diff --git a/tests/gcm/test_divergence.py b/tests/gcm/test_divergence.py
index f7052f5b2..99db4a34b 100644
--- a/tests/gcm/test_divergence.py
+++ b/tests/gcm/test_divergence.py
@@ -11,7 +11,7 @@ from dowhy.gcm.divergence import (
 
 
 @flaky(max_runs=5)
-def test_estimate_kl_divergence_continuous():
+def test_given_simple_gaussian_data_when_estimate_kl_divergence_continuous_then_returns_expected_result():
     X = np.random.normal(0, 1, 10000)
     Y = np.random.normal(1, 1, 10000)
 
@@ -20,7 +20,7 @@ def test_estimate_kl_divergence_continuous():
 
 
 @flaky(max_runs=5)
-def test_estimate_kl_divergence_categorical():
+def test_given_simple_categorical_data_estimate_kl_divergence_categorical_then_returns_expected_result():
     X = np.random.choice(4, 1000, replace=True, p=[0.25, 0.5, 0.125, 0.125]).astype(str)
     Y = np.random.choice(4, 1000, replace=True, p=[0.5, 0.25, 0.125, 0.125]).astype(str)
 
@@ -30,7 +30,7 @@ def test_estimate_kl_divergence_categorical():
     )
 
 
-def test_estimate_kl_divergence_of_probabilities():
+def test_given_probability_vectors_when_estimate_kl_divergence_of_probabilities_then_returns_expected_result():
     assert estimate_kl_divergence_of_probabilities(
         np.array([[0.25, 0.5, 0.125, 0.125], [0.5, 0.25, 0.125, 0.125]]),
         np.array([[0.5, 0.25, 0.125, 0.125], [0.25, 0.5, 0.125, 0.125]]),
@@ -38,7 +38,7 @@ def test_estimate_kl_divergence_of_probabilities():
 
 
 @flaky(max_runs=5)
-def test_auto_estimate_kl_divergence_continuous():
+def test_given_simple_gaussian_data_when_auto_estimate_kl_divergence_then_correctly_selects_continuous_version():
     X = np.random.normal(0, 1, 10000)
     Y = np.random.normal(1, 1, 10000)
 
@@ -47,7 +47,7 @@ def test_auto_estimate_kl_divergence_continuous():
 
 
 @flaky(max_runs=5)
-def test_auto_estimate_kl_divergence_categorical():
+def test_given_categorical_data_when_auto_estimate_kl_divergence_then_correctly_selects_categorical_version():
     X = np.random.choice(4, 1000, replace=True, p=[0.25, 0.5, 0.125, 0.125]).astype(str)
     Y = np.random.choice(4, 1000, replace=True, p=[0.5, 0.25, 0.125, 0.125]).astype(str)
 
@@ -55,7 +55,7 @@ def test_auto_estimate_kl_divergence_categorical():
     assert auto_estimate_kl_divergence(X, Y) == approx(0.25 * np.log(0.25 / 0.5) + 0.5 * np.log(0.5 / 0.25), abs=0.1)
 
 
-def test_auto_estimate_kl_divergence_probabilities():
+def test_given_probability_vectors_when_auto_estimate_kl_divergence_then_correctly_selects_probability_version():
     assert auto_estimate_kl_divergence(
         np.array([[0.25, 0.5, 0.125, 0.125], [0.5, 0.25, 0.125, 0.125]]),
         np.array([[0.5, 0.25, 0.125, 0.125], [0.25, 0.5, 0.125, 0.125]]),