[Findit] Adding suspected CL to master flake analysis in preparation for try jobs

appengine/findit/model/flake/master_flake_analysis.py

 # Copyright 2016 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 base64
+import logging
 
 from google.appengine.ext import ndb
 
+from gae_libs.http.http_client_appengine import HttpClientAppengine
+from gae_libs.gitiles.cached_gitiles_repository import CachedGitilesRepository
 from gae_libs.model.versioned_model import VersionedModel
 from model import result_status
 from model import triage_status
 from model.base_analysis import BaseAnalysis
 from model.base_build_model import BaseBuildModel
 from model.base_triaged_model import TriagedModel
+from model.flake.flake_suspected_cl import FlakeSuspectedCL
 from model.flake.flake_swarming_task import FlakeSwarmingTaskData
 
 
 class DataPoint(ndb.Model):
   build_number = ndb.IntegerProperty(indexed=False)
   pass_rate = ndb.FloatProperty(indexed=False)
   task_id = ndb.StringProperty(indexed=False)
   commit_position = ndb.IntegerProperty(indexed=False)
   git_hash = ndb.StringProperty(indexed=False)
   previous_build_commit_position = ndb.IntegerProperty(indexed=False)
   previous_build_git_hash = ndb.StringProperty(indexed=False)
   blame_list = ndb.StringProperty(repeated=True)
 
 
 class MasterFlakeAnalysis(
     BaseAnalysis, BaseBuildModel, VersionedModel, TriagedModel):
   """Represents an analysis of a flaky test on a Waterfall test cycle."""
 
+  GIT_REPO = CachedGitilesRepository(
+      HttpClientAppengine(),
+      'https://chromium.googlesource.com/chromium/src.git')
+
   @ndb.ComputedProperty
   def step_name(self):
     return self.key.pairs()[0][1].split('/')[3]
 
   @ndb.ComputedProperty
   def test_name(self):
     return base64.urlsafe_b64decode(self.key.pairs()[0][1].split('/')[4])
 
   @property
   def error_message(self):
@@ skipping 45 matching lines @@
   def UpdateTriageResult(self, triage_result, suspect_info, user_name,
                          version_number=None):
     super(MasterFlakeAnalysis, self).UpdateTriageResult(
         triage_result, suspect_info, user_name, version_number=version_number)
 
     if triage_result == triage_status.TRIAGED_CORRECT:
       self.result_status = result_status.FOUND_CORRECT
     else:
       self.result_status = result_status.FOUND_INCORRECT
 
+  def UpdateSuspectedCL(self, repo_name, revision):
+    """Sets suspected_cl information."""
+    change_log = self.GIT_REPO.GetChangeLog(revision)

[stgao, 2017/01/05 21:28:03]

It seems better to move this out to the caller code, and keep the ndb model just
to deal with data persistence and read.

What do you think?

[lijeffrey, 2017/01/06 05:18:16]

Sounds good, by moving change_log outside to the caller, this function is no
longer necessary since it simply crafts a FlakeCulprit entity and sets it. This
function will be implemented in the try job pipeline CL.

+
+    if change_log:
+      code_review_url = change_log.code_review_url or change_log.commit_url

[stgao, 2017/01/05 21:28:03]

See comment above.

[lijeffrey, 2017/01/06 05:18:16]

Done.

+      suspected_cl = FlakeSuspectedCL.Create(
+          repo_name, revision, change_log.commit_position, code_review_url)
+      self.suspected_cl = suspected_cl
+    else:  # pragma: no cover
+      logging.error('Unable to retrieve change logs for %s', revision)
+
+  def GetSuspectedFlakeDataPoint(self):
+    """Gets the corresponding data point to the suspected flake build."""
+    if self.suspected_flake_build_number is not None:
+      for data_point in self.data_points:
+        if data_point.build_number == self.suspected_flake_build_number:
+          return data_point
+
+    return None
+
   def Reset(self):
     super(MasterFlakeAnalysis, self).Reset()
     self.original_master_name = None
     self.original_builder_name = None
     self.original_build_number = None
     self.original_step_name = None
     self.original_test_name = None
     self.bug_id = None
     self.swarming_rerun_results = []
     self.error = None
     self.correct_regression_range = None
     self.correct_culprit = None
     self.algorithm_parameters = None
     self.suspected_flake_build_number = None
+    self.suspected_cl = None
+    self.try_job_status = None
     self.data_points = []
     self.result_status = None
 
   # The original build/step/test in which a flake actually occurred.
   # A CQ trybot step has to be mapped to a Waterfall buildbot step.
   # A gtest suite.PRE_PRE_test has to be normalized to suite.test.
   original_master_name = ndb.StringProperty(indexed=True)
   original_builder_name = ndb.StringProperty(indexed=True)
   original_build_number = ndb.IntegerProperty(indexed=True)
   original_step_name = ndb.StringProperty(indexed=True)
@@ skipping 29 matching lines @@
   #     'max_build_numbers_to_look_back': 500,
   #     'max_flake_in_a_row': 4,
   #     'max_stable_in_a_row': 4,
   #     'upper_flake_threshold': 0.98
   # }
   algorithm_parameters = ndb.JsonProperty(indexed=False)
 
   # The suspected build number to have introduced the flakiness.
   suspected_flake_build_number = ndb.IntegerProperty()
 
+  # The suspected CL associated with the try job results, if any.
+  suspected_cl = ndb.LocalStructuredProperty(FlakeSuspectedCL)
+
+  # The status of the try job, if any. None if no suspected flake build number
+  # yet.
+  try_job_status = ndb.IntegerProperty(indexed=False)
+
   # The data points used to plot the flakiness graph build over build.
   data_points = ndb.LocalStructuredProperty(
       DataPoint, repeated=True, compressed=True)
 
   # Whether the analysis was triggered by a manual request through check flake,
   # Findit's automatic analysis upon detection, or Findit API.
   triggering_source = ndb.IntegerProperty(default=None, indexed=True)
 
   # Who triggered the analysis. Used for differentiating between manual and
   # automatic runs, and determining the most active users to gather feedback.
   triggering_user_email = ndb.StringProperty(default=None, indexed=False)
 
   # Overall conclusion of analysis result for the flake. Found untriaged, Found
   # Correct, etc. used to filter what is displayed on the check flake dashboard.
   result_status = ndb.IntegerProperty(indexed=True)