blob: 934a91fc1e0b03742fd5ddbde46b3111fb7dfa9d [file] [log] [blame]
# Shell class for a test, inherited by all individual tests
# Methods:
# __init__ initialise
# initialize run once for each job
# setup run once for each new version of the test installed
# run run the test (wrapped by job.run_test())
# Data:
# job backreference to the job this test instance is part of
# outputdir eg. results/<job>/<testname.tag>
# resultsdir eg. results/<job>/<testname.tag>/results
# profdir eg. results/<job>/<testname.tag>/profiling
# debugdir eg. results/<job>/<testname.tag>/debug
# bindir eg. tests/<test>
# src eg. tests/<test>/src
# tmpdir eg. tmp/<tempname>_<testname.tag>
#pylint: disable=C0111
import fcntl, json, os, re, sys, shutil, stat, tempfile, time, traceback
import logging
from autotest_lib.client.bin import utils
from autotest_lib.client.common_lib import error
class base_test(object):
preserve_srcdir = False
network_destabilizing = False
def __init__(self, job, bindir, outputdir):
self.job = job
self.pkgmgr = job.pkgmgr
self.autodir = job.autodir
self.outputdir = outputdir
self.tagged_testname = os.path.basename(self.outputdir)
self.resultsdir = os.path.join(self.outputdir, 'results')
self.profdir = os.path.join(self.outputdir, 'profiling')
self.debugdir = os.path.join(self.outputdir, 'debug')
# TODO(ericli): figure out how autotest crash handler work with cros
# Once this is re-enabled import getpass.
# crash handler, we should restore it in near term.
# if getpass.getuser() == 'root':
# self.configure_crash_handler()
# else:
self.crash_handling_enabled = False
self.bindir = bindir
self.srcdir = os.path.join(self.bindir, 'src')
self.tmpdir = tempfile.mkdtemp("_" + self.tagged_testname,
self._keyvals = []
self._new_keyval = False
self.failed_constraints = []
self.iteration = 0
self.before_iteration_hooks = []
self.after_iteration_hooks = []
# Flag to indicate if the test has succeeded or failed.
self.success = False
def configure_crash_handler(self):
def crash_handler_report(self):
def assert_(self, expr, msg='Assertion failed.'):
if not expr:
raise error.TestError(msg)
def write_test_keyval(self, attr_dict):
utils.write_keyval(self.outputdir, attr_dict,
def _append_type_to_keys(dictionary, typename):
new_dict = {}
for key, value in dictionary.iteritems():
new_key = "%s{%s}" % (key, typename)
new_dict[new_key] = value
return new_dict
def output_perf_value(self, description, value, units=None,
higher_is_better=None, graph=None, replacement='_'):
Records a measured performance value in an output file.
The output file will subsequently be parsed by the TKO parser to have
the information inserted into the results database.
@param description: A string describing the measured perf value. Must
be maximum length 256, and may only contain letters, numbers,
periods, dashes, and underscores. For example:
"page_load_time", "scrolling-frame-rate".
@param value: A number representing the measured perf value, or a list
of measured values if a test takes multiple measurements.
Measured perf values can be either ints or floats.
@param units: A string describing the units associated with the
measured perf value. Must be maximum length 32, and may only
contain letters, numbers, periods, dashes, and underscores.
For example: "msec", "fps", "score", "runs_per_second".
@param higher_is_better: A boolean indicating whether or not a "higher"
measured perf value is considered to be better. If False, it is
assumed that a "lower" measured value is considered to be
better. This impacts dashboard plotting and email notification.
Pure autotests are expected to specify either True or False!
This value can be set to "None" to indicate that the perf
dashboard should apply the rules encoded via Chromium
unit-info.json. This is only used for tracking Chromium based
tests (in particular telemetry).
@param graph: A string indicating the name of the graph on which
the perf value will be subsequently displayed on the chrome perf
dashboard. This allows multiple metrics be grouped together on
the same graphs. Defaults to None, indicating that the perf
value should be displayed individually on a separate graph.
@param replacement: string to replace illegal characters in
|description| and |units| with.
if len(description) > 256:
raise ValueError('The description must be at most 256 characters.')
if units and len(units) > 32:
raise ValueError('The units must be at most 32 characters.')
# If |replacement| is legal replace illegal characters with it.
string_regex = re.compile(r'[^-\.\w]')
if replacement is None or, replacement):
raise ValueError('Invalid replacement string to mask illegal '
'characters. May only contain letters, numbers, '
'periods, dashes, and underscores. '
'replacement: %s' % replacement)
description = re.sub(string_regex, replacement, description)
units = re.sub(string_regex, replacement, units) if units else None
charts = {}
output_file = os.path.join(self.resultsdir, 'results-chart.json')
if os.path.isfile(output_file):
with open(output_file, 'r') as fp:
contents =
if contents:
charts = json.loads(contents)
if graph:
first_level = graph
second_level = description
first_level = description
second_level = 'summary'
direction = 'up' if higher_is_better else 'down'
# All input should be a number - but at times there are strings
# representing numbers logged, attempt to convert them to numbers.
# If a non number string is logged an exception will be thrown.
if isinstance(value, list):
value = map(float, value)
value = float(value)
result_type = 'scalar'
value_key = 'value'
result_value = value
# The chart json spec go/telemetry-json differenciates between a single
# value vs a list of values. Lists of values get extra processing in
# the chromeperf dashboard ( mean, standard deviation etc)
# Tests can log one or more values for the same metric, to adhere stricly
# to the specification the first value logged is a scalar but if another
# value is logged the results become a list of scalar.
# TODO Figure out if there would be any difference of always using list
# of scalar even if there is just one item in the list.
if isinstance(value, list):
result_type = 'list_of_scalar_values'
value_key = 'values'
if first_level in charts and second_level in charts[first_level]:
if 'values' in charts[first_level][second_level]:
result_value = charts[first_level][second_level]['values']
elif 'value' in charts[first_level][second_level]:
result_value = [charts[first_level][second_level]['value']]
result_value = value
elif first_level in charts and second_level in charts[first_level]:
result_type = 'list_of_scalar_values'
value_key = 'values'
if 'values' in charts[first_level][second_level]:
result_value = charts[first_level][second_level]['values']
result_value = [charts[first_level][second_level]['value'], value]
test_data = {
second_level: {
'type': result_type,
'units': units,
value_key: result_value,
'improvement_direction': direction
if first_level in charts:
charts.update({first_level: test_data})
with open(output_file, 'w') as fp:
fp.write(json.dumps(charts, indent=2))
def write_perf_keyval(self, perf_dict):
self.write_iteration_keyval({}, perf_dict,
def write_attr_keyval(self, attr_dict):
self.write_iteration_keyval(attr_dict, {},
def write_iteration_keyval(self, attr_dict, perf_dict, tap_report=None):
# append the dictionaries before they have the {perf} and {attr} added
self._keyvals.append({'attr':attr_dict, 'perf':perf_dict})
self._new_keyval = True
if attr_dict:
attr_dict = self._append_type_to_keys(attr_dict, "attr")
utils.write_keyval(self.resultsdir, attr_dict, type_tag="attr",
if perf_dict:
perf_dict = self._append_type_to_keys(perf_dict, "perf")
utils.write_keyval(self.resultsdir, perf_dict, type_tag="perf",
keyval_path = os.path.join(self.resultsdir, "keyval")
print >> open(keyval_path, "a"), ""
def analyze_perf_constraints(self, constraints):
if not self._new_keyval:
# create a dict from the keyvals suitable as an environment for eval
keyval_env = self._keyvals[-1]['perf'].copy()
keyval_env['__builtins__'] = None
self._new_keyval = False
failures = []
# evaluate each constraint using the current keyvals
for constraint in constraints:'___________________ constraint = %s', constraint)'___________________ keyvals = %s', keyval_env)
if not eval(constraint, keyval_env):
failures.append('%s: constraint was not met' % constraint)
failures.append('could not evaluate constraint: %s'
% constraint)
# keep track of the errors for each iteration
def process_failed_constraints(self):
msg = ''
for i, failures in enumerate(self.failed_constraints):
if failures:
msg += 'iteration %d:%s ' % (i, ','.join(failures))
if msg:
raise error.TestFail(msg)
def register_before_iteration_hook(self, iteration_hook):
This is how we expect test writers to register a before_iteration_hook.
This adds the method to the list of hooks which are executed
before each iteration.
@param iteration_hook: Method to run before each iteration. A valid
hook accepts a single argument which is the
test object.
def register_after_iteration_hook(self, iteration_hook):
This is how we expect test writers to register an after_iteration_hook.
This adds the method to the list of hooks which are executed
after each iteration. Hooks are executed starting with the most-
recently registered, in stack fashion.
@param iteration_hook: Method to run after each iteration. A valid
hook accepts a single argument which is the
test object.
def initialize(self):
def setup(self):
def warmup(self, *args, **dargs):
def drop_caches_between_iterations(self):
if self.job.drop_caches_between_iterations:
def _call_run_once_with_retry(self, constraints, profile_only,
postprocess_profiled_run, args, dargs):
"""Thin wrapper around _call_run_once that retries unsuccessful tests.
If the job object's attribute test_retry is > 0 retry any tests that
ran unsuccessfully X times.
*Note this does not competely re-initialize the test, it only
re-executes code once all the initial job set up (packages,
sysinfo, etc) is complete.
if self.job.test_retry != 0:'Test will be retried a maximum of %d times',
max_runs = self.job.test_retry
for retry_run in xrange(0, max_runs+1):
self._call_run_once(constraints, profile_only,
postprocess_profiled_run, args, dargs)
except error.TestFailRetry as err:
if retry_run == max_runs:
self.job.record('INFO', None, None, 'Run %s failed with %s' % (
retry_run, err))
if retry_run > 0:
self.write_test_keyval({'test_retries_before_success': retry_run})
def _call_run_once(self, constraints, profile_only,
postprocess_profiled_run, args, dargs):
# execute iteration hooks
for hook in self.before_iteration_hooks:
if profile_only:
if not self.job.profilers.present():
self.job.record('WARN', None, None,
'No profilers have been added but '
'profile_only is set - nothing '
'will be run')
*args, **dargs)
self.run_once(*args, **dargs)
# Catch and re-raise to let after_iteration_hooks see the exception.
for hook in reversed(self.after_iteration_hooks):
def execute(self, iterations=None, test_length=None, profile_only=None,
_get_time=time.time, postprocess_profiled_run=None,
constraints=(), *args, **dargs):
This is the basic execute method for the tests inherited from base_test.
If you want to implement a benchmark test, it's better to implement
the run_once function, to cope with the profiling infrastructure. For
other tests, you can just override the default implementation.
@param test_length: The minimum test length in seconds. We'll run the
run_once function for a number of times large enough to cover the
minimum test length.
@param iterations: A number of iterations that we'll run the run_once
function. This parameter is incompatible with test_length and will
be silently ignored if you specify both.
@param profile_only: If true run X iterations with profilers enabled.
If false run X iterations and one with profiling if profiles are
enabled. If None, default to the value of job.default_profile_only.
@param _get_time: [time.time] Used for unit test time injection.
@param postprocess_profiled_run: Run the postprocessing for the
profiled run.
# For our special class of tests, the benchmarks, we don't want
# profilers to run during the test iterations. Let's reserve only
# the last iteration for profiling, if needed. So let's stop
# all profilers if they are present and active.
profilers = self.job.profilers
if profile_only is None:
profile_only = self.job.default_profile_only
# If the user called this test in an odd way (specified both iterations
# and test_length), let's warn them.
if iterations and test_length:
logging.debug('Iterations parameter ignored (timed execution)')
if test_length:
test_start = _get_time()
time_elapsed = 0
timed_counter = 0
logging.debug('Test started. Specified %d s as the minimum test '
'length', test_length)
while time_elapsed < test_length:
timed_counter = timed_counter + 1
if time_elapsed == 0:
logging.debug('Executing iteration %d', timed_counter)
elif time_elapsed > 0:
logging.debug('Executing iteration %d, time_elapsed %d s',
timed_counter, time_elapsed)
self._call_run_once_with_retry(constraints, profile_only,
postprocess_profiled_run, args,
test_iteration_finish = _get_time()
time_elapsed = test_iteration_finish - test_start
logging.debug('Test finished after %d iterations, '
'time elapsed: %d s', timed_counter, time_elapsed)
if iterations is None:
iterations = 1
if iterations > 1:
logging.debug('Test started. Specified %d iterations',
for self.iteration in xrange(1, iterations + 1):
if iterations > 1:
logging.debug('Executing iteration %d of %d',
self.iteration, iterations)
self._call_run_once_with_retry(constraints, profile_only,
postprocess_profiled_run, args,
if not profile_only:
self.iteration += 1
self.run_once_profiling(postprocess_profiled_run, *args, **dargs)
# Do any postprocessing, normally extracting performance keyvals, etc
def run_once_profiling(self, postprocess_profiled_run, *args, **dargs):
profilers = self.job.profilers
# Do a profiling run if necessary
if profilers.present():
logging.debug('Profilers present. Profiling run started')
self.run_once(*args, **dargs)
# Priority to the run_once() argument over the attribute.
postprocess_attribute = getattr(self,
if (postprocess_profiled_run or
(postprocess_profiled_run is None and
def postprocess(self):
def postprocess_iteration(self):
def cleanup(self):
def before_run_once(self):
Override in tests that need it, will be called before any run_once()
call including the profiling run (when it's called before starting
the profilers).
def after_run_once(self):
Called after every run_once (including from a profiled run when it's
called after stopping the profilers).
def _make_writable_to_others(directory):
mode = os.stat(directory).st_mode
mode = mode | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH
os.chmod(directory, mode)
def _exec(self, args, dargs):
if self.network_destabilizing:
# write out the test attributes into a keyval
dargs = dargs.copy()
run_cleanup = dargs.pop('run_cleanup', self.job.run_test_cleanup)
keyvals = dargs.pop('test_attributes', {}).copy()
keyvals['version'] = self.version
for i, arg in enumerate(args):
keyvals['param-%d' % i] = repr(arg)
for name, arg in dargs.iteritems():
keyvals['param-%s' % name] = repr(arg)
_validate_args(args, dargs, self.initialize, self.setup,
self.execute, self.cleanup)
# Make resultsdir and tmpdir accessible to everyone. We may
# output data to these directories as others, e.g., chronos.
# Initialize:
_cherry_pick_call(self.initialize, *args, **dargs)
lockfile = open(os.path.join(self.job.tmpdir, '.testlock'), 'w')
fcntl.flock(lockfile, fcntl.LOCK_EX)
# Setup: (compile and install the test, if needed)
p_args, p_dargs = _cherry_pick_args(self.setup, args, dargs)
utils.update_version(self.srcdir, self.preserve_srcdir,
self.version, self.setup,
*p_args, **p_dargs)
fcntl.flock(lockfile, fcntl.LOCK_UN)
# Execute:
# call self.warmup cherry picking the arguments it accepts and
# translate exceptions if needed
_call_test_function(_cherry_pick_call, self.warmup,
*args, **dargs)
if hasattr(self, 'run_once'):
p_args, p_dargs = _cherry_pick_args(self.run_once,
args, dargs)
# pull in any non-* and non-** args from self.execute
for param in _get_nonstar_args(self.execute):
if param in dargs:
p_dargs[param] = dargs[param]
p_args, p_dargs = _cherry_pick_args(self.execute,
args, dargs)
_call_test_function(self.execute, *p_args, **p_dargs)
except Exception:
# Save the exception while we run our cleanup() before
# reraising it, but log it to so actual time of error is known.
exc_info = sys.exc_info()
logging.warning('Autotest caught exception when running test:',
if run_cleanup:
_cherry_pick_call(self.cleanup, *args, **dargs)
except Exception:
logging.error('Ignoring exception during cleanup() '
logging.error('Now raising the earlier %s error',
raise exc_info[0], exc_info[1], exc_info[2]
# Be nice and prevent a circular reference.
del exc_info
if run_cleanup:
_cherry_pick_call(self.cleanup, *args, **dargs)
except error.AutotestError:
if self.network_destabilizing:
# Pass already-categorized errors on up.
except Exception, e:
if self.network_destabilizing:
# Anything else is an ERROR in our own code, not execute().
raise error.UnhandledTestError(e)
if self.network_destabilizing:
def runsubtest(self, url, *args, **dargs):
Execute another autotest test from inside the current test's scope.
@param test: Parent test.
@param url: Url of new test.
@param tag: Tag added to test name.
@param args: Args for subtest.
@param dargs: Dictionary with args for subtest.
@iterations: Number of subtest iterations.
@profile_only: If true execute one profiled run.
dargs["profile_only"] = dargs.get("profile_only", False)
test_basepath = self.outputdir[len(self.job.resultdir + "/"):]
return self.job.run_test(url, master_testpath=test_basepath,
*args, **dargs)
def _get_nonstar_args(func):
"""Extract all the (normal) function parameter names.
Given a function, returns a tuple of parameter names, specifically
excluding the * and ** parameters, if the function accepts them.
@param func: A callable that we want to chose arguments for.
@return: A tuple of parameters accepted by the function.
return func.func_code.co_varnames[:func.func_code.co_argcount]
def _cherry_pick_args(func, args, dargs):
"""Sanitize positional and keyword arguments before calling a function.
Given a callable (func), an argument tuple and a dictionary of keyword
arguments, pick only those arguments which the function is prepared to
accept and return a new argument tuple and keyword argument dictionary.
func: A callable that we want to choose arguments for.
args: A tuple of positional arguments to consider passing to func.
dargs: A dictionary of keyword arguments to consider passing to func.
A tuple of: (args tuple, keyword arguments dictionary)
# Cherry pick args:
if func.func_code.co_flags & 0x04:
# func accepts *args, so return the entire args.
p_args = args
p_args = ()
# Cherry pick dargs:
if func.func_code.co_flags & 0x08:
# func accepts **dargs, so return the entire dargs.
p_dargs = dargs
# Only return the keyword arguments that func accepts.
p_dargs = {}
for param in _get_nonstar_args(func):
if param in dargs:
p_dargs[param] = dargs[param]
return p_args, p_dargs
def _cherry_pick_call(func, *args, **dargs):
"""Cherry picks arguments from args/dargs based on what "func" accepts
and calls the function with the picked arguments."""
p_args, p_dargs = _cherry_pick_args(func, args, dargs)
return func(*p_args, **p_dargs)
def _validate_args(args, dargs, *funcs):
"""Verify that arguments are appropriate for at least one callable.
Given a list of callables as additional parameters, verify that
the proposed keyword arguments in dargs will each be accepted by at least
one of the callables.
NOTE: args is currently not supported and must be empty.
args: A tuple of proposed positional arguments.
dargs: A dictionary of proposed keyword arguments.
*funcs: Callables to be searched for acceptance of args and dargs.
error.AutotestError: if an arg won't be accepted by any of *funcs.
all_co_flags = 0
all_varnames = ()
for func in funcs:
all_co_flags |= func.func_code.co_flags
all_varnames += func.func_code.co_varnames[:func.func_code.co_argcount]
# Check if given args belongs to at least one of the methods below.
if len(args) > 0:
# Current implementation doesn't allow the use of args.
raise error.TestError('Unnamed arguments not accepted. Please '
'call job.run_test with named args only')
# Check if given dargs belongs to at least one of the methods below.
if len(dargs) > 0:
if not all_co_flags & 0x08:
# no func accepts *dargs, so:
for param in dargs:
if not param in all_varnames:
raise error.AutotestError('Unknown parameter: %s' % param)
def _installtest(job, url):
(group, name) = job.pkgmgr.get_package_name(url, 'test')
# Bail if the test is already installed
group_dir = os.path.join(job.testdir, "download", group)
if os.path.exists(os.path.join(group_dir, name)):
return (group, name)
# If the group directory is missing create it and add
# an empty so that sub-directories are
# considered for import.
if not os.path.exists(group_dir):
f = file(os.path.join(group_dir, ''), 'w+')
logging.debug("%s: installing test url=%s", name, url)
tarball = os.path.basename(url)
tarball_path = os.path.join(group_dir, tarball)
test_dir = os.path.join(group_dir, name)
job.pkgmgr.fetch_pkg(tarball, tarball_path,
repo_url = os.path.dirname(url))
# Create the directory for the test
if not os.path.exists(test_dir):
os.mkdir(os.path.join(group_dir, name))
job.pkgmgr.untar_pkg(tarball_path, test_dir)
# For this 'sub-object' to be importable via the name
# '' we need to provide an,
# so link the main entry point to this.
os.symlink(name + '.py', os.path.join(group_dir, name,
# The test is now installed.
return (group, name)
def _call_test_function(func, *args, **dargs):
"""Calls a test function and translates exceptions so that errors
inside test code are considered test failures."""
return func(*args, **dargs)
except error.AutotestError:
except Exception, e:
# Other exceptions must be treated as a FAIL when
# raised during the test functions
raise error.UnhandledTestFail(e)
def runtest(job, url, tag, args, dargs,
local_namespace={}, global_namespace={},
before_test_hook=None, after_test_hook=None,
before_iteration_hook=None, after_iteration_hook=None):
local_namespace = local_namespace.copy()
global_namespace = global_namespace.copy()
# if this is not a plain test name then download and install the
# specified test
if url.endswith('.tar.bz2'):
(testgroup, testname) = _installtest(job, url)
bindir = os.path.join(job.testdir, 'download', testgroup, testname)
importdir = os.path.join(job.testdir, 'download')
modulename = '%s.%s' % (re.sub('/', '.', testgroup), testname)
classname = '%s.%s' % (modulename, testname)
path = testname
# If the test is local, it may be under either testdir or site_testdir.
# Tests in site_testdir override tests defined in testdir
testname = path = url
testgroup = ''
path = re.sub(':', '/', testname)
modulename = os.path.basename(path)
classname = '%s.%s' % (modulename, modulename)
# Try installing the test package
# The job object may be either a server side job or a client side job.
# 'install_pkg' method will be present only if it's a client side job.
if hasattr(job, 'install_pkg'):
bindir = os.path.join(job.testdir, testname)
job.install_pkg(testname, 'test', bindir)
except error.PackageInstallError:
# continue as a fall back mechanism and see if the test code
# already exists on the machine
bindir = None
for dir in [job.testdir, getattr(job, 'site_testdir', None)]:
if dir is not None and os.path.exists(os.path.join(dir, path)):
importdir = bindir = os.path.join(dir, path)
if not bindir:
raise error.TestError(testname + ': test does not exist')
subdir = os.path.join(dargs.pop('master_testpath', ""), testname)
outputdir = os.path.join(job.resultdir, subdir)
if tag:
outputdir += '.' + tag
local_namespace['job'] = job
local_namespace['bindir'] = bindir
local_namespace['outputdir'] = outputdir
sys.path.insert(0, importdir)
exec ('import %s' % modulename, local_namespace, global_namespace)
exec ("mytest = %s(job, bindir, outputdir)" % classname,
local_namespace, global_namespace)
pwd = os.getcwd()
mytest = global_namespace['mytest']
mytest.success = False
if before_test_hook:
# we use the register iteration hooks methods to register the passed
# in hooks
if before_iteration_hook:
if after_iteration_hook:
mytest._exec(args, dargs)
mytest.success = True
if after_test_hook:
shutil.rmtree(mytest.tmpdir, ignore_errors=True)