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69 lines
2.7 KiB
Python
69 lines
2.7 KiB
Python
# Copyright 2013-2015 ARM Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import re
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from wlauto import AndroidUiAutoBenchmark
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class Caffeinemark(AndroidUiAutoBenchmark):
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name = 'caffeinemark'
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description = """
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CaffeineMark is a series of tests that measure the speed of Java
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programs running in various hardware and software configurations.
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http://www.benchmarkhq.ru/cm30/info.html
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From the website:
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CaffeineMark scores roughly correlate with the number of Java instructions
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executed per second, and do not depend significantly on the the amount of
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memory in the system or on the speed of a computers disk drives or internet
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connection.
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The following is a brief description of what each test does:
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- Sieve: The classic sieve of eratosthenes finds prime numbers.
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- Loop: The loop test uses sorting and sequence generation as to measure
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compiler optimization of loops.
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- Logic: Tests the speed with which the virtual machine executes
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decision-making instructions.
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- Method: The Method test executes recursive function calls to see how
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well the VM handles method calls.
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- Float: Simulates a 3D rotation of objects around a point.
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- Graphics: Draws random rectangles and lines.
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- Image: Draws a sequence of three graphics repeatedly.
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- Dialog: Writes a set of values into labels and editboxes on a form.
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The overall CaffeineMark score is the geometric mean of the individual
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scores, i.e., it is the 9th root of the product of all the scores.
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"""
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package = "com.flexycore.caffeinemark"
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activity = ".Application"
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summary_metrics = ['OverallScore']
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regex = re.compile(r'CAFFEINEMARK RESULT: (?P<type>\w+) (?P<value>\S+)')
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def update_result(self, context):
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super(Caffeinemark, self).update_result(context)
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with open(self.logcat_log) as fh:
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for line in fh:
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match = self.regex.search(line)
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if match:
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metric = match.group('type')
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value = float(match.group('value'))
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context.result.add_metric(metric, value)
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