"""This module implements basic symbolic execution search strategies."""
from random import randrange
from typing import List
from mythril.laser.ethereum.state.global_state import GlobalState
from . import BasicSearchStrategy
from random import choices
[docs]class DepthFirstSearchStrategy(BasicSearchStrategy):
"""Implements a depth first search strategy I.E.
Follow one path to a leaf, and then continue to the next one
"""
[docs] def get_strategic_global_state(self) -> GlobalState:
"""
:return:
"""
return self.work_list.pop()
[docs] def view_strategic_global_state(self) -> GlobalState:
"""
:return:
"""
return self.work_list[-1]
[docs]class BreadthFirstSearchStrategy(BasicSearchStrategy):
"""Implements a breadth first search strategy I.E.
Execute all states of a "level" before continuing
"""
[docs] def get_strategic_global_state(self) -> GlobalState:
"""
:return:
"""
return self.work_list.pop(0)
[docs] def view_strategic_global_state(self) -> GlobalState:
"""
:return:
"""
return self.work_list[0]
[docs]class ReturnRandomNaivelyStrategy(BasicSearchStrategy):
"""chooses a random state from the worklist with equal likelihood."""
def __init__(self, work_list, max_depth, **kwargs):
super().__init__(work_list, max_depth, **kwargs)
self.previous_random_value = -1
[docs] def get_strategic_global_state(self) -> GlobalState:
"""
:return:
"""
if len(self.work_list) > 0:
if self.previous_random_value == -1:
return self.work_list.pop(randrange(len(self.work_list)))
else:
new_state = self.work_list.pop(self.previous_random_value)
self.previous_random_value = -1
return new_state
else:
raise IndexError
[docs] def view_strategic_global_state(self) -> GlobalState:
"""
:return:
"""
if len(self.work_list) > 0:
self.previous_random_value = randrange(len(self.work_list))
return self.work_list[self.previous_random_value]
else:
raise IndexError
[docs]class ReturnWeightedRandomStrategy(BasicSearchStrategy):
"""chooses a random state from the worklist with likelihood based on
inverse proportion to depth."""
def __init__(self, work_list, max_depth, **kwargs):
super().__init__(work_list, max_depth, **kwargs)
self.previous_random_value = -1
[docs] def get_strategic_global_state(self) -> GlobalState:
"""
:return:
"""
probability_distribution = [
1 / (global_state.mstate.depth + 1) for global_state in self.work_list
]
if self.previous_random_value != -1:
ns = self.work_list.pop(self.previous_random_value)
self.previous_random_value = -1
return ns
else:
return self.work_list.pop(
choices(range(len(self.work_list)), probability_distribution)[0]
)
[docs] def view_strategic_global_state(self) -> GlobalState:
"""
:return:
"""
probability_distribution = [
1 / (global_state.mstate.depth + 1) for global_state in self.work_list
]
self.previous_random_value = choices(
range(len(self.work_list)), probability_distribution
)[0]
return self.work_list[self.previous_random_value]