These are the database objects used while finding the regression range. 1/3

appengine/findit/model/flake/master_flake_analysis.py

Index: appengine/findit/model/flake/master_flake_analysis.py
diff --git a/appengine/findit/model/flake/master_flake_analysis.py b/appengine/findit/model/flake/master_flake_analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0cb54ec1294db1c19d9f725101a2824b29748fe
--- /dev/null
+++ b/appengine/findit/model/flake/master_flake_analysis.py
@@ -0,0 +1,72 @@
+# Copyright 2014 The Chromium Authors. All rights reserved.

[chanli, 2016/07/07 23:38:28]

2014 -> 2016

[caiw, 2016/07/14 00:59:40]

Done.

+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+from google.appengine.ext import ndb
+
+from common import constants
+from model import analysis_status
+from model.flake.base_analysis import BaseAnalysis
+from model.flake.flake_swarming_task import FlakeSwarmingTask
+
+class MasterFlakeAnalysis(BaseAnalysis):
+  """Represents an analysis of a candidate flaky test in a Chromium Waterfall.
+
+  """
+  @staticmethod
+  def CreateAnalysisId(master_name, builder_name, step_name):
+    return '%s/%s/%s' % (master_name, builder_name, step_name)
+
+  @ndb.ComputedProperty
+  def master_name(self):

[stgao, 2016/07/08 22:51:27]

Same here for using BaseBuildModel class.

[caiw, 2016/07/14 00:59:40]

Done.

+    return self.key.pairs()[0][1].split('/')[0]
+
+  @ndb.ComputedProperty
+  def builder_name(self):
+    return self.key.pairs()[0][1].split('/')[1]
+
+  @ndb.ComputedProperty
+  def step_name(self):
+    return self.key.pairs()[0][1].split('/')[2]
+
+  @ndb.ComputedProperty
+  def build_number(self):
+    return 0
+
+  @staticmethod
+  def _CreateKey(master_name, builder_name, step_name):  # pragma: no cover
+    return ndb.Key('MasterFlakeAnalysis',
+                   MasterFlakeAnalysis.CreateAnalysisId(
+                       master_name, builder_name, step_name))
+
+  @staticmethod
+  def Create(master_name, builder_name, step_name):  # pragma: no cover
+    return MasterFlakeAnalysis(
+
+        key=MasterFlakeAnalysis._CreateKey(
+            master_name, builder_name, step_name))
+
+  @staticmethod
+  def Get(master_name, builder_name, step_name):  # pragma: no cover
+    return MasterFlakeAnalysis._CreateKey(
+        master_name, builder_name, step_name).get()
+
+  def generate_data(self):
+    data = []
+    # Generates data in the format which the template can read
+    # Go through list of flake_swarming_tasks
+    for fst in self.flake_swarming_tasks:
+      # Go through list of flake_swarming_task_results
+      for fstr in fst.flake_swarming_task_results:
+        # Append results to a list (this is a placeholder)
+        data.append((fstr.successes, fstr.tries,
+                     fstr.master_name(), fstr.builder_name(),
+                     fstr.step_name(), fstr.build_number(),
+                     fstr.test_name()))
+  # List of tested build_numbers and their corresponding success rates.
+  # We need to keep these sorted manually.
+  # This is not going to work when we have multiple tests.
+  build_numbers = ndb.IntegerProperty(indexed=True, repeated=True)
+  success_rates = ndb.FloatProperty(indexed=False, repeated=True)

[stgao, 2016/07/08 22:51:27]

Is the success rate per-test? If so, should we make test_name as part of the
key?

[caiw, 2016/07/14 00:59:40]

Done.

+  flake_swarming_tasks = ndb.KeyProperty(
+      kind='FlakeSwarmingTask', repeated=True)