diff --git a/analytics/tests/test_utils.py b/analytics/tests/test_utils.py
index bf12199..0dde71a 100644
--- a/analytics/tests/test_utils.py
+++ b/analytics/tests/test_utils.py
@@ -1,11 +1,133 @@
+import utils
 import unittest
+import numpy as np
+import pandas as pd
+import math
 
 class TestUtils(unittest.TestCase):
 
     #example test for test's workflow purposes
     def test_segment_parsion(self):
         self.assertTrue(True)
-
+    
+    def test_confidence_all_normal_value(self):
+        segment = [1, 2, 0, 6, 8, 5, 3]
+        utils_result = utils.find_confidence(segment)
+        result = 1.6
+        relative_tolerance = 1e-2
+        self.assertTrue(math.isclose(utils_result, result, rel_tol = relative_tolerance))
+    
+    def test_confidence_all_nan_value(self):
+        segment = [np.NaN, np.NaN, np.NaN, np.NaN]
+        self.assertEqual(utils.find_confidence(segment), 0)
+    
+    def test_confidence_with_nan_value(self):
+        data = [np.NaN, np.NaN, 0, 8]
+        utils_result = utils.find_confidence(data)
+        result = 1.6
+        relative_tolerance = 1e-2
+        self.assertTrue(math.isclose(utils_result, result, rel_tol = relative_tolerance))
+    
+    def test_interval_all_normal_value(self):
+        data = [1, 2, 1, 2, 4, 1, 2, 4, 5, 6]
+        data = pd.Series(data)
+        center = 4
+        window_size = 2
+        result = [1, 2, 4, 1, 2]
+        self.assertEqual(list(utils.get_interval(data, center, window_size)), result)
+    
+    def test_interval_wrong_ws(self):
+        data = [1, 2, 4, 1, 2, 4]
+        data = pd.Series(data)
+        center = 3
+        window_size = 6
+        result = [1, 2, 4, 1, 2, 4]
+        self.assertEqual(list(utils.get_interval(data, center, window_size)), result)
+    
+    def test_subtract_min_without_nan(self):
+        segment = [1, 2, 4, 1, 2, 4]    
+        segment = pd.Series(segment)
+        result = [0, 1, 3, 0, 1, 3]
+        utils_result = list(utils.subtract_min_without_nan(segment))
+        self.assertEqual(utils_result, result)
+    
+    def test_subtract_min_with_nan(self):
+        segment = [np.NaN, 2, 4, 1, 2, 4]
+        segment = pd.Series(segment)
+        result = [2, 4, 1, 2, 4]
+        utils_result = list(utils.subtract_min_without_nan(segment)[1:])
+        self.assertEqual(utils_result, result)
+    
+    def test_get_convolve(self):
+        data = [1, 2, 3, 2, 2, 0, 2, 3, 4, 3, 2, 1, 1, 2, 3, 4, 3, 2, 0]
+        data = pd.Series(data)
+        pattern_index = [2, 8, 15]
+        window_size = 2
+        av_model = [1, 2, 3, 2, 1]
+        result = []
+        self.assertNotEqual(utils.get_convolve(pattern_index, av_model, data, window_size), result)
+    
+    def test_get_convolve_with_nan(self):
+        data = [1, 2, 3, 2, np.NaN, 0, 2, 3, 4, np.NaN, 2, 1, 1, 2, 3, 4, 3, np.NaN, 0]
+        data = pd.Series(data)
+        pattern_index = [2, 8, 15]
+        window_size = 2
+        av_model = [1, 2, 3, 2, 1]
+        result = utils.get_convolve(pattern_index, av_model, data, window_size)
+        for val in result:
+            self.assertFalse(np.isnan(val))
+    
+    def test_get_convolve_empty_data(self):
+        data = []
+        pattern_index = []
+        window_size = 2
+        av_model = []
+        result = []
+        self.assertEqual(utils.get_convolve(pattern_index, av_model, data, window_size), result)
+    
+    def test_get_distribution_density(self):
+        segment = [1, 1, 1, 3, 5, 5, 5]
+        segment = pd.Series(segment)
+        result = (3, 5, 1)
+        self.assertEqual(utils.get_distribution_density(segment), result)
+    
+    def test_find_jump_parameters_center(self):
+        segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
+        segment = pd.Series(segment)
+        jump_center = [10, 11]
+        self.assertIn(utils.find_jump_parameters(segment, 0)[0], jump_center)
+    
+    def test_find_jump_parameters_height(self):
+        segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
+        segment = pd.Series(segment)
+        jump_height = [3.5, 4]
+        self.assertGreaterEqual(utils.find_jump_parameters(segment, 0)[1], jump_height[0])
+        self.assertLessEqual(utils.find_jump_parameters(segment, 0)[1], jump_height[1])
+    
+    def test_find_jump_parameters_length(self):
+        segment = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
+        segment = pd.Series(segment)
+        jump_length = 2
+        self.assertEqual(utils.find_jump_parameters(segment, 0)[2], jump_length)
+    
+    def test_find_drop_parameters_center(self):
+        segment = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+        segment = pd.Series(segment)
+        drop_center = [14, 15]
+        self.assertIn(utils.find_drop_parameters(segment, 0)[0], drop_center)
+    
+    def test_find_drop_parameters_height(self):
+        segment = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+        segment = pd.Series(segment)
+        drop_height = [3.5, 4]
+        self.assertGreaterEqual(utils.find_drop_parameters(segment, 0)[1], drop_height[0])
+        self.assertLessEqual(utils.find_drop_parameters(segment, 0)[1], drop_height[1])
+    
+    def test_find_drop_parameters_length(self):
+        segment = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+        segment = pd.Series(segment)
+        drop_length = 2
+        self.assertEqual(utils.find_drop_parameters(segment, 0)[2], drop_length)
 
 if __name__ == '__main__':
     unittest.main()