1 # Copyright (c) 2024 Cisco and/or its affiliates.
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at:
6 # http://www.apache.org/licenses/LICENSE-2.0
8 # Unless required by applicable law or agreed to in writing, software
9 # distributed under the License is distributed on an "AS IS" BASIS,
10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 # See the License for the specific language governing permissions and
12 # limitations under the License.
14 """Script for determining whether per-patch perf test votes -1.
16 This script expects a particular tree created on a filesystem by
17 per_patch_perf.sh bootstrap script, including test results
18 exported as json files according to a current model schema.
19 This script extracts the results (according to result type)
20 and joins them into one list of floats for parent and one for current.
22 This script then uses jumpavg library to determine whether there was
23 a regression, progression or no change for each testcase.
25 If the set of test names does not match, or there was a regression,
26 this script votes -1 (by exiting with code 1), otherwise it votes +1 (exit 0).
31 from resources.libraries.python import jumpavg
32 from resources.libraries.python.model.parse import parse
36 """Execute the main logic, return a number to return as the return code.
38 Call parse to get parent and current data.
39 Use higher fake value for parent, so changes that keep a test failing
40 are marked as regressions.
42 If there are multiple iterations, the value lists are joined.
43 For each test, call jumpavg.classify to detect possible regression.
45 If there is at least one regression, return 3.
47 :returns: Return code, 0 or 3 based on the comparison result.
52 current_aggregate = {}
58 parent_results = parse(f"csit_parent/{iteration}", fake_value=2.0)
59 parent_names = list(parent_results)
60 if test_names is None:
61 test_names = parent_names
65 assert parent_names == test_names, f"{parent_names} != {test_names}"
66 current_results = parse(f"csit_current/{iteration}", fake_value=1.0)
67 current_names = list(current_results)
69 current_names == parent_names
70 ), f"{current_names} != {parent_names}"
71 for name in test_names:
72 if name not in parent_aggregate:
73 parent_aggregate[name] = []
74 if name not in current_aggregate:
75 current_aggregate[name] = []
76 parent_aggregate[name].extend(parent_results[name])
77 current_aggregate[name].extend(current_results[name])
79 for name in test_names:
80 parent_values = parent_aggregate[name]
81 current_values = current_aggregate[name]
82 print(f"Time-ordered MRR values for parent build: {parent_values}")
83 print(f"Time-ordered MRR values for current build: {current_values}")
84 parent_values = sorted(parent_values)
85 current_values = sorted(current_values)
86 max_value = max([1.0] + parent_values + current_values)
87 parent_stats = jumpavg.AvgStdevStats.for_runs(parent_values)
88 current_stats = jumpavg.AvgStdevStats.for_runs(current_values)
89 parent_group_list = jumpavg.BitCountingGroupList(
91 ).append_group_of_runs([parent_stats])
92 combined_group_list = (
93 parent_group_list.copy().extend_runs_to_last_group([current_stats])
95 separated_group_list = parent_group_list.append_group_of_runs(
98 print(f"Value-ordered MRR values for parent build: {parent_values}")
99 print(f"Value-ordered MRR values for current build: {current_values}")
100 avg_diff = (current_stats.avg - parent_stats.avg) / parent_stats.avg
101 print(f"Difference of averages relative to parent: {100 * avg_diff}%")
102 print(f"Jumpavg representation of parent group: {parent_stats}")
103 print(f"Jumpavg representation of current group: {current_stats}")
105 f"Jumpavg representation of both as one group:"
106 f" {combined_group_list[0].stats}"
108 bits_diff = separated_group_list.bits - combined_group_list.bits
109 compared = "longer" if bits_diff >= 0 else "shorter"
111 f"Separate groups are {compared} than single group"
112 f" by {abs(bits_diff)} bits"
114 # TODO: Version of classify that takes max_value and list of stats?
115 # That matters if only stats (not list of floats) are given.
116 classified_list = jumpavg.classify([parent_values, current_values])
117 anomaly_name = "normal (no anomaly)"
118 if len(classified_list) > 1:
119 anomaly = classified_list[1].comment
120 anomaly_name = "anomaly progression"
121 if anomaly == "regression":
122 anomaly_name = "anomaly regression"
123 exit_code = 3 # 1 or 2 can be caused by other errors
124 print(f"Test name {name}: {anomaly_name}")
125 print(f"Exit code: {exit_code}")
129 if __name__ == "__main__":