def setParameter(self,parametername,parametervalue):
if not parametername in ['randomseed']:
print __doc__
raise Exception("Invalid parameter '%s' for gameguidance." % parametername)
GuidanceBase.setParameter(self,parametername,parametervalue)
if parametername=='randomseed':
self._rndchoose=random.Random(parametervalue).choice
def __init__(self):
GuidanceBase.__init__(self)
self.setParameter('lookahead',15)
self.setParameter('randomseed',time.time())
self.setParameter('rerouteafter',1)
self._lastroute=[]
self._steps_to_reroute=0
def setParameter(self,parametername,parametervalue):
if not parametername in ['maxdepth','mindepth','randomseed']:
print __doc__
raise Exception("Invalid parameter '%s' for gameguidance." % parametername)
GuidanceBase.setParameter(self,parametername,parametervalue)
if parametername=='randomseed':
self._rndchoose=random.Random(parametervalue).choice
def __init__(self):
GuidanceBase.__init__(self)
self._NUM_TABU_ACTIONS = None
self._NUM_TABU_STATES = None
self._NUM_TABU_TRANSITIONS = None
self._tabulist_action = None
self._tabulist_state = None
self._tabulist_transition = None
def __init__(self):
GuidanceBase.__init__(self)
self._guidances = []
self._guidanceOpts = []
self._covs = []
self._currIndex = -1
self._model = None
self._filename = None
def __init__(self):
GuidanceBase.__init__(self)
self._guidances = []
self._guidanceOpts = []
self._covs = []
self._currIndex = -1
self._model = None
self._filename = None
def __init__(self):
GuidanceBase.__init__(self)
self._NUM_TABU_ACTIONS = None
self._NUM_TABU_STATES = None
self._NUM_TABU_TRANSITIONS = None
self._tabulist_action = None
self._tabulist_state = None
self._tabulist_transition = None
def setParameter(self,parametername,parametervalue):
if not parametername in ['lookahead','randomseed','rerouteafter']:
print __doc__
raise Exception("Invalid parameter '%s' for gameguidance." % parametername)
GuidanceBase.setParameter(self,parametername,parametervalue)
if parametername=='randomseed':
self._rndchoose=random.Random(parametervalue).choice
elif parametername=='rerouteafter':
self._steps_to_reroute=self.getParameter(parametername)
def __init__(self):
GuidanceBase.__init__(self)
# default parameters:
self.setParameter("transitionweight", 0)
self.setParameter("searchdepth", 10)
self.setParameter("searchorder", "shortestfirst")
self.setParameter("maxtransitions", 10000)
self.setParameter("greedy", 0)
self.setParameter("searchconstraint", "noloops")
def __init__(self):
GuidanceBase.__init__(self)
# default parameters:
self.setParameter("transitionweight", 0)
self.setParameter("searchdepth", 10)
self.setParameter("searchorder", "shortestfirst")
self.setParameter("maxtransitions", 10000)
self.setParameter("greedy", 0)
self.setParameter("searchconstraint", "noloops")
def setParameter(self, paramname, paramvalue):
accepted = ("numtabuactions", "numtabustates", "numtabutransitions")
if paramname == "numtabuactions":
self._NUM_TABU_ACTIONS = self._parseSize(paramname, paramvalue)
elif paramname == "numtabustates":
self._NUM_TABU_STATES = self._parseSize(paramname, paramvalue)
elif paramname == "numtabutransitions":
self._NUM_TABU_TRANSITIONS = self._parseSize(paramname, paramvalue)
else:
print __doc__
raise Exception("Invalid parameter '%s' for tabuguidance. Accepted parameters: %s" % paramname, accepted)
GuidanceBase.setParameter(self, paramname, paramvalue)
def markExecuted(self,transition_object):
locked=0 # keeps if this function has acquired search_front_lock
# Update advances to the 'official' coverage object
GuidanceBase.markExecuted(self,transition_object)
# Then cleanup search front: remove every entry from the
# search front if it starts with some other than the executed
# transition_object, and shorten entries starting with the
# transition.
# Shortening cannot be done if paths are too short, therefore,
# if the thread is running, let it do its job
while 1:
self.search_front_lock.acquire()
locked=1
if len(self.search_front[0][1])<2:
# There is just one action in the search front, it can
# be safely removed only if the thread is no more
# running, that is, time_to_quit signal has been
# given.
if self.time_to_quit: break
else:
self.search_front_lock.release()
locked=0
time.sleep(1) # give some time to the thread
continue
# NOTE: This may cause a livelock if there are
# deadlocks in the model: search front is not getting
# any deeper. There should not be deadlocks!
else:
break
# If the thread is quitting, there is no reason to
# cleanup the search front
#if self.time_to_quit:
# if locked: self.search_front_lock.release()
# return
# This function must own the lock now, search_front can be
# edited.
new_search_front=[]
for points,path,reqs in self.search_front:
if path[0]==transition_object:
self._front_shortened=1 # message to the thread
new_search_front.append([points,path[1:],reqs])
self.search_front=new_search_front
self.log("Search front reduced to length %s and depth %s" %
(len(self.search_front),len(self.search_front[0][1])))
self.search_front_lock.release()
def markExecuted(self, transition_object):
locked = 0 # keeps if this function has acquired search_front_lock
# Update advances to the 'official' coverage object
GuidanceBase.markExecuted(self, transition_object)
# Then cleanup search front: remove every entry from the
# search front if it starts with some other than the executed
# transition_object, and shorten entries starting with the
# transition.
# Shortening cannot be done if paths are too short, therefore,
# if the thread is running, let it do its job
while 1:
self.search_front_lock.acquire()
locked = 1
if len(self.search_front[0][1]) < 2:
# There is just one action in the search front, it can
# be safely removed only if the thread is no more
# running, that is, time_to_quit signal has been
# given.
if self.time_to_quit: break
else:
self.search_front_lock.release()
locked = 0
time.sleep(1) # give some time to the thread
continue
# NOTE: This may cause a livelock if there are
# deadlocks in the model: search front is not getting
# any deeper. There should not be deadlocks!
else:
break
# If the thread is quitting, there is no reason to
# cleanup the search front
#if self.time_to_quit:
# if locked: self.search_front_lock.release()
# return
# This function must own the lock now, search_front can be
# edited.
new_search_front = []
for points, path, reqs in self.search_front:
if path[0] == transition_object:
self._front_shortened = 1 # message to the thread
new_search_front.append([points, path[1:], reqs])
self.search_front = new_search_front
self.log("Search front reduced to length %s and depth %s" %
(len(self.search_front), len(self.search_front[0][1])))
self.search_front_lock.release()
def setParameter(self, paramname, paramvalue):
accepted = ("numtabuactions", "numtabustates", "numtabutransitions")
if paramname == 'numtabuactions':
self._NUM_TABU_ACTIONS = self._parseSize(paramname, paramvalue)
elif paramname == 'numtabustates':
self._NUM_TABU_STATES = self._parseSize(paramname, paramvalue)
elif paramname == 'numtabutransitions':
self._NUM_TABU_TRANSITIONS = self._parseSize(paramname, paramvalue)
else:
print __doc__
raise Exception(
"Invalid parameter '%s' for tabuguidance. Accepted parameters: %s"
% paramname, accepted)
GuidanceBase.setParameter(self, paramname, paramvalue)
def markExecuted(self, transition):
# special case: add the very first (source) state to the tabu-list
statelist = self._tabulist_state
if statelist and len(statelist) == 0:
statelist.add(transition.getSourceState())
# add actions/states/transitions to tabulists if given tabulist exists
if self._tabulist_action is not None:
self._tabulist_action.add(str(transition.getAction()))
if self._tabulist_state is not None:
self._tabulist_state.add(str(transition.getDestState()))
if self._tabulist_transition is not None:
self._tabulist_transition.add(str(transition))
GuidanceBase.markExecuted(self, transition)
def __init__(self):
GuidanceBase.__init__(self)
self.setParameter('maxdepth',100)
self.setParameter('mindepth',1)
self.setParameter('randomseed',time.time())
self._lastroute=[]
self._steps_to_reroute=0
# search front is a list of triplets: (score, path, coverage)
# where score is a pair: (coverage_percentage, steps since
# last change coverage_percentage). Thus, the bigger the
# number of steps, the faster the final coverage_percentage is
# achieved.
self.search_front=[]
self._thread_id=None
self._front_shortened=0 # msg from markExecuted to thread
def markExecuted(self, transition):
# special case: add the very first (source) state to the tabu-list
statelist = self._tabulist_state
if statelist and len(statelist) == 0:
statelist.add(transition.getSourceState())
# add actions/states/transitions to tabulists if given tabulist exists
if self._tabulist_action is not None:
self._tabulist_action.add(str(transition.getAction()))
if self._tabulist_state is not None:
self._tabulist_state.add(str(transition.getDestState()))
if self._tabulist_transition is not None:
self._tabulist_transition.add(str(transition))
GuidanceBase.markExecuted(self, transition)
def __init__(self):
GuidanceBase.__init__(self)
self.setParameter('maxdepth', 100)
self.setParameter('mindepth', 1)
self.setParameter('randomseed', time.time())
self._lastroute = []
self._steps_to_reroute = 0
# search front is a list of triplets: (score, path, coverage)
# where score is a pair: (coverage_percentage, steps since
# last change coverage_percentage). Thus, the bigger the
# number of steps, the faster the final coverage_percentage is
# achieved.
self.search_front = []
self._thread_id = None
self._front_shortened = 0 # msg from markExecuted to thread
def prepareForRun(self):
nonexit = "Nonexisting string"
if self.getParameter("help", nonexit) != nonexit:
print __doc__
raise Exception("Asked only for help")
GuidanceBase.prepareForRun(self)
if len(self._requirements) != 1:
raise Exception("Needs exactly one requirement")
if not self._testmodel:
raise Exception("Model should be given")
self._stored_path = []
self._to_sleep_actions =\
self._testmodel.matchedActions(set([self._sleep_ts_re]))
self._last_go_back = False
self._search_state = GoodState
self._forbiden_set = set()
self.log("Wormguidance ready for rocking")
def prepareForRun(self):
nonexit="Nonexisting string"
if self.getParameter("help", nonexit) != nonexit:
print __doc__
raise Exception("Asked only for help")
GuidanceBase.prepareForRun(self)
if len(self._requirements) != 1 :
raise Exception("Needs exactly one requirement")
if not self._testmodel :
raise Exception("Model should be given")
self._stored_path=[]
self._to_sleep_actions =\
self._testmodel.matchedActions(set([self._sleep_ts_re]))
self._last_go_back = False
self._search_state = GoodState
self._forbiden_set = set()
self.log("Wormguidance ready for rocking")
def setParameter(self, name, value):
accepted = ("transitionweight", "searchorder", "searchdepth",
"maxtransitions", "searchconstraint")
if name == "transitionweight":
if isinstance(value, str) and value.startswith('kw:'):
kww = float(value[3:])
self._transitionweight = \
lambda t: kww if 'kw_' in str(t.getAction()) else 0
else:
if value < 0:
self.log("WARNING! Negative transition weight " +
"doesn't make sense!")
self._transitionweight = lambda t: value
elif name == "searchorder":
if value == "bestfirst":
self._toHeap = lambda p, badness: (badness, len(p), p)
self._fromHeap = lambda values: (values[2], values[0])
elif value == "shortestfirst":
self._toHeap = lambda p, badness: (len(p), badness, p)
self._fromHeap = lambda values: (values[2], values[1])
else:
raise ValueError("Invalid searchorder: '%s'" % (value, ))
elif name in ("searchdepth", "searchradius"):
self._searchDepth = value
elif name == "maxtransitions":
self._maxTransitions = value
elif name == "greedy":
self._greedy = value
elif name == "searchconstraint":
if value == "nocrossingpaths":
self._seco = NO_CROSSING_PATHS
elif value == "noloops":
self._seco = NO_LOOPS
elif value == "noconstraint":
self._seco = NONE
else:
raise ValueError("Invalid searchconstraint '%s'" % value)
else:
print __doc__
raise ValueError("Invalid parameter '%s' for newguidance. " %
name +
"Accepted parameters: %s" % ",".join(accepted))
GuidanceBase.setParameter(self, name, value)
def setParameter(self, name, value):
accepted = ("transitionweight", "searchorder", "searchdepth", "maxtransitions", "searchconstraint")
if name == "transitionweight":
if isinstance(value, str) and value.startswith("kw:"):
kww = float(value[3:])
self._transitionweight = lambda t: kww if "kw_" in str(t.getAction()) else 0
else:
if value < 0:
self.log("WARNING! Negative transition weight " + "doesn't make sense!")
self._transitionweight = lambda t: value
elif name == "searchorder":
if value == "bestfirst":
self._toHeap = lambda p, badness: (badness, len(p), p)
self._fromHeap = lambda values: (values[2], values[0])
elif value == "shortestfirst":
self._toHeap = lambda p, badness: (len(p), badness, p)
self._fromHeap = lambda values: (values[2], values[1])
else:
raise ValueError("Invalid searchorder: '%s'" % (value,))
elif name in ("searchdepth", "searchradius"):
self._searchDepth = value
elif name == "maxtransitions":
self._maxTransitions = value
elif name == "greedy":
self._greedy = value
elif name == "searchconstraint":
if value == "nocrossingpaths":
self._seco = NO_CROSSING_PATHS
elif value == "noloops":
self._seco = NO_LOOPS
elif value == "noconstraint":
self._seco = NONE
else:
raise ValueError("Invalid searchconstraint '%s'" % value)
else:
print __doc__
raise ValueError(
"Invalid parameter '%s' for newguidance. " % name + "Accepted parameters: %s" % ",".join(accepted)
)
GuidanceBase.setParameter(self, name, value)
def __init__(self):
GuidanceBase.__init__(self)
self.began = 0
# initializes the random number generator
random.seed()
# a list of suggestions
# seach method appends a suggestion every time it makes a decision
# about next chosen transition, and suggestAction pops suggestions
# from the beginning of it.
self.suggestions = []
# tells how many keywords there are in the suggestions list
self.keywordsInSuggestions = 0
# the lock is acquired before accessing the variables shared by
# suggestAction and searcher threads
self.lock = threading.Lock()
# the suggestion event signals for a new suggestion added to the suggestions list
self.suggestionEvent = threading.Event()
# the state event signals for a new root state for search thread to start searching
# next suggestions (search waits for this after a verification action)
self.stateEvent = threading.Event()
# is suggestAction waiting for suggestion?
self.waitingForSuggestion = 0
# is searcher thread waiting for verification?
self.waitingForVerification = 0
# The estimated delay after a keyword execution, is updated by suggestAction.
self.keywordDelay = 0.1
def __init__(self): GuidanceBase.__init__(self) self.began = 0 # initializes the random number generator random.seed() # a list of suggestions # seach method appends a suggestion every time it makes a decision # about next chosen transition, and suggestAction pops suggestions # from the beginning of it. self.suggestions = [] # tells how many keywords there are in the suggestions list self.keywordsInSuggestions = 0 # the lock is acquired before accessing the variables shared by # suggestAction and searcher threads self.lock = threading.Lock() # the suggestion event signals for a new suggestion added to the suggestions list self.suggestionEvent = threading.Event() # the state event signals for a new root state for search thread to start searching # next suggestions (search waits for this after a verification action) self.stateEvent = threading.Event() # is suggestAction waiting for suggestion? self.waitingForSuggestion = 0 # is searcher thread waiting for verification? self.waitingForVerification = 0 # The estimated delay after a keyword execution, is updated by suggestAction. self.keywordDelay = 0.1
def __init__(self):
GuidanceBase.__init__(self)
self._stored_path=[]
self._random_select=random.Random(time.time()).choice
self._sleep_ts_re = re.compile(r"SLEEPts.*")
def __init__(self):
GuidanceBase.__init__(self)
self.setParameter('randomseed',time.time())
def prepareForRun(self):
GuidanceBase.prepareForRun(self)
self.search_front_lock = thread.allocate_lock()
def __init__(self):
GuidanceBase.__init__(self)
def __init__(self):
GuidanceBase.__init__(self)
self.setParameter('randomseed', time.time())
def markExecuted(self, transition):
self._path = self._path[1:]
GuidanceBase.markExecuted(self,transition)
def __init__(self):
GuidanceBase.__init__(self)
self._stored_path = []
self._random_select = random.Random(time.time()).choice
self._sleep_ts_re = re.compile(r"SLEEPts.*")
def prepareForRun(self):
GuidanceBase.prepareForRun(self)
self.search_front_lock=thread.allocate_lock()
def __init__(self):
GuidanceBase.__init__(self)
def __init__(self):
GuidanceBase.__init__(self)
self._port = None
self._manager = None
self._iAmTheManagerStarter = False
self._sgParams = []
def __init__(self):
GuidanceBase.__init__(self)
self._port = None
self._manager = None
self._iAmTheManagerStarter = False
self._sgParams = []
def markExecuted(self, transition):
self._path = self._path[1:]
GuidanceBase.markExecuted(self, transition)
