_number_ratio_bounded(): | |
| """Round number ratio always between 0 and 1.""" | |
| r = engine.analyze( | |
| "Scores: 80.00, 90.00, 70.00, 60.00, 50.00. " | |
| "Means: 4.23, 5.67, 3.89, 6.12, 2.45." | |
| ) | |
| assert 0.0 <= r.round_number_ratio <= 1.0 | |
| def test_suspicious_duplicates_list(): | |
| """Suspicious duplicates is always a list.""" | |
| r = engine.analyze( | |
| "Mean 4.5672, SD 1.2341. Mean 4.5672, SD 1.2341. " | |
| "Effect 0.3847, p 0.0234. Effect 0.3847, p 0.0234." | |
| ) | |
| assert isinstance(r.suspicious_duplicates, list) | |
| def test_summary_not_empty(): | |
| """Summary always returns a non-empty string.""" | |
| r = engine.analyze( | |
| "The sample had mean age 34.7 years, SD 12.3, n equals 245. " | |
| "Primary outcome mean was 67.4, SD 15.8, effect size 0.43." | |
| ) | |
| assert isinstance(r.summary, str) | |
| assert len(r.summary) > 10 | |
| def test_risk_level_valid(): | |
| """Risk level is always one of the four valid values.""" | |
| r = engine.analyze( | |
| "Mean 73.47, SD 12.83, n 87. Effect d 0.43. " | |
| "Correlation r 0.34, p 0.023. CI 0.12 to 0.56." | |
| ) | |
| assert r.risk_level in ("low", "medium", "high", "critical") |