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bbannier/Flaky tests.ipynb

Created February 5, 2020 20:24
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Flaky tests.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Some statistical analysis of flaky tests\n",
"\n",
"We currently seem to be cursed by perpetually red CI state. Hardly any test run seems to pass without any failure. Since our CI does not provide a clean signal, it seems in cannot give a lot of convidence in that changes we introduce are safe.\n",
"\n",
"The following presents some statistical analysis on flaky test statistics we have gathered from our internal CI. As a reminder, our CI runs the `stout`, `libprocess` and `mesos` test suites in variing configurations on a number of platforms.\n",
"\n",
"\n",
"## Setting\n",
"\n",
"Currently our CI executes 38,482 tests from 13 setups, and in any CI run we might see a handful of failures. These numbers already suggest that typically tests do not fail often. To get a feeling for our flake rates we can make some back-of-the-envelope estimations.\n",
"\n",
"We can think of running a test as an experiment with a binary outcome, either the test passes or fails. Let's say that test $i$ fails with a probability $p_i$. We are running the test in $N$ setups, and the outcome in any one setup having no effect on the outcomes on other platforms, i.e., the test runs are statistically independent experiments.\n",
"\n",
"In this approximation, each test run is a [Bernoulli trial](https://en.wikipedia.org/wiki/Bernoulli_trial), and the probability to see $k$ successes (passing test runs) from test $i$ when running it $n$ times is described by a [Binominal distribution](https://en.wikipedia.org/wiki/Binomial_distribution),\n",
"\n",
"$$f(k; n, p_i) = {n\\choose k} p_i^k (1-p_i)^{n-k}$$\n",
"\n",
"We take $n$ as the number of setups; for the case where the test never fails in a single test run we are interested in $k=0$ and we are left with a success probability\n",
"\n",
"$$f(p_i) = (1-p_i)^{n}$$"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"plt.style.use('seaborn-darkgrid')\n",
"\n",
"p = np.linspace(0, 1, 100)\n",
"f = (1 - p)**13\n",
"plt.plot(p, f, label='$n=13$')\n",
"plt.xlabel('$p_i$')\n",
"plt.ylabel('probability of success $f(p_i)$')\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above plot show the probability for the test to pass in a single test run for all 13 setups. We can see that the probability drops rapidly as the probability for the test to fail in isolation increases, e.g., if the test fails only in 5% of the cases, the probability to not see this test fail in any one of 13 setups is only 50%.\n",
"\n",
"As an added challenge, we not only run the test in parallel in differnt setups, but also repeatedly for new source code revision. Again, experiements from different revisions are independent (assuming for simplicity that for some stretches in time no change affecting the test in question was made), and we can redo above calculation not for $n$ set to the number of setups, but now $n=n_\\text{setups} \\times n_\\text{runs}$, e.g., for 10 consecutive runs, and see that it drops even faster,"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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gP+AHRHsB54nmAKxPKa5UFLIZtqgPQEQEqDIBuPsg8KKZ3QjcDDwJzAUuB15ILbo6iyaCqQYgIgLJRwF9HjjM3U8H3g9cXP+Q0pPXRDARkWGJl4Jw914Ad98MbK1/SOkpZDNKACIisaTLQa8xs6VE6wEdTzQruGVEw0DVBCQiArUtBbEGODn+fW7dI0pRIZvVjmAiIrGkS0EMAN9PKZbUaSKYiMiIoJaC0H4AIiIjAksAWdUARERiSTeF/xfgeuD2eG5ASynkNApIRKQs6SigLwJ/C1xuZncB18dLRO+UmWWBa4F5QB+w0N1XjfOeO4Db3P2HCWPbqXw8DHRoaIhMJlPv24uItJRETUDu/rS7X0K0NMS7gJVmdreZHV3F5acD7e5+NHApsHSc91wBzEwSUxL5XPShP6hagIhIsgRgZqea2c+B3wKPEiWBzwLVfFs/FrgTwN1XAEeMuffHidYXujNJTEkUslFxNRRURCR5E9AngR+4++8qXzSzy6u4djqwseJ40Mzy7j5gZnOBM4GPA1/Z0Q06O9vI53MJQ47kclm6OtsAmNbZzvSphZru00pyuSzFYkezw2io0MocWnlBZa6npAngqcoPfzO72t0vc/dlVVy7CeiqOM7G8woAPk202uhvgf2BbWb2vLuPqg309vYlDHdEsdhBf1/0uNc3vEWpr6X2s69JsdhBT8+WZofRUKGVObTygsqcVHd31w7PVZUAzOwcYCFwkJktiF/OAQXgsirjWA6cBtxiZvOBJ8on4n6F8rMuB9aO/fCvh3IfgIaCiohUXwP4KfAbYDFRR22GqL1+XYJnLQNONrMH4uvPNrNFwCp3/2WC+9SskI0TgPoARESqTgCfcvfrzewN4IIx5xZXcwN3L41z7dPjvO/yKmNKrFwD6NeCcCIiVSeAF+PfYz+wW+qrdD4eBaQagIhI9QngRDP70JjXMrRYAhhuAlIfgIhI1Qlgu6aaWEt9kg53AmtBOBGRqieCFdz9RuBAwOKfA+OfljE8EUw1ABGRqmsAL8W/d1QTaAkjNQAlABGRqhKAu98V/74RwMz2dPc30gwsDfm4D0B7AoiIJF8OegHwPaDHzDqB88YuC7E7y+fiUUBqAhIRSbwhzBLgKHc/jGhT+P9R/5DSk9dEMBGRYUkTwGZ3Xw/g7muBt+ofUnoKmggmIjKs2rWAriq/P94V7H7gSKKNXVqGJoKJiIyotg/AK36XPz1vA1pqSc2CFoMTERlW7Sig8uifC4BF8XVZoB+4LrXo6mykD0BNQCIiSfsALgROAP4v0U5gf6x3QGnSRDARkRFJE8Ar7v4q0BUP/5xR/5DSo4lgIiIjkiaAjWZ2OjBkZucDe6UQU2o0DFREZETSBLAQeIFoF7D3AH9X94hSVJ4IpmGgIiIJZwK7+2bg0XgpiC+kFFNq4hYg1QBERKh9KYiNZjaNFlsKIpPJUMhl1AksIkLtS0G8nxZcCgKifgANAxURCWwpCIBCLquJYCIiBLYUBJRrAEoAIiK1LAVRdludY2mIfDbDNo0CEhFJthQEgJntAbwbeM7dX08rsLTMmFpg09aBZochItJ0ifoAzOw/ASuALwErzOyTqUSVohlTC/S83d/sMEREmi5pJ/DngcPc/XTg/cDF9Q8pXcV2JQAREUieAEru3gvDk8K21j+kdM3sKLBRCUBEJNlEMGCNmS0F7iWaB7C6/iGlqzg1z8atAwyUhobXBhIRCVHSGsBPgDXAyfHvc+seUcqKUwsAbNqqWoCIhC1pDeC/ufvxqUTSIOUE0PN2P3t0tNSGZiIidZU0AQyZ2TKi+QAlAHdfXPeoUlSZAEREQpY0AfwYyAHdwDriJNBKRhKA5gKISNiSJoDNwDeBDUAX0RaRLWU4AWzZ1uRIRESaK2kC+DJwpLuvM7O9gduBX1dzoZllgWuBeURrCC1091UV5z8PfCI+/JW7fzVhbFVRDUBEJJJ0FNAb7r4OwN1fAzYluPZ0oN3djwYuBZaWT5jZHOAs4BhgPnCKmR2SMLaqTMlnmTYlpz4AEQle4iYgM7sL+D1wONBRXim0is7gY4E74/euMLMjKs69BHzY3QcBzKxAipPMtByEiEjyBPB/Kv5+OeG104GNFceDZpZ39wF37wdeN7MM8HXgUXd/ZuwNOjvbyOdzCR8byeWyFIsdAOzV2UZvf2n4eLKqLHMoQitzaOUFlbmeku4JfOPO37VDm4g6jsuy7j7cEG9m7USjjDazg87l3t7atx8oFjvo6dkCQOeULK9v3jp8PFlVljkUoZU5tPKCypxUd3fXDs8l7QPYFcuBBQBmNh94onwi/uZ/G/C4u59fbgpKy0w1AYmIJG4C2hXLgJPN7AEgA5xtZouAVURzC04A2szs1Pj9l7n7g2kEoj4AEZEGJgB3LwEXjHn56Yq/2xsVS3Fqgbf7S2ztH6S9UFufgohIq2tkE9BuQ8tBiIgEmgBmxglgoyaDiUjAgkwAqgGIiASeADYoAYhIwIJOAKoBiEjIgkwAXe15shklABEJW5AJIJfNML1dcwFEJGxBJgCIN4dXAhCRgAWcAArqBBaRoAWdANQEJCIhCzwBaCKYiIQr8ATQz9DQULNDERFpiqATwGBpiN6+VFeeFhHZbQWbAGZ2aDKYiIQt2AQwQ7OBRSRwwSYALQchIqELOAFEe+FoLoCIhCrYBDBz6hQAzQYWkWAFmwCmFrJMyWXUBCQiwQo2AWQyGc0GFpGgBZsAAPacNoVXNm5tdhgiIk0RdAI4/F1FHnt5E29t05IQIhKeoBPAsXP2YKA0xB9e6Gl2KCIiDRd0Api33wy62vLct/qNZociItJwQSeAfDbDMbNnsnzNm5S0KJyIBCboBABw7Jw92fB2P0+u3dzsUEREGir4BHD0/jPJZVAzkIgEJ/gEMGNqgUP2m8F9a95sdigiIg0VfAIAOG7OHjy7/i3WbtKcABEJhxIAcNycPQG4X7UAEQmIEgDwp3tM5U+K7fzi319l01YtDSEiYcg36kFmlgWuBeYBfcBCd19Vcf5c4HxgALjC3f+lUbFlMhkuPn4Oi+94ioU/e5xrPjaXfae3N+rxIiJN0cgawOlAu7sfDVwKLC2fMLN9gL8HPgj8BXC1mbU1MDY+dMBefPdj7+P1t7Zx9k2P8fBLPQyUNDdARCavhtUAgGOBOwHcfYWZHVFx7khgubv3AX1mtgo4BHiogfFx+LuK/K+/OZSLf/EEF9zy70wtZDl43+kcsNc0utryTGvL0VHIkctmyGUzZDMZMkAmE12fKf8xxvivpm/atDbeequvSU9vjtDKHFp5IcwyH/7uvZiZr/8nSSMTwHRgY8XxoJnl3X1gnHObgRljb9DZ2UY+n6vp4blclmKxY6fve3+xg9s/dyz3PrueR1/s4ZGXerht5Vq2bBus6bkiIrvq8Fkzufnco+p+30YmgE1AV8VxNv7wH+9cF7DdCm29vbVn/WKxg56eLVW//7hZRY6bVRw+HigNsWXbAFu2DVIagsHSEINDQxD9jyHGby5q5goT07va2bQ5rKGtoZU5tPJCmGW2d81M9PlVqbu7a4fnGpkAlgOnAbeY2XzgiYpz/wZcaWbtQBtwELCygbHtVD6bYXp7genthWaHUrVisYOettpqTK0qtDKHVl4Is8ydbXl63t5W9/s2MgEsA042sweImsXPNrNFwCp3/6WZfQe4j6hj+kvuHlaKFxFpsIYlAHcvAReMefnpivPXAdc1Kh4RkdBpIpiISKCUAEREAqUEICISKCUAEZFAKQGIiAQqM6S9cEVEgqQagIhIoJQAREQCpQQgIhKoRi4FkbrdedOZtFRR5s8Dn4gPf+XuX218lPW1szJXvOcO4DZ3/2Hjo6yvKv47nwosIVpm5WHgIndv6Q6+Ksr8BeBMoARc5e7LmhJonZnZUcDX3P1DY14/DfgK0efXj+PVE3bJZKsB7NabzqRkojLPAc4CjgHmA6eY2SFNibK+dljmClcAMxsaVbom+u/cBXwd+Ki7HwU8D+zVjCDrbKIyF4GLgaOBU4BvNyXCOjOzS4DrgfYxrxeAbxGV9QTgPDPbe1efN9kSwKhNZ4BxN51x941AedOZVjdRmV8CPuzug/G3wQIwGRbZm6jMmNnHib4V3tn40FIzUZmPIVpdd6mZ3Qe85u7rGx9i3U1U5reAF4Bp8U+p4dGlYzXwV+O8fhDRwpkb3H0bcD9w/K4+bLIlgHE3ndnBuXE3nWlBOyyzu/e7++tmljGzbwCPuvszTYmyvnZYZjObS9Qs8JVmBJaiif5t7wWcCPwDcCrwX8zsPQ2OLw0TlRmiLzhPAo8A32lkYGlx91uB/nFOpfL5NdkSwC5vOtOCJioz8R4L/xy/58IGx5aWicr8aWA/4LfAZ4FFZvbhxoaXionK/AbwkLuvdfde4F7g0EYHmIKJynwqsC8wG5gFnG5mRzY4vkZK5fNrsiWA5cACgB1sOnOcmbWb2Qx2w01narTDMptZBrgNeNzdz3f3ybKv5Q7L7O6XuPtRcQfaDcA33X0yNAVN9G/7EWCume0Vf0OeT/TNuNVNVOYNwNtAX7x3SA9Q3O4Ok8dTwAFmtoeZTSFq/nlwV286qUYBEeamMzssM5Aj6jBqi0eJAFzm7rv8D6fJJvzv3NzQUrOzf9uXAXfF773F3SfDl5udlfkkYIWZlYjaxO9uYqypMLMzgU53/1Fc9ruIPr9+7O4v7+r9tRSEiEigJlsTkIiIVEkJQEQkUEoAIiKBUgIQEQmUEoCISKCUAEREAqUEICISqMk2EUykYczsZ0RfomYDewMXuvsdzY1KpHqqAYjUbh6wxt2PJFp2e0mT4xFJRAlApAbxInvdQHmDnSeJ9x8ws39sVlwiSSgBiNRmLvBsxXpShwGPxxsPFZoXlkj11AcgUpt5wKy4JpAjqglcQrQM82PNDEykWqoBiNRmHvAL4A/AQ8AP3H05SgDSQlQDEKnNPOA8d//7Ma8fAEyGXdckAEoAIrV5N/Ds2Bfd/ZwmxCJSE+0HICISKPUBiIgESglARCRQSgAiIoFSAhARCZQSgIhIoJQAREQCpQQgIhIoJQARkUD9fy1k5fygiXplAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f = (1 - p)**130\n",
"plt.plot(p, f, label='$n=130$')\n",
"plt.xlabel('$p_i$')\n",
"plt.ylabel('probability of success $f(p_i)$')\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To answer the question how likely we are to see $m$ consecutive failure free runs when executing it on 13 platforms, we can look at the cummulative distribution which tells us the probability to see $m$ failure-free runs in a row. We see that even if a test only fails 0.5% of the time, the probility to see more failure free runs drops below 50% after already 12 runs."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import scipy.stats\n",
"\n",
"k = np.arange(0, 31)\n",
"n = 13\n",
"\n",
"plt.plot(k, scipy.stats.binom.cdf(0, k*n, 0.005), label='$p_i=0.005$')\n",
"plt.plot(k, scipy.stats.binom.cdf(0, k*n, 0.01), label='$p_i=0.01$')\n",
"plt.plot(k, scipy.stats.binom.cdf(0, k*n, 0.05), label='$p_i=0.05$')\n",
"plt.xlabel('number of runs on 13 setups $m$')\n",
"plt.ylabel('probability of success')\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we not only execute a single test, but many thousands of test, we only need a very small average failure probability to always observe at least on single failure (a red CI status)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data\n",
"\n",
"Since we have only limited Jenkins history we use CI results posted to Slack as a store. This is problematic since we only learn about tests when its first failure is reported which makes it hard to determine points when tests became flaky. Additionally, the message format we use does not denote on which platform a test fail; this is problematic when a test suite aborted and reports no results on tests. In this situation we cannot tell whether a test failed on the setup we observed the abort on.\n",
"\n",
"I use a Slack history imported by the tool already used by Benno."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Filter messages from CI bot from Slack history and store report data in a list.\n",
"\n",
"import json\n",
"\n",
"j = json.load(open('messages_core-ci.json'))\n",
"j = [J for J in j if 'bot_id' in J and J['bot_id'] == 'B2HPH9RGB']\n",
"j = [J for J in j if 'attachments' in J and J['attachments']]\n",
"j = [J for J in j if 'fields' in J['attachments'][0] and J['attachments'][0]['fields']]\n",
"j = [J for J in j if 'value' in J['attachments'][0]['fields'][0]]\n",
"\n",
"failures = []\n",
"\n",
"for J in reversed(j):\n",
" lines = J['attachments'][0]['fields'][0]['value'].split('\\n')\n",
"\n",
" branch = '-'.join(lines[0].split()[4].split('-'))\n",
" \n",
" failed_tests = []\n",
" if len(lines) < 4 or 'Failed Tests' not in lines[3]:\n",
" continue\n",
" for l in lines[4:]:\n",
" if not l:\n",
" break\n",
" test = l.split()[0]\n",
" failed_tests.append(test)\n",
" \n",
" # Estimate the number of test setups from the number of tests and the number of aborts.\n",
" l = list(map(lambda x: x.split(',')[0], lines[2].split(': ')))\n",
" num_passed, num_failed = int(l[1]), int(l[2])\n",
" num_empty = failed_tests.count('[empty]')\n",
" num_setups = int(np.round((num_passed + num_failed)/2650 + num_empty))\n",
"\n",
" if not failed_tests:\n",
" failed_tests.append(None)\n",
" \n",
" for test in failed_tests:\n",
" failures.append({'branch':branch, 'test':test, 'setups': num_setups, 'passed': num_passed, 'failed': num_failed, 'empty': num_empty})"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>branch</th>\n",
" <th>empty</th>\n",
" <th>failed</th>\n",
" <th>passed</th>\n",
" <th>setups</th>\n",
" <th>test</th>\n",
" <th>build</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>#4921-asf/master-0e709d31</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>38409</td>\n",
" <td>14</td>\n",
" <td>ROOT_CGROUPS_Stat</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>#4913-asf/master-e65ab60b</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>38658</td>\n",
" <td>16</td>\n",
" <td>SimpleEviction</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>#4913-asf/master-e65ab60b</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>38658</td>\n",
" <td>16</td>\n",
" <td>LocalStoreTestWithTar</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>#4913-asf/master-e65ab60b</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>38658</td>\n",
" <td>16</td>\n",
" <td>ROOT_CGROUPS_CFS_EnableCfs</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>#4913-asf/master-e65ab60b</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>38658</td>\n",
" <td>16</td>\n",
" <td>[empty]</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>#4912-asf/master-9bba87b9</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>38660</td>\n",
" <td>16</td>\n",
" <td>GroupPathWithRestrictivePerms</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>#4912-asf/master-9bba87b9</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>38660</td>\n",
" <td>16</td>\n",
" <td>[empty]</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>#4909-asf/master-bf4e8b39</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>38660</td>\n",
" <td>16</td>\n",
" <td>HttpCachedConcurrent</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>#4909-asf/master-bf4e8b39</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>38660</td>\n",
" <td>16</td>\n",
" <td>[empty]</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>#4905-asf/master-066e0f81</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>38660</td>\n",
" <td>16</td>\n",
" <td>NonRetryableFrrors</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>#4905-asf/master-066e0f81</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>38660</td>\n",
" <td>16</td>\n",
" <td>[empty]</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>#4904-asf/master-81b179fe</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>38661</td>\n",
" <td>16</td>\n",
" <td>[empty]</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>#4901-asf/master-f24736d4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>36062</td>\n",
" <td>16</td>\n",
" <td>PartitionAwareTaskCompletedOnPartitionedAgent</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>#4901-asf/master-f24736d4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>36062</td>\n",
" <td>16</td>\n",
" <td>ExecutorMessageToRecoveredHttpFramework</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>#4901-asf/master-f24736d4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>36062</td>\n",
" <td>16</td>\n",
" <td>[empty]</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>#4901-asf/master-f24736d4</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>36062</td>\n",
" <td>16</td>\n",
" <td>[empty]</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158</th>\n",
" <td>#4888-asf/master-84a6eef1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>38648</td>\n",
" <td>16</td>\n",
" <td>Auth</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>159</th>\n",
" <td>#4888-asf/master-84a6eef1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>38648</td>\n",
" <td>16</td>\n",
" <td>[empty]</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>#4885-asf/master-68cf4372</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>38647</td>\n",
" <td>16</td>\n",
" <td>ROOT_CGROUPS_CFS_EnableCfs</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td>#4885-asf/master-68cf4372</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>38647</td>\n",
" <td>16</td>\n",
" <td>ROOT_CGROUPS_Stat</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>#4885-asf/master-68cf4372</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>38647</td>\n",
" <td>16</td>\n",
" <td>[empty]</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>180</th>\n",
" <td>#4883-asf/master-9b889a10</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>38649</td>\n",
" <td>16</td>\n",
" <td>[empty]</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>#4882-asf/master-8c6e0e57</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>38480</td>\n",
" <td>16</td>\n",
" <td>ROOT_CGROUPS_Stat</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>#4882-asf/master-8c6e0e57</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>38480</td>\n",
" <td>16</td>\n",
" <td>[empty]</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>#4881-asf/master-0f3455cc</td>\n",
" <td>0</td>\n",
" <td>65</td>\n",
" <td>38585</td>\n",
" <td>15</td>\n",
" <td>ROOT_INTERNET_CURL_HealthyTaskViaHTTPWithConta...</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>186</th>\n",
" <td>#4881-asf/master-0f3455cc</td>\n",
" <td>0</td>\n",
" <td>65</td>\n",
" <td>38585</td>\n",
" <td>15</td>\n",
" <td>ROOT_INTERNET_CURL_HealthyTaskViaHTTPSWithCont...</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td>#4881-asf/master-0f3455cc</td>\n",
" <td>0</td>\n",
" <td>65</td>\n",
" <td>38585</td>\n",
" <td>15</td>\n",
" <td>ROOT_INTERNET_CURL_HealthyTaskViaHTTPSWithCont...</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td>#4881-asf/master-0f3455cc</td>\n",
" <td>0</td>\n",
" <td>65</td>\n",
" <td>38585</td>\n",
" <td>15</td>\n",
" <td>ROOT_INTERNET_CURL_HealthyTaskViaHTTPSWithCont...</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>189</th>\n",
" <td>#4881-asf/master-0f3455cc</td>\n",
" <td>0</td>\n",
" <td>65</td>\n",
" <td>38585</td>\n",
" <td>15</td>\n",
" <td>ROOT_INTERNET_CURL_HealthyTaskViaHTTPSWithCont...</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>#4881-asf/master-0f3455cc</td>\n",
" <td>0</td>\n",
" <td>65</td>\n",
" <td>38585</td>\n",
" <td>15</td>\n",
" <td>ROOT_DOCKER_DockerHealthyTask</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10753</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>INTERNET_CURL_InvokeFetchByName</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10754</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>INTERNET_CURL_FetchManifest</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10755</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>INTERNET_CURL_FetchBlob</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10756</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>INTERNET_CURL_FetchImage</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10757</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>INTERNET_CURL_InvokeFetchByName</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10758</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>INTERNET_CURL_FetchManifest</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10759</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>INTERNET_CURL_FetchBlob</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10760</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>INTERNET_CURL_FetchImage</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10761</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>INTERNET_CURL_InvokeFetchByName</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10762</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>ROOT_INTERNET_CURL_ScratchImage</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10763</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>ROOT_INTERNET_CURL_ImageDigest</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10764</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>ROOT_INTERNET_CURL_CommandTaskUser</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10765</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>ROOT_INTERNET_CURL_ScratchImage</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10766</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>ROOT_INTERNET_CURL_ImageDigest</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10767</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>ROOT_INTERNET_CURL_CommandTaskUser</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10768</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>ROOT_INTERNET_CURL_ScratchImage</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10769</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>ROOT_INTERNET_CURL_ImageDigest</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10770</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>ROOT_INTERNET_CURL_CommandTaskUser</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10771</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>ROOT_INTERNET_CURL_LaunchCommandTask</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10772</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>ROOT_INTERNET_CURL_LaunchContainerInHostNetwork</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10773</th>\n",
" <td>#3401-asf/master-81d4b20d</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>33476</td>\n",
" <td>13</td>\n",
" <td>ROOT_INTERNET_CURL…</td>\n",
" <td>488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10873</th>\n",
" <td>#3392-asf/master-40b40d9b</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12085</td>\n",
" <td>5</td>\n",
" <td>None</td>\n",
" <td>489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10874</th>\n",
" <td>#3391-asf/master-eb9ab808</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15141</td>\n",
" <td>6</td>\n",
" <td>None</td>\n",
" <td>490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10875</th>\n",
" <td>#3389-asf/master-8c25ef96</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15141</td>\n",
" <td>6</td>\n",
" <td>None</td>\n",
" <td>491</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10899</th>\n",
" <td>#3373-asf/master-729ec620</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>45751</td>\n",
" <td>17</td>\n",
" <td>ResourceStatistics</td>\n",
" <td>492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10900</th>\n",
" <td>#3373-asf/master-729ec620</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>45751</td>\n",
" <td>17</td>\n",
" <td>EndpointCreateThenOfferRemove</td>\n",
" <td>492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10901</th>\n",
" <td>#3373-asf/master-729ec620</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>45751</td>\n",
" <td>17</td>\n",
" <td>AttachInputToNestedContainerSession/1</td>\n",
" <td>492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10903</th>\n",
" <td>#3369-asf/master-90ce7976</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>42989</td>\n",
" <td>17</td>\n",
" <td>ROOT_INTERNET_CURL_HealthyTaskViaHTTPWithConta...</td>\n",
" <td>493</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10904</th>\n",
" <td>#3369-asf/master-90ce7976</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>42989</td>\n",
" <td>17</td>\n",
" <td>[empty]</td>\n",
" <td>493</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10906</th>\n",
" <td>#3371-asf/master-20c3c34b</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>45753</td>\n",
" <td>17</td>\n",
" <td>RecoverNestedContainer/15</td>\n",
" <td>494</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2800 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" branch empty failed passed setups \\\n",
"2 #4921-asf/master-0e709d31 0 1 38409 14 \n",
"32 #4913-asf/master-e65ab60b 1 4 38658 16 \n",
"33 #4913-asf/master-e65ab60b 1 4 38658 16 \n",
"34 #4913-asf/master-e65ab60b 1 4 38658 16 \n",
"35 #4913-asf/master-e65ab60b 1 4 38658 16 \n",
"36 #4912-asf/master-9bba87b9 1 2 38660 16 \n",
"37 #4912-asf/master-9bba87b9 1 2 38660 16 \n",
"44 #4909-asf/master-bf4e8b39 1 2 38660 16 \n",
"45 #4909-asf/master-bf4e8b39 1 2 38660 16 \n",
"54 #4905-asf/master-066e0f81 1 2 38660 16 \n",
"55 #4905-asf/master-066e0f81 1 2 38660 16 \n",
"56 #4904-asf/master-81b179fe 1 1 38661 16 \n",
"76 #4901-asf/master-f24736d4 2 4 36062 16 \n",
"77 #4901-asf/master-f24736d4 2 4 36062 16 \n",
"78 #4901-asf/master-f24736d4 2 4 36062 16 \n",
"79 #4901-asf/master-f24736d4 2 4 36062 16 \n",
"158 #4888-asf/master-84a6eef1 1 2 38648 16 \n",
"159 #4888-asf/master-84a6eef1 1 2 38648 16 \n",
"174 #4885-asf/master-68cf4372 1 3 38647 16 \n",
"175 #4885-asf/master-68cf4372 1 3 38647 16 \n",
"176 #4885-asf/master-68cf4372 1 3 38647 16 \n",
"180 #4883-asf/master-9b889a10 1 1 38649 16 \n",
"181 #4882-asf/master-8c6e0e57 1 2 38480 16 \n",
"182 #4882-asf/master-8c6e0e57 1 2 38480 16 \n",
"185 #4881-asf/master-0f3455cc 0 65 38585 15 \n",
"186 #4881-asf/master-0f3455cc 0 65 38585 15 \n",
"187 #4881-asf/master-0f3455cc 0 65 38585 15 \n",
"188 #4881-asf/master-0f3455cc 0 65 38585 15 \n",
"189 #4881-asf/master-0f3455cc 0 65 38585 15 \n",
"190 #4881-asf/master-0f3455cc 0 65 38585 15 \n",
"... ... ... ... ... ... \n",
"10753 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10754 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10755 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10756 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10757 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10758 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10759 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10760 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10761 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10762 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10763 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10764 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10765 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10766 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10767 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10768 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10769 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10770 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10771 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10772 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10773 #3401-asf/master-81d4b20d 0 93 33476 13 \n",
"10873 #3392-asf/master-40b40d9b 0 0 12085 5 \n",
"10874 #3391-asf/master-eb9ab808 0 0 15141 6 \n",
"10875 #3389-asf/master-8c25ef96 0 0 15141 6 \n",
"10899 #3373-asf/master-729ec620 0 3 45751 17 \n",
"10900 #3373-asf/master-729ec620 0 3 45751 17 \n",
"10901 #3373-asf/master-729ec620 0 3 45751 17 \n",
"10903 #3369-asf/master-90ce7976 1 2 42989 17 \n",
"10904 #3369-asf/master-90ce7976 1 2 42989 17 \n",
"10906 #3371-asf/master-20c3c34b 0 1 45753 17 \n",
"\n",
" test build \n",
"2 ROOT_CGROUPS_Stat 0 \n",
"32 SimpleEviction 1 \n",
"33 LocalStoreTestWithTar 1 \n",
"34 ROOT_CGROUPS_CFS_EnableCfs 1 \n",
"35 [empty] 1 \n",
"36 GroupPathWithRestrictivePerms 2 \n",
"37 [empty] 2 \n",
"44 HttpCachedConcurrent 3 \n",
"45 [empty] 3 \n",
"54 NonRetryableFrrors 4 \n",
"55 [empty] 4 \n",
"56 [empty] 5 \n",
"76 PartitionAwareTaskCompletedOnPartitionedAgent 6 \n",
"77 ExecutorMessageToRecoveredHttpFramework 6 \n",
"78 [empty] 6 \n",
"79 [empty] 6 \n",
"158 Auth 7 \n",
"159 [empty] 7 \n",
"174 ROOT_CGROUPS_CFS_EnableCfs 8 \n",
"175 ROOT_CGROUPS_Stat 8 \n",
"176 [empty] 8 \n",
"180 [empty] 9 \n",
"181 ROOT_CGROUPS_Stat 10 \n",
"182 [empty] 10 \n",
"185 ROOT_INTERNET_CURL_HealthyTaskViaHTTPWithConta... 11 \n",
"186 ROOT_INTERNET_CURL_HealthyTaskViaHTTPSWithCont... 11 \n",
"187 ROOT_INTERNET_CURL_HealthyTaskViaHTTPSWithCont... 11 \n",
"188 ROOT_INTERNET_CURL_HealthyTaskViaHTTPSWithCont... 11 \n",
"189 ROOT_INTERNET_CURL_HealthyTaskViaHTTPSWithCont... 11 \n",
"190 ROOT_DOCKER_DockerHealthyTask 11 \n",
"... ... ... \n",
"10753 INTERNET_CURL_InvokeFetchByName 488 \n",
"10754 INTERNET_CURL_FetchManifest 488 \n",
"10755 INTERNET_CURL_FetchBlob 488 \n",
"10756 INTERNET_CURL_FetchImage 488 \n",
"10757 INTERNET_CURL_InvokeFetchByName 488 \n",
"10758 INTERNET_CURL_FetchManifest 488 \n",
"10759 INTERNET_CURL_FetchBlob 488 \n",
"10760 INTERNET_CURL_FetchImage 488 \n",
"10761 INTERNET_CURL_InvokeFetchByName 488 \n",
"10762 ROOT_INTERNET_CURL_ScratchImage 488 \n",
"10763 ROOT_INTERNET_CURL_ImageDigest 488 \n",
"10764 ROOT_INTERNET_CURL_CommandTaskUser 488 \n",
"10765 ROOT_INTERNET_CURL_ScratchImage 488 \n",
"10766 ROOT_INTERNET_CURL_ImageDigest 488 \n",
"10767 ROOT_INTERNET_CURL_CommandTaskUser 488 \n",
"10768 ROOT_INTERNET_CURL_ScratchImage 488 \n",
"10769 ROOT_INTERNET_CURL_ImageDigest 488 \n",
"10770 ROOT_INTERNET_CURL_CommandTaskUser 488 \n",
"10771 ROOT_INTERNET_CURL_LaunchCommandTask 488 \n",
"10772 ROOT_INTERNET_CURL_LaunchContainerInHostNetwork 488 \n",
"10773 ROOT_INTERNET_CURL… 488 \n",
"10873 None 489 \n",
"10874 None 490 \n",
"10875 None 491 \n",
"10899 ResourceStatistics 492 \n",
"10900 EndpointCreateThenOfferRemove 492 \n",
"10901 AttachInputToNestedContainerSession/1 492 \n",
"10903 ROOT_INTERNET_CURL_HealthyTaskViaHTTPWithConta... 493 \n",
"10904 [empty] 493 \n",
"10906 RecoverNestedContainer/15 494 \n",
"\n",
"[2800 rows x 7 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a dataframe containing history of failures of `asf/master` builds.\n",
"\n",
"import pymc3 as pm\n",
"import pandas as pd\n",
"\n",
"df = pd.DataFrame.from_dict(failures)\n",
"\n",
"# Filter out builds for the `master` branch.\n",
"master = df.branch.str.contains('asf/master-')\n",
"df = df[master]\n",
"\n",
"# Add a `build` column which indexes builds of `asf/master`.\n",
"df['build'] = df.branch.map({b: i for i, b in enumerate(df.groupby('branch', sort=False).groups.keys())})\n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Auto-assigning NUTS sampler...\n",
"Initializing NUTS using jitter+adapt_diag...\n",
"Multiprocess sampling (2 chains in 10 jobs)\n",
"NUTS: [rate]\n",
"Sampling 2 chains: 100%|██████████| 3000/3000 [00:15<00:00, 197.46draws/s]\n"
]
}
],
"source": [
"NSETUPS = 18\n",
"\n",
"def analyze(test, data): \n",
" build = np.arange(data.build.min(), df.build.max() + 1)\n",
" fails = np.zeros(len(build))\n",
" setups = np.zeros(len(build))\n",
" for b, xs in data.groupby('build'):\n",
" idx = np.where(build == b)[0][0] \n",
" fails[idx] = len(xs)\n",
" setups[idx] = xs.setups.iloc[0]\n",
"\n",
" cutoff = 500\n",
" build = build[-cutoff:]\n",
" fails = fails[-cutoff:]\n",
" setups = setups[-cutoff:]\n",
"\n",
" mask = fails <= NSETUPS # FIXME: set at max\n",
" # mask = fails <= NSETUPS*1000\n",
" unmask = np.logical_not(mask)\n",
" \n",
" build = build.astype(int)\n",
" fails = fails.astype(int)\n",
" setups = setups.astype(int)\n",
"\n",
" ########\n",
"\n",
" with pm.Model() as model:\n",
" # switchpoint = pm.DiscreteUniform('switchpoint', lower=build.min(), upper=build.max(), testval=build.mean())\n",
"\n",
" # early_rate = pm.Uniform('early_rate', lower=0., upper=1., testval=.5)\n",
" # late_rate = pm.Uniform('late_rate', lower=0., upper=1., testval=1e-12)\n",
" # rate = pm.math.switch(switchpoint >= build[mask], early_rate, late_rate)\n",
" BGaussianRandomWalk = pm.Bound(pm.GaussianRandomWalk, lower=0., upper=1.)\n",
" # rate = BGaussianRandomWalk('rate', sd=1, shape=len(build[mask]))\n",
" rate = BGaussianRandomWalk('rate', tau=10, shape=len(build[mask]), testval=1e-12)\n",
" \n",
" #failures = pm.Binomial('failures', p=rate, n=setups[mask], observed=fails[mask])\n",
" failures = pm.Binomial('failures', p=rate, n=18, observed=fails[mask])\n",
" \n",
" #trace = pm.sample(5000, chains=2, cores=10, nuts_kwargs=dict(target_accept=.95))\n",
" trace = pm.sample(sample=40000, tune=1000, chains=2, cores=10, nuts_kwargs=dict(target_accept=.95))\n",
" \n",
" if 0:\n",
" switch = trace['switchpoint']\n",
" switch_hpd = pm.stats.hpd(switch)\n",
" \n",
" early = trace['early_rate']\n",
" early_hpd = pm.stats.hpd(early)\n",
"\n",
" late = trace['late_rate']\n",
" late_hpd = pm.stats.hpd(late)\n",
" \n",
" rate = trace['rate']\n",
" rate_hpd = pm.stats.hpd(rate)\n",
" \n",
" return {\n",
" 'test': test,\n",
" 'build_min': build.min(),\n",
" 'build_max': build.max(),\n",
" 'all': [build, fails],\n",
" 'accepted': [build[mask], fails[mask]],\n",
" 'filtered': [build[unmask], fails[unmask]],\n",
" 'trace': trace,\n",
" 'model': model,\n",
" 'switch': 0, #switch.mean(),\n",
" 'switch_min': 0,#switch_hpd[0],\n",
" 'switch_max': 0,#switch_hpd[1],\n",
" 'early': rate.mean(), #early.mean(),\n",
" 'early_min': rate.mean(), #early_hpd[0],\n",
" 'early_max': rate.mean(), #early_hpd[1],\n",
" 'late': rate.mean(), #late.mean(),\n",
" 'late_min': rate_hpd[0], #late_hpd[0],\n",
" 'late_max': rate_hpd[1], #late_hpd[1],\n",
" }\n",
"\n",
"# summarize(analyze('', df.groupby('test').get_group('[empty]')))\n",
"# summarize(analyze('', df.groupby('test').get_group('LOGROTATE_CustomRotateOptions')))\n",
"t = analyze('', df.groupby('test').get_group('AgentRegi'))\n",
"# summarize(t)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/bbannier/src/cistat/_venv/lib/python3.7/site-packages/matplotlib/axes/_base.py:3604: MatplotlibDeprecationWarning: \n",
"The `ymin` argument was deprecated in Matplotlib 3.0 and will be removed in 3.2. Use `bottom` instead.\n",
" alternative='`bottom`', obj_type='argument')\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x144 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"r = t['trace']['rate'].T\n",
"rm = np.mean(r, axis=1)\n",
"rmin, rmax = pm.stats.hpd(r.T, alpha=0.05).T\n",
"\n",
"plt.plot(rm, label='rate')\n",
"plt.plot(np.median(r, axis=1), label='median rate')\n",
"plt.plot(rmin, label='rate-')\n",
"plt.plot(rmax, label='rate+')\n",
"\n",
"plt.legend()\n",
"# pm.traceplot(t['trace'])\n",
"plt.figure()\n",
"summarize(t)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"TEST = 'LOGROTATE_CustomRotateOptions' # Good example for switch\n",
"# TEST = 'Used'\n",
"\n",
"# TEST = 'PartitionAwareTaskCompletedOnPartitionedAgent' # Good example for no switch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"results = {}\n",
"test = df.groupby('test').get_group(TEST)\n",
"for test, data in df.groupby('test', sort=False):\n",
" print(\"Analyzing {}\".format(test))\n",
" result = analyze(test, data)\n",
" # summarize(result)\n",
" results[test] = result\n",
"\n",
"# Store results.\n",
"import pickle\n",
"with open('results_.pickle', 'wb') as f:\n",
" pickle.dump(results, f, protocol=pickle.HIGHEST_PROTOCOL)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# results = pickle.load(open('results_.pickle', 'rb'))\n",
"\n",
"# Create a dataframe containing summarized information\n",
"rs = {}\n",
"\n",
"for test, result in results.items():\n",
" r = dict(result)\n",
" for col in ['all', 'accepted', 'filtered', 'trace', 'model']:\n",
" r.pop(col)\n",
" rs[test] = r\n",
"\n",
"rs = pd.DataFrame.from_dict(rs, orient='index')\n",
"\n",
"# Add a `better` column measuring wheter a test's failure rate improved. The\n",
"# value is the range [0, 1] with higher values denoting tests failing less.\n",
"phi = np.arcsin(rs.early/np.sqrt(rs.early**2 + rs.late**2))\n",
"rs['late_d'] = (rs.late_max - rs.late_min)\n",
"rs[rs.late_d<0.5].sort_values(by='late_d', ascending=True)[['switch', 'switch_min', 'switch_max', 'late_d', 'early', 'early_min', 'early_max', 'late', 'late_min', 'late_max']]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def summarize(result):\n",
" plt.plot(*result['accepted'], '.', alpha=0.2)\n",
" plt.plot(*result['filtered'], '.', alpha=0.2)\n",
" plt.xlabel('build')\n",
" plt.ylabel('number of failures')\n",
"\n",
" fails_max = result['all'][1].max()\n",
" \n",
" plt.vlines(result['switch'], 0, fails_max, alpha=0.5, color='orange')\n",
" plt.fill_betweenx([0, fails_max], result['switch_min'], result['switch_max'], alpha=0.2, color='orange')\n",
"\n",
" plt.plot([result['build_min'], result['switch']], [result['early']*NSETUPS, result['early']*NSETUPS], alpha=0.5, color='gray')\n",
" plt.fill_between([result['build_min'], result['switch']], result['early_min']*NSETUPS, result['early_max']*NSETUPS, alpha=0.2, color='gray')\n",
"\n",
" plt.plot([result['switch'], result['build_max']], [result['late']*NSETUPS, result['late']*NSETUPS], alpha=0.5, color='gray')\n",
" plt.fill_between([result['switch'], result['build_max']], result['late_min']*NSETUPS, result['late_max']*NSETUPS, alpha=0.2, color='gray');\n",
"\n",
" pm.traceplot(result['trace'])\n",
"\n",
" from IPython.display import display, HTML\n",
" # display(HTML(pm.summary(result['trace']).round(3).to_html()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"summarize(results['ROOT_Metrics'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.scatterplot(x='late', y='late_z', hue='early', data=rs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# ppc = pm.sample_ppc(results['ROOT_INTERNET_CURL_HealthyTaskViaHTTPSWithContainerImage']['trace'], samples=100, model=results['ROOT_INTERNET_CURL_HealthyTaskViaHTTPSWithContainerImage']['model'], vars='failures')\n",
"# ppc.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"t = results['ROOT_INTERNET_CURL_HealthyTaskViaHTTPSWithContainerImage']\n",
"s = pm.sample_ppc(t['trace'], model=t['model'], samples=1)\n",
"b = result['all'][0]\n",
"# result\n",
"# len(s['failures'][0]), len(b)\n",
"summarize(t)\n",
"pm.plot_posterior(t['trace']);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"summarize(results['ResourceStatistics'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Are tests getting more flaky?\n",
"\n",
"Since we only know about a test's existance after it has failed once, we cannot examine if initially non-flaky tests become more flaky. We can however check if a test started to fail more after some time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"\n",
"rs['switch_'] = rs.switch - rs.build_min\n",
"rs['dbetter'] = rs.better_sd/rs.better\n",
"sns.scatterplot(x='switch_', y='dbetter', hue='late', size=1/rs.dbetter, data=rs)\n",
"plt.semilogy()\n",
"plt.plot([0, rs.build_max.max()], [1, 1], alpha=0.5);\n",
"\n",
"plt.figure()\n",
"sns.scatterplot(x='switch_', y='better', hue='late', size=np.log(rs.better), data=rs);\n",
"plt.semilogy()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.groupby('test').get_group('Used')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pm.GaussianRandomWalk?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s = df[['build', 'setups']].drop_duplicates()\n",
"plt.plot(s.build, s.setups)\n",
"np.median(s.setups)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pm.GaussianRandomWalk?"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
},
{
"data": {
"image/png": 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C\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"d = df.groupby('test').get_group('')\n",
"print(len(d.build))\n",
"plt.plot(d.build, d.failed, 'o--');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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