# Copyright (c) 2019 Cisco and/or its affiliates. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at: # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Script for determining whether per-patch perf test votes -1. This script assumes there exist two text files with processed BMRR results, located at hardcoded relative paths (subdirs thereof), having several lines of json-parseable lists of float values, corresponding to testcase results. This script then uses jumpavg library to determine whether there was a regression, progression or no change for each testcase. If number of tests does not match, or there was a regression, this script votes -1 (by exiting with code 1), otherwise it votes +1 (exit 0). """ import json import sys from resources.libraries.python import jumpavg def hack(value_list): """Return middle two quartiles, hoping to reduce influence of outliers. Currently "middle two" is "all", but that can change in future. :param value_list: List to pick subset from. :type value_list: list of float :returns: New list containing middle values. :rtype: list of float """ tmp = sorted(value_list) eight = len(tmp) / 8 ret = tmp[3*eight:-eight] return tmp # ret iteration = -1 parent_iterations = list() current_iterations = list() num_tests = None while 1: iteration += 1 parent_lines = list() current_lines = list() filename = f"csit_parent/{iteration}/results.txt" try: with open(filename) as parent_file: parent_lines = parent_file.readlines() except IOError: break num_lines = len(parent_lines) filename = f"csit_current/{iteration}/results.txt" with open(filename) as current_file: current_lines = current_file.readlines() if num_lines != len(current_lines): print(f"Number of tests does not match within iteration {iteration}") sys.exit(1) if num_tests is None: num_tests = num_lines elif num_tests != num_lines: print( f"Number of tests does not match previous at iteration {iteration}" ) sys.exit(1) parent_iterations.append(parent_lines) current_iterations.append(current_lines) exit_code = 0 for test_index in range(num_tests): parent_values = list() current_values = list() for iteration_index in range(len(parent_iterations)): parent_values.extend( json.loads(parent_iterations[iteration_index][test_index]) ) current_values.extend( json.loads(current_iterations[iteration_index][test_index]) ) print(f"Time-ordered MRR values for parent build: {parent_values}") print(f"Time-ordered MRR values for current build: {current_values}") parent_values = hack(parent_values) current_values = hack(current_values) max_value = max([1.0] + parent_values + current_values) parent_stats = jumpavg.AvgStdevStats.for_runs(parent_values) current_stats = jumpavg.AvgStdevStats.for_runs(current_values) parent_group_list = jumpavg.BitCountingGroupList( max_value=max_value).append_group_of_runs([parent_stats]) combined_group_list = parent_group_list.copy().extend_runs_to_last_group( [current_stats]) separated_group_list = parent_group_list.append_group_of_runs( [current_stats]) print(f"Value-ordered MRR values for parent build: {parent_values}") print(f"Value-ordered MRR values for current build: {current_values}") avg_diff = (current_stats.avg - parent_stats.avg) / parent_stats.avg print(f"Difference of averages relative to parent: {100 * avg_diff}%") print(f"Jumpavg representation of parent group: {parent_stats}") print(f"Jumpavg representation of current group: {current_stats}") print( f"Jumpavg representation of both as one group:" f" {combined_group_list[0].stats}" ) bits_diff = separated_group_list.bits - combined_group_list.bits compared = u"longer" if bits_diff >= 0 else u"shorter" print( f"Separate groups are {compared} than single group" f" by {abs(bits_diff)} bits" ) # TODO: Version of classify that takes max_value and list of stats? # That matters if only stats (not list of floats) are given. classified_list = jumpavg.classify([parent_values, current_values]) if len(classified_list) < 2: print(f"Test test_index {test_index}: normal (no anomaly)") continue anomaly = classified_list[1].comment if anomaly == u"regression": print(f"Test test_index {test_index}: anomaly regression") exit_code = 3 # 1 or 2 can be caused by other errors continue print(f"Test test_index {test_index}: anomaly {anomaly}") print(f"Exit code: {exit_code}") sys.exit(exit_code)