Refactor perf bisect script _CalculateConfidence method.

tools/bisect-perf-regression_test.py

+# Copyright 2014 The Chromium Authors. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+import unittest
+
+# Special import necessary because filename contains dash characters.
+bisect_perf_module = __import__('bisect-perf-regression')
+
+
+class BisectPerfRegressionTest(unittest.TestCase):
+  """Test case for top-level functions in the bisect-perf-regrssion module."""
+
+  def setUp(self):
+    """Sets up the test environment before each test method."""
+    pass
+
+  def tearDown(self):
+    """Cleans up the test environment after each test method."""
+    pass
+
+  def testCalculateTruncatedMeanRaisesError(self):
+    """CalculateTrunctedMean raises an error when passed an empty list."""
+    with self.assertRaises(TypeError):
+      bisect_perf_module.CalculateTruncatedMean([], 0)
+
+  def testCalculateMeanSingleNum(self):
+    """Tests the CalculateMean function with a single number."""
+    self.assertEqual(3.0, bisect_perf_module.CalculateMean([3]))
+
+  def testCalculateMeanShortList(self):
+    """Tests the CalculateMean function with a short list."""
+    self.assertEqual(0.5, bisect_perf_module.CalculateMean([-3, 0, 1, 4]))
+
+  def testCalculateMeanCompareAlternateImplementation(self):
+    """Tests CalculateMean by comparing against an alternate implementation."""
+    def AlternateMeanFunction(values):
+      """Simple arithmetic mean function."""
+      return sum(values) / float(len(values))
+    test_values_lists = [[1], [5, 6.5, 1.2, 3], [-3, 0, 1, 4],
+                         [-3, -1, 0.12, 0.752, 3.33, 8, 16, 32, 439]]
+    for values in test_values_lists:
+      self.assertEqual(
+          AlternateMeanFunction(values),
+          bisect_perf_module.CalculateMean(values))
+
+  def testCalculateConfidence(self):
+    """Tests the confidence calculation."""
+    bad_values = [[0, 1], [1, 2]]
+    good_values = [[6, 7], [7, 8]]
+    # Closest means are mean(1, 2) and mean(6, 7).
+    distance = 6.5 - 1.5
+    # Standard deviation of [n-1, n, n, n+1] is 0.8165.
+    stddev_sum = 0.8165 + 0.8165
+    # Expected confidence is an int in the range [0, 100].
+    expected_confidence = min(100, int(100 * distance / float(stddev_sum)))
+    self.assertEqual(
+        expected_confidence,
+        bisect_perf_module.CalculateConfidence(bad_values, good_values))
+
+  def testCalculateConfidence0(self):
+    """Tests the confidence calculation when it's expected to be 0."""
+    bad_values = [[0, 1], [1, 2], [4, 5], [0, 2]]
+    good_values = [[4, 5], [6, 7], [7, 8]]
+    # Both groups have value lists with means of 4.5, which means distance
+    # between groups is zero, and thus confidence is zero.
+    self.assertEqual(
+        0, bisect_perf_module.CalculateConfidence(bad_values, good_values))
+
+  def testCalculateConfidence100(self):
+    """Tests the confidence calculation when it's expected to be 100."""
+    bad_values = [[1, 1], [1, 1]]
+    good_values = [[1.2, 1.2], [1.2, 1.2]]
+    # Standard deviation in both groups is zero, so confidence is 100.
+    self.assertEqual(
+        100, bisect_perf_module.CalculateConfidence(bad_values, good_values))
+
+if __name__ == '__main__':
+  unittest.main()