| Index: tools/bisect-perf-regression_test.py
|
| diff --git a/tools/bisect-perf-regression_test.py b/tools/bisect-perf-regression_test.py
|
| new file mode 100644
|
| index 0000000000000000000000000000000000000000..44d526edfd4845b672388baa24daa1cae50cda41
|
| --- /dev/null
|
| +++ b/tools/bisect-perf-regression_test.py
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| @@ -0,0 +1,79 @@
|
| +# Copyright 2014 The Chromium Authors. All rights reserved.
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| +# 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):
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| + bisect_perf_module.CalculateTruncatedMean([], 0)
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| +
|
| + def testCalculateMeanSingleNum(self):
|
| + """Tests the CalculateMean function with a single number."""
|
| + self.assertEqual(3.0, bisect_perf_module.CalculateMean([3]))
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| +
|
| + def testCalculateMeanShortList(self):
|
| + """Tests the CalculateMean function with a short list."""
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| + self.assertEqual(0.5, bisect_perf_module.CalculateMean([-3, 0, 1, 4]))
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| +
|
| + def testCalculateMeanCompareAlternateImplementation(self):
|
| + """Tests CalculateMean by comparing against an alternate implementation."""
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| + def AlternateMeanFunction(values):
|
| + """Simple arithmetic mean function."""
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| + return sum(values) / float(len(values))
|
| + test_values_lists = [[1], [5, 6.5, 1.2, 3], [-3, 0, 1, 4],
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| + [-3, -1, 0.12, 0.752, 3.33, 8, 16, 32, 439]]
|
| + for values in test_values_lists:
|
| + self.assertEqual(
|
| + AlternateMeanFunction(values),
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| + bisect_perf_module.CalculateMean(values))
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| +
|
| + def testCalculateConfidence(self):
|
| + """Tests the confidence calculation."""
|
| + bad_values = [[0, 1], [1, 2]]
|
| + good_values = [[6, 7], [7, 8]]
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| + # Closest means are mean(1, 2) and mean(6, 7).
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| + distance = 6.5 - 1.5
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| + # Standard deviation of [n-1, n, n, n+1] is 0.8165.
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| + stddev_sum = 0.8165 + 0.8165
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| + # Expected confidence is an int in the range [0, 100].
|
| + expected_confidence = min(100, int(100 * distance / float(stddev_sum)))
|
| + self.assertEqual(
|
| + expected_confidence,
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| + 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
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| + # between groups is zero, and thus confidence is zero.
|
| + self.assertEqual(
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| + 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]]
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| + good_values = [[1.2, 1.2], [1.2, 1.2]]
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| + # Standard deviation in both groups is zero, so confidence is 100.
|
| + self.assertEqual(
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| + 100, bisect_perf_module.CalculateConfidence(bad_values, good_values))
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| +
|
| +if __name__ == '__main__':
|
| + unittest.main()
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|
|