Add bpf_varianz_test/tests/test_core.py

bpf_varianz_test/tests/test_core.py

@@ -0,0 +1,53 @@
+import pytest
+import math
+import numpy as np
+from bpf_varianz_test import core
+
+
+def test_perform_levene_test_basic():
+    kprobe_samples = [1.0, 1.2, 1.1, 1.3, 1.05]
+    bpf_samples = [1.0, 1.0, 1.02, 0.98, 1.01]
+
+    p_value, significant = core.perform_levene_test(kprobe_samples, bpf_samples)
+
+    assert isinstance(p_value, float)
+    assert isinstance(significant, bool)
+    assert 0.0 <= p_value <= 1.0
+
+    # Erwartung: Varianzen unterschiedlich -> p-Wert klein oder merkbar signifikant
+    # (nicht streng testen, da Stichproben klein)
+    assert p_value > 0 or significant in (True, False)
+
+
+def test_perform_levene_test_identical_samples():
+    kprobe_samples = [1.0, 1.1, 0.9, 1.05, 0.95]
+    bpf_samples = list(kprobe_samples)
+
+    p_value, significant = core.perform_levene_test(kprobe_samples, bpf_samples)
+    assert pytest.approx(p_value, rel=1e-6) == 1.0 or p_value > 0.9
+    assert significant is False
+
+
+def test_bootstrap_variance_confidence_interval_typical_range():
+    samples = [1.0, 1.1, 0.9, 1.2, 1.05, 0.95, 1.0]
+    low, high = core.bootstrap_variance_confidence_interval(samples, 500)
+
+    assert isinstance(low, float)
+    assert isinstance(high, float)
+    assert low < high
+
+    var_emp = np.var(samples, ddof=1)
+    # Das empirische Varianzmaß sollte im Intervall liegen
+    assert low <= var_emp <= high
+
+
+def test_bootstrap_variance_confidence_interval_increasing_sample():
+    samples_small_var = [1.0, 1.01, 0.99, 1.0, 1.02]
+    samples_large_var = [1.0, 1.3, 0.7, 1.4, 0.6]
+
+    low_s, high_s = core.bootstrap_variance_confidence_interval(samples_small_var, 200)
+    low_l, high_l = core.bootstrap_variance_confidence_interval(samples_large_var, 200)
+
+    # Erwartung: Varianzintervall der größeren Streuung weiter/höher
+    assert (high_l - low_l) >= (high_s - low_s)
+    assert high_l > high_s