Instruction:
Can you generate the documentation for the following Python 3 function
def _solver(self):
        if self._stored_solver is not None:
            return self._stored_solver

        track = o.CONSTRAINT_TRACKING_IN_SOLVER in self.state.options
        approximate_first = o.APPROXIMATE_FIRST in self.state.options

        if o.STRINGS_ANALYSIS in self.state.options:
            if 'smtlib_cvc4' in backend_manager.backends._backends_by_name:
                our_backend = backend_manager.backends.smtlib_cvc4
            elif 'smtlib_z3' in backend_manager.backends._backends_by_name:
                our_backend = backend_manager.backends.smtlib_z3
            elif 'smtlib_abc' in backend_manager.backends._backends_by_name:
                our_backend = backend_manager.backends.smtlib_abc
            else:
                raise ValueError("Could not find suitable string solver!")
            if o.COMPOSITE_SOLVER in self.state.options:
                self._stored_solver = claripy.SolverComposite(
                    template_solver_string=claripy.SolverCompositeChild(backend=our_backend, track=track)
                )
        elif o.ABSTRACT_SOLVER in self.state.options:
            self._stored_solver = claripy.SolverVSA()
        elif o.SYMBOLIC in self.state.options and o.REPLACEMENT_SOLVER in self.state.options:
            self._stored_solver = claripy.SolverReplacement(auto_replace=False)
        elif o.SYMBOLIC in self.state.options and o.CACHELESS_SOLVER in self.state.options:
            self._stored_solver = claripy.SolverCacheless(track=track)
        elif o.SYMBOLIC in self.state.options and o.COMPOSITE_SOLVER in self.state.options:
            self._stored_solver = claripy.SolverComposite(track=track)
        elif o.SYMBOLIC in self.state.options and any(opt in self.state.options for opt in o.approximation):
            self._stored_solver = claripy.SolverHybrid(track=track, approximate_first=approximate_first)
        elif o.HYBRID_SOLVER in self.state.options:
            self._stored_solver = claripy.SolverHybrid(track=track, approximate_first=approximate_first)
        elif o.SYMBOLIC in self.state.options:
            self._stored_solver = claripy.Solver(track=track)
        else:
            self._stored_solver = claripy.SolverConcrete()

        return self._stored_solver

Response: Creates or gets a Claripy solver based on the state options.


Instruction:
Create a Python 3 function for
creating an unconstrained BV with the specified name and size.

Response:
def Unconstrained(self, name, bits, uninitialized=True, inspect=True, events=True, key=None, eternal=False, **kwargs):
        """
        Creates an unconstrained symbol or a default concrete value (0), based on the state options.

        :param name:            The name of the symbol.
        :param bits:            The size (in bits) of the symbol.
        :param uninitialized:   Whether this value should be counted as an "uninitialized" value in the course of an
                                analysis.
        :param inspect:         Set to False to avoid firing SimInspect breakpoints
        :param events:          Set to False to avoid generating a SimEvent for the occasion
        :param key:             Set this to a tuple of increasingly specific identifiers (for example,
                                ``('mem', 0xffbeff00)`` or ``('file', 4, 0x20)`` to cause it to be tracked, i.e.
                                accessable through ``solver.get_variables``.
        :param eternal:         Set to True in conjunction with setting a key to cause all states with the same
                                ancestry to retrieve the same symbol when trying to create the value. If False, a
                                counter will be appended to the key.

        :returns:               an unconstrained symbol (or a concrete value of 0).
        """
        if o.SYMBOLIC_INITIAL_VALUES in self.state.options:
            # Return a symbolic value
            if o.ABSTRACT_MEMORY in self.state.options:
                l.debug("Creating new top StridedInterval")
                r = claripy.TSI(bits=bits, name=name, uninitialized=uninitialized, **kwargs)
            else:
                l.debug("Creating new unconstrained BV named %s", name)
                if o.UNDER_CONSTRAINED_SYMEXEC in self.state.options:
                    r = self.BVS(name, bits, uninitialized=uninitialized, key=key, eternal=eternal, inspect=inspect, events=events, **kwargs)
                else:
                    r = self.BVS(name, bits, uninitialized=uninitialized, key=key, eternal=eternal, inspect=inspect, events=events, **kwargs)

            return r
        else:
            # Return a default value, aka. 0
            return claripy.BVV(0, bits)


Instruction:
Write a Python 3 function that can
create a bit - vector symbol.

Response:
def BVS(self, name, size,
            min=None, max=None, stride=None,
            uninitialized=False,
            explicit_name=None, key=None, eternal=False,
            inspect=True, events=True,
            **kwargs): #pylint:disable=redefined-builtin
        """
        Creates a bit-vector symbol (i.e., a variable). Other keyword parameters are passed directly on to the
        constructor of claripy.ast.BV.

        :param name:            The name of the symbol.
        :param size:            The size (in bits) of the bit-vector.
        :param min:             The minimum value of the symbol. Note that this **only** work when using VSA.
        :param max:             The maximum value of the symbol. Note that this **only** work when using VSA.
        :param stride:          The stride of the symbol. Note that this **only** work when using VSA.
        :param uninitialized:   Whether this value should be counted as an "uninitialized" value in the course of an
                                analysis.
        :param explicit_name:   Set to True to prevent an identifier from appended to the name to ensure uniqueness.
        :param key:             Set this to a tuple of increasingly specific identifiers (for example,
                                ``('mem', 0xffbeff00)`` or ``('file', 4, 0x20)`` to cause it to be tracked, i.e.
                                accessable through ``solver.get_variables``.
        :param eternal:         Set to True in conjunction with setting a key to cause all states with the same
                                ancestry to retrieve the same symbol when trying to create the value. If False, a
                                counter will be appended to the key.
        :param inspect:         Set to False to avoid firing SimInspect breakpoints
        :param events:          Set to False to avoid generating a SimEvent for the occasion

        :return:                A BV object representing this symbol.
        """

        # should this be locked for multithreading?
        if key is not None and eternal and key in self.eternal_tracked_variables:
            r = self.eternal_tracked_variables[key]
            # pylint: disable=too-many-boolean-expressions
            if size != r.length or min != r.args[1] or max != r.args[2] or stride != r.args[3] or uninitialized != r.args[4] or bool(explicit_name) ^ (r.args[0] == name):
                l.warning("Variable %s being retrieved with differnt settings than it was tracked with", name)
        else:
            r = claripy.BVS(name, size, min=min, max=max, stride=stride, uninitialized=uninitialized, explicit_name=explicit_name, **kwargs)
            if key is not None:
                self.register_variable(r, key, eternal)

        if inspect:
            self.state._inspect('symbolic_variable', BP_AFTER, symbolic_name=next(iter(r.variables)), symbolic_size=size, symbolic_expr=r)
        if events:
            self.state.history.add_event('unconstrained', name=next(iter(r.variables)), bits=size, **kwargs)
        if o.TRACK_SOLVER_VARIABLES in self.state.options:
            self.all_variables = list(self.all_variables)
            self.all_variables.append(r)
        return r


Instruction:
Given the following Python 3 function, write the documentation
def eval_to_ast(self, e, n, extra_constraints=(), exact=None):
        return self._solver.eval_to_ast(e, n, extra_constraints=self._adjust_constraint_list(extra_constraints), exact=exact)

Response: Evaluate an expression using the solver if necessary. Returns an AST object.


Instruction:
Given the following Python 3 function, write the documentation
def _eval(self, e, n, extra_constraints=(), exact=None):
        return self._solver.eval(e, n, extra_constraints=self._adjust_constraint_list(extra_constraints), exact=exact)

Response: Evaluate an expression using the solver if necessary. Returns a tuple of the solutions that are approximate.


Instruction:
Implement a Python 3 function for
returning the maximum value of expression e.

Response:
def max(self, e, extra_constraints=(), exact=None):
        """
        Return the maximum value of expression `e`.

        :param e                : expression (an AST) to evaluate
        :param extra_constraints: extra constraints (as ASTs) to add to the solver for this solve
        :param exact            : if False, return approximate solutions.
        :return: the maximum possible value of e (backend object)
        """
        if exact is False and o.VALIDATE_APPROXIMATIONS in self.state.options:
            ar = self._solver.max(e, extra_constraints=self._adjust_constraint_list(extra_constraints), exact=False)
            er = self._solver.max(e, extra_constraints=self._adjust_constraint_list(extra_constraints))
            assert er <= ar
            return ar
        return self._solver.max(e, extra_constraints=self._adjust_constraint_list(extra_constraints), exact=exact)


Instruction:
Can you implement a function in Python 3 that
returns True if v is a solution of expr False otherwise.

Response:
def solution(self, e, v, extra_constraints=(), exact=None):
        """
        Return True if `v` is a solution of `expr` with the extra constraints, False otherwise.

        :param e:                   An expression (an AST) to evaluate
        :param v:                   The proposed solution (an AST)
        :param extra_constraints:   Extra constraints (as ASTs) to add to the solver for this solve.
        :param exact:               If False, return approximate solutions.
        :return:                    True if `v` is a solution of `expr`, False otherwise
        """
        if exact is False and o.VALIDATE_APPROXIMATIONS in self.state.options:
            ar = self._solver.solution(e, v, extra_constraints=self._adjust_constraint_list(extra_constraints), exact=False)
            er = self._solver.solution(e, v, extra_constraints=self._adjust_constraint_list(extra_constraints))
            if er is True:
                assert ar is True
            return ar
        return self._solver.solution(e, v, extra_constraints=self._adjust_constraint_list(extra_constraints), exact=exact)


Instruction:
Explain what the following Python 3 code does
def unsat_core(self, extra_constraints=()):
        if o.CONSTRAINT_TRACKING_IN_SOLVER not in self.state.options:
            raise SimSolverOptionError('CONSTRAINT_TRACKING_IN_SOLVER must be enabled before calling unsat_core().')
        return self._solver.unsat_core(extra_constraints=extra_constraints)

Response: This function returns the unsatisfiable version of the current core.


Instruction:
Given the following Python 3 function, write the documentation
def satisfiable(self, extra_constraints=(), exact=None):
        if exact is False and o.VALIDATE_APPROXIMATIONS in self.state.options:
            er = self._solver.satisfiable(extra_constraints=self._adjust_constraint_list(extra_constraints))
            ar = self._solver.satisfiable(extra_constraints=self._adjust_constraint_list(extra_constraints), exact=False)
            if er is True:
                assert ar is True
            return ar
        return self._solver.satisfiable(extra_constraints=self._adjust_constraint_list(extra_constraints), exact=exact)

Response: This function checks if the solver is in a sat state.


Instruction:
Make a summary of the following Python 3 code
def add(self, *constraints):
        cc = self._adjust_constraint_list(constraints)
        return self._solver.add(cc)

Response: Adds some constraints to the solver.


Instruction:
How would you implement a function in Python 3 that
casts a solution for the given expression to type cast_to.

Response:
def _cast_to(e, solution, cast_to):
        """
        Casts a solution for the given expression to type `cast_to`.

        :param e: The expression `value` is a solution for
        :param value: The solution to be cast
        :param cast_to: The type `value` should be cast to. Must be one of the currently supported types (bytes|int)
        :raise ValueError: If cast_to is a currently unsupported cast target.
        :return: The value of `solution` cast to type `cast_to`
        """
        if cast_to is None:
            return solution

        if type(solution) is bool:
            if cast_to is bytes:
                return bytes([int(solution)])
            elif cast_to is int:
                return int(solution)
        elif type(solution) is float:
            solution = _concrete_value(claripy.FPV(solution, claripy.fp.FSort.from_size(len(e))).raw_to_bv())

        if cast_to is bytes:
            if len(e) == 0:
                return b""
            return binascii.unhexlify('{:x}'.format(solution).zfill(len(e)//4))

        if cast_to is not int:
            raise ValueError("cast_to parameter {!r} is not a valid cast target, currently supported are only int and bytes!".format(cast_to))

        return solution


Instruction:
Here you have a function in Python 3, explain what it does
def eval_upto(self, e, n, cast_to=None, **kwargs):
        concrete_val = _concrete_value(e)
        if concrete_val is not None:
            return [self._cast_to(e, concrete_val, cast_to)]

        cast_vals = [self._cast_to(e, v, cast_to) for v in self._eval(e, n, **kwargs)]
        if len(cast_vals) == 0:
            raise SimUnsatError('Not satisfiable: %s, expected up to %d solutions' % (e.shallow_repr(), n))
        return cast_vals

Response: Evaluate an expression and return a list of possible values.


Instruction:
How would you explain what the following Python 3 function does
def eval_one(self, e, **kwargs):
        try:
            return self.eval_exact(e, 1, **{k: v for (k, v) in kwargs.items() if k != 'default'})[0]
        except (SimUnsatError, SimValueError, SimSolverModeError):
            if 'default' in kwargs:
                return kwargs.pop('default')
            raise

Response: Evaluate an expression to get the only possible solution. Errors if either no or more than one solution is returned.


Instruction:
Here you have a function in Python 3, explain what it does
def eval_atmost(self, e, n, **kwargs):
        r = self.eval_upto(e, n+1, **kwargs)
        if len(r) > n:
            raise SimValueError("Concretized %d values (must be at most %d) in eval_atmost" % (len(r), n))
        return r

Response: Evaluate an expression to get at most n possible solutions. Errors if either none or more than n solutions are returned.


Instruction:
Implement a function in Python 3 to
return True if the expression e has only one solution by querying the constraint solver. It does also add that unique solution to the constraints.

Response:
def unique(self, e, **kwargs):
        """
        Returns True if the expression `e` has only one solution by querying
        the constraint solver. It does also add that unique solution to the
        solver's constraints.
        """
        if not isinstance(e, claripy.ast.Base):
            return True

        # if we don't want to do symbolic checks, assume symbolic variables are multivalued
        if o.SYMBOLIC not in self.state.options and self.symbolic(e):
            return False

        r = self.eval_upto(e, 2, **kwargs)
        if len(r) == 1:
            self.add(e == r[0])
            return True
        elif len(r) == 0:
            raise SimValueError("unsatness during uniqueness check(ness)")
        else:
            return False


Instruction:
How would you explain what the following Python 3 function does
def symbolic(self, e): # pylint:disable=R0201
        if type(e) in (int, bytes, float, bool):
            return False
        return e.symbolic

Response: Returns True if the expression e is symbolic.


Instruction:
Implement a function in Python 3 to
return True if e is a concrete value or is a value set with only 1 possible value.

Response:
def single_valued(self, e):
        """
        Returns True whether `e` is a concrete value or is a value set with
        only 1 possible value. This differs from `unique` in that this *does*
        not query the constraint solver.
        """
        if self.state.mode == 'static':
            if type(e) in (int, bytes, float, bool):
                return True
            else:
                return e.cardinality <= 1

        else:
            # All symbolic expressions are not single-valued
            return not self.symbolic(e)


Instruction:
Create a Python 3 function to
simplify the state of this object.

Response:
def simplify(self, e=None):
        """
        Simplifies `e`. If `e` is None, simplifies the constraints of this
        state.
        """
        if e is None:
            return self._solver.simplify()
        elif isinstance(e, (int, float, bool)):
            return e
        elif isinstance(e, claripy.ast.Base) and e.op in claripy.operations.leaf_operations_concrete:
            return e
        elif isinstance(e, SimActionObject) and e.op in claripy.operations.leaf_operations_concrete:
            return e.ast
        elif not isinstance(e, (SimActionObject, claripy.ast.Base)):
            return e
        else:
            return self._claripy_simplify(e)


Instruction:
How would you code a function in Python 3 to
add a preconstraint that variable == value to the state.

Response:
def preconstrain(self, value, variable):
        """
        Add a preconstraint that ``variable == value`` to the state.

        :param value:       The concrete value. Can be a bitvector or a bytestring or an integer.
        :param variable:    The BVS to preconstrain.
        """
        if not isinstance(value, claripy.ast.Base):
            value = self.state.solver.BVV(value, len(variable))
        elif value.op != 'BVV':
            raise ValueError("Passed a value to preconstrain that was not a BVV or a string")

        if variable.op not in claripy.operations.leaf_operations:
            l.warning("The variable %s to preconstrain is not a leaf AST. This may cause replacement failures in the "
                      "claripy replacement backend.", variable)
            l.warning("Please use a leaf AST as the preconstraining variable instead.")

        constraint = variable == value
        l.debug("Preconstraint: %s", constraint)

        # add the constraint for reconstraining later
        self.variable_map[next(iter(variable.variables))] = constraint
        self.preconstraints.append(constraint)
        if o.REPLACEMENT_SOLVER in self.state.options:
            self.state.solver._solver.add_replacement(variable, value, invalidate_cache=False)
        else:
            self.state.add_constraints(*self.preconstraints)
        if not self.state.satisfiable():
            l.warning("State went unsat while adding preconstraints")


Instruction:
Implement a Python 3 function for
removing the preconstraints from the state.

Response:
def remove_preconstraints(self, to_composite_solver=True, simplify=True):
        """
        Remove the preconstraints from the state.

        If you are using the zen plugin, this will also use that to filter the constraints.

        :param to_composite_solver:     Whether to convert the replacement solver to a composite solver. You probably
                                        want this if you're switching from tracing to symbolic analysis.
        :param simplify:                Whether to simplify the resulting set of constraints.
        """
        if not self.preconstraints:
            return

        # cache key set creation
        precon_cache_keys = set()

        for con in self.preconstraints:
            precon_cache_keys.add(con.cache_key)

        # if we used the replacement solver we didn't add constraints we need to remove so keep all constraints
        if o.REPLACEMENT_SOLVER in self.state.options:
            new_constraints = self.state.solver.constraints
        else:
            new_constraints = list(filter(lambda x: x.cache_key not in precon_cache_keys, self.state.solver.constraints))

        if self.state.has_plugin("zen_plugin"):
            new_constraints = self.state.get_plugin("zen_plugin").filter_constraints(new_constraints)

        if to_composite_solver:
            self.state.options.discard(o.REPLACEMENT_SOLVER)
            self.state.options.add(o.COMPOSITE_SOLVER)

        # clear the solver's internal memory and replace it with the new solver options and constraints
        self.state.solver.reload_solver(new_constraints)

        if simplify:
            l.debug("simplifying solver...")
            self.state.solver.simplify()
            l.debug("...simplification done")


Instruction:
Can you generate a brief explanation for the following Python 3 code
def reconstrain(self):

        # test all solver splits
        subsolvers = self.state.solver._solver.split()

        for solver in subsolvers:
            solver.timeout = 1000 * 10  # 10 seconds
            if not solver.satisfiable():
                for var in solver.variables:
                    if var in self.variable_map:
                        self.state.solver.add(self.variable_map[var])
                    else:
                        l.warning("var %s not found in self.variable_map", var)

Response: Split the solver and re - add any preconstraints associated with each of the variables.


Instruction:
How would you explain what the following Python 3 function does
def add_ref(self, ref):

        self.refs[ref.insn_addr].append(ref)
        self.data_addr_to_ref[ref.memory_data.addr].append(ref)

Response: Adds a reference to a memory data object.


Instruction:
Can you write a function in Python 3 where it
gets an arbitrary CFGNode from the segment tree.

Response:
def get_any_node(self, addr, is_syscall=None, anyaddr=False, force_fastpath=False):
        """
        Get an arbitrary CFGNode (without considering their contexts) from our graph.

        :param int addr:        Address of the beginning of the basic block. Set anyaddr to True to support arbitrary
                                address.
        :param bool is_syscall: Whether you want to get the syscall node or any other node. This is due to the fact that
                                syscall SimProcedures have the same address as the targer it returns to.
                                None means get either, True means get a syscall node, False means get something that isn't
                                a syscall node.
        :param bool anyaddr:    If anyaddr is True, then addr doesn't have to be the beginning address of a basic
                                block. By default the entire graph.nodes() will be iterated, and the first node
                                containing the specific address is returned, which is slow. If you need to do many such
                                queries, you may first call `generate_index()` to create some indices that may speed up the
                                query.
        :param bool force_fastpath: If force_fastpath is True, it will only perform a dict lookup in the _nodes_by_addr
                                    dict.
        :return: A CFGNode if there is any that satisfies given conditions, or None otherwise
        """

        # fastpath: directly look in the nodes list
        if not anyaddr:
            try:
                return self._nodes_by_addr[addr][0]
            except (KeyError, IndexError):
                pass

        if force_fastpath:
            return None

        # slower path
        #if self._node_lookup_index is not None:
        #    pass

        # the slowest path
        # try to show a warning first
        # TODO: re-enable it once the segment tree is implemented
        #if self._node_lookup_index_warned == False:
        #    l.warning('Calling get_any_node() with anyaddr=True is slow on large programs. '
        #              'For better performance, you may first call generate_index() to generate some indices that may '
        #              'speed the node lookup.')
        #    self._node_lookup_index_warned = True

        for n in self.graph.nodes():
            if self.ident == "CFGEmulated":
                cond = n.looping_times == 0
            else:
                cond = True
            if anyaddr and n.size is not None:
                cond = cond and n.addr <= addr < n.addr + n.size
            else:
                cond = cond and (addr == n.addr)
            if cond:
                if is_syscall is None:
                    return n
                if n.is_syscall == is_syscall:
                    return n

        return None


Instruction:
Write a Python 3 function for
getting all CFGNodes whose address is the specified one.

Response:
def get_all_nodes(self, addr, is_syscall=None, anyaddr=False):
        """
        Get all CFGNodes whose address is the specified one.

        :param addr:       Address of the node
        :param is_syscall: True returns the syscall node, False returns the normal CFGNode, None returns both
        :return:           all CFGNodes
        """
        results = [ ]

        for cfg_node in self.graph.nodes():
            if cfg_node.addr == addr or (anyaddr and
                                         cfg_node.size is not None and
                                         cfg_node.addr <= addr < (cfg_node.addr + cfg_node.size)
                                         ):
                if is_syscall and cfg_node.is_syscall:
                    results.append(cfg_node)
                elif is_syscall is False and not cfg_node.is_syscall:
                    results.append(cfg_node)
                else:
                    results.append(cfg_node)

        return results


Instruction:
Write a Python 3 function that can
return a list of predecessors of a CFGNode.

Response:
def get_predecessors(self, cfgnode, excluding_fakeret=True, jumpkind=None):
        """
        Get predecessors of a node in the control flow graph.

        :param CFGNode cfgnode:             The node.
        :param bool excluding_fakeret:      True if you want to exclude all predecessors that is connected to the node
                                            with a fakeret edge.
        :param str or None jumpkind:        Only return predecessors with the specified jumpkind. This argument will be
                                            ignored if set to None.
        :return:                            A list of predecessors
        :rtype:                             list
        """

        if excluding_fakeret and jumpkind == 'Ijk_FakeRet':
            return [ ]

        if not excluding_fakeret and jumpkind is None:
            # fast path
            if cfgnode in self.graph:
                return list(self.graph.predecessors(cfgnode))
            return [ ]

        predecessors = []
        for pred, _, data in self.graph.in_edges([cfgnode], data=True):
            jk = data['jumpkind']
            if jumpkind is not None:
                if jk == jumpkind:
                    predecessors.append(pred)
            elif excluding_fakeret:
                if jk != 'Ijk_FakeRet':
                    predecessors.append(pred)
            else:
                predecessors.append(pred)
        return predecessors


Instruction:
Here you have a function in Python 3, explain what it does
def get_successors(self, node, excluding_fakeret=True, jumpkind=None):

        if jumpkind is not None:
            if excluding_fakeret and jumpkind == 'Ijk_FakeRet':
                return [ ]

        if not excluding_fakeret and jumpkind is None:
            # fast path
            if node in self.graph:
                return list(self.graph.successors(node))
            return [ ]

        successors = []
        for _, suc, data in self.graph.out_edges([node], data=True):
            jk = data['jumpkind']
            if jumpkind is not None:
                if jumpkind == jk:
                    successors.append(suc)
            elif excluding_fakeret:
                if jk != 'Ijk_FakeRet':
                    successors.append(suc)
            else:
                successors.append(suc)
        return successors

Response: Get successors of a node in the control flow graph.


Instruction:
Explain what the following Python 3 code does
def get_successors_and_jumpkind(self, node, excluding_fakeret=True):

        successors = []
        for _, suc, data in self.graph.out_edges([node], data=True):
            if not excluding_fakeret or data['jumpkind'] != 'Ijk_FakeRet':
                successors.append((suc, data['jumpkind']))
        return successors

Response: Get a list of tuples where the first element is the successor of the node and the second element is the jumpkind of the successor.


Instruction:
Can you write a function in Python 3 where it
gets all predecessors of a specific node on the control flow graph.

Response:
def get_all_predecessors(self, cfgnode):
        """
        Get all predecessors of a specific node on the control flow graph.

        :param CFGNode cfgnode: The CFGNode object
        :return: A list of predecessors in the CFG
        :rtype: list
        """
        s = set()
        for child, parent in networkx.dfs_predecessors(self.graph, cfgnode).items():
            s.add(child)
            s.add(parent)
        return list(s)


Instruction:
Can you write a function in Python 3 where it
returns all nodes that have an out degree > = 2

Response:
def get_branching_nodes(self):
        """
        Returns all nodes that has an out degree >= 2
        """
        nodes = set()
        for n in self.graph.nodes():
            if self.graph.out_degree(n) >= 2:
                nodes.add(n)
        return nodes


Instruction:
Can you create a Python 3 function that
gets the exit statement ID for control flow to reach source block from destination block.

Response:
def get_exit_stmt_idx(self, src_block, dst_block):
        """
        Get the corresponding exit statement ID for control flow to reach destination block from source block. The exit
        statement ID was put on the edge when creating the CFG.
        Note that there must be a direct edge between the two blocks, otherwise an exception will be raised.

        :return: The exit statement ID
        """

        if not self.graph.has_edge(src_block, dst_block):
            raise AngrCFGError('Edge (%s, %s) does not exist in CFG' % (src_block, dst_block))

        return self.graph[src_block][dst_block]['stmt_idx']


Instruction:
How would you explain what the following Python 3 function does
def prepare_native_return_state(native_state):

        javavm_simos = native_state.project.simos
        ret_state = native_state.copy()

        # set successor flags
        ret_state.regs._ip = ret_state.callstack.ret_addr
        ret_state.scratch.guard = ret_state.solver.true
        ret_state.history.jumpkind = 'Ijk_Ret'

        # if available, lookup the return value in native memory
        ret_var = ret_state.callstack.invoke_return_variable
        if ret_var is not None:
            # get return symbol from native state
            native_cc = javavm_simos.get_native_cc()
            ret_symbol = native_cc.get_return_val(native_state).to_claripy()
            # convert value to java type
            if ret_var.type in ArchSoot.primitive_types:
                # return value has a primitive type
                # => we need to manually cast the return value to the correct size, as this
                #    would be usually done by the java callee
                ret_value = javavm_simos.cast_primitive(ret_state, ret_symbol,
                                                        to_type=ret_var.type)
            else:
                # return value has a reference type
                # => ret_symbol is a opaque ref
                # => lookup corresponding java reference
                ret_value = ret_state.jni_references.lookup(ret_symbol)

        else:
            ret_value = None

        # teardown return state
        SimEngineSoot.prepare_return_state(ret_state, ret_value)

        # finally, delete all local references
        ret_state.jni_references.clear_local_references()

        return [ret_state]

Response: Prepare the state for a native function call returns.


Instruction:
Implement a function in Python 3 to
return a concretization of the contents of the file as a flat bytestring.

Response:
def concretize(self, **kwargs):
        """
        Return a concretization of the contents of the file, as a flat bytestring.
        """
        size = self.state.solver.min(self._size, **kwargs)
        data = self.load(0, size)

        kwargs['cast_to'] = kwargs.get('cast_to', bytes)
        kwargs['extra_constraints'] = tuple(kwargs.get('extra_constraints', ())) + (self._size == size,)
        return self.state.solver.eval(data, **kwargs)


Instruction:
Implement a function in Python 3 to
return a list of the packets read or written as bytestrings.

Response:
def concretize(self, **kwargs):
        """
        Returns a list of the packets read or written as bytestrings.
        """
        lengths = [self.state.solver.eval(x[1], **kwargs) for x in self.content]
        kwargs['cast_to'] = bytes
        return [b'' if i == 0 else self.state.solver.eval(x[0][i*self.state.arch.byte_width-1:], **kwargs) for i, x in zip(lengths, self.content)]


Instruction:
Can you write a function in Python 3 where it
reads a single entry from the stream.

Response:
def read(self, pos, size, **kwargs):
        """
        Read a packet from the stream.

        :param int pos:     The packet number to read from the sequence of the stream. May be None to append to the stream.
        :param size:        The size to read. May be symbolic.
        :param short_reads: Whether to replace the size with a symbolic value constrained to less than or equal to the original size. If unspecified, will be chosen based on the state option.
        :return:            A tuple of the data read (a bitvector of the length that is the maximum length of the read) and the actual size of the read.
        """
        short_reads = kwargs.pop('short_reads', None)

        # sanity check on read/write modes
        if self.write_mode is None:
            self.write_mode = False
        elif self.write_mode is True:
            raise SimFileError("Cannot read and write to the same SimPackets")

        # sanity check on packet number and determine if data is already present
        if pos is None:
            pos = len(self.content)
        if pos < 0:
            raise SimFileError("SimPacket.read(%d): Negative packet number?" % pos)
        elif pos > len(self.content):
            raise SimFileError("SimPacket.read(%d): Packet number is past frontier of %d?" % (pos, len(self.content)))
        elif pos != len(self.content):
            _, realsize = self.content[pos]
            self.state.solver.add(size <= realsize)
            if not self.state.solver.satisfiable():
                raise SimFileError("Packet read size constraint made state unsatisfiable???")
            return self.content[pos] + (pos+1,)

        # typecheck
        if type(size) is int:
            size = self.state.solver.BVV(size, self.state.arch.bits)

        # The read is on the frontier. let's generate a new packet.
        orig_size = size
        max_size = None

        # if short reads are enabled, replace size with a symbol
        if short_reads is True or (short_reads is None and sim_options.SHORT_READS in self.state.options):
            size = self.state.solver.BVS('packetsize_%d_%s' % (len(self.content), self.ident), self.state.arch.bits, key=('file', self.ident, 'packetsize', len(self.content)))
            self.state.solver.add(size <= orig_size)

        # figure out the maximum size of the read
        if not self.state.solver.symbolic(size):
            max_size = self.state.solver.eval(size)
        elif self.state.solver.satisfiable(extra_constraints=(size <= self.state.libc.max_packet_size,)):
            l.info("Constraining symbolic packet size to be less than %d", self.state.libc.max_packet_size)
            if not self.state.solver.is_true(orig_size <= self.state.libc.max_packet_size):
                self.state.solver.add(size <= self.state.libc.max_packet_size)
            if not self.state.solver.symbolic(orig_size):
                max_size = min(self.state.solver.eval(orig_size), self.state.libc.max_packet_size)
            else:
                max_size = self.state.solver.max(size)
        else:
            max_size = self.state.solver.min(size)
            l.warning("Could not constrain symbolic packet size to <= %d; using minimum %d for size", self.state.libc.max_packet_size, max_size)
            self.state.solver.add(size == max_size)

        # generate the packet data and return it
        data = self.state.solver.BVS('packet_%d_%s' % (len(self.content), self.ident), max_size * self.state.arch.byte_width, key=('file', self.ident, 'packet', len(self.content)))
        packet = (data, size)
        self.content.append(packet)
        return packet + (pos+1,)


Instruction:
Write a Python 3 script to
write a packet to the stream.

Response:
def write(self, pos, data, size=None, events=True, **kwargs):
        """
        Write a packet to the stream.

        :param int pos:     The packet number to write in the sequence of the stream. May be None to append to the stream.
        :param data:        The data to write, as a string or bitvector.
        :param size:        The optional size to write. May be symbolic; must be constrained to at most the size of data.
        :return:            The next packet to use after this
        """
        if events:
            self.state.history.add_event('fs_write', filename=self.name, data=data, size=size, pos=pos)

        # sanity check on read/write modes
        if self.write_mode is None:
            self.write_mode = True
        elif self.write_mode is False:
            raise SimFileError("Cannot read and write to the same SimPackets")

        data = _deps_unpack(data)[0]
        if type(data) is bytes:
            data = claripy.BVV(data)
        if size is None:
            size = len(data) // self.state.arch.byte_width if isinstance(data, claripy.Bits) else len(data)
        if type(size) is int:
            size = self.state.solver.BVV(size, self.state.arch.bits)

        # sanity check on packet number and determine if data is already present
        if pos < 0:
            raise SimFileError("SimPacket.write(%d): Negative packet number?" % pos)
        elif pos > len(self.content):
            raise SimFileError("SimPacket.write(%d): Packet number is past frontier of %d?" % (pos, len(self.content)))
        elif pos != len(self.content):
            realdata, realsize = self.content[pos]
            maxlen = max(len(realdata), len(data))
            self.state.solver.add(realdata[maxlen-1:0] == data[maxlen-1:0])
            self.state.solver.add(size == realsize)
            if not self.state.solver.satisfiable():
                raise SimFileError("Packet write equality constraints made state unsatisfiable???")
            return pos+1

        # write it out!
        self.content.append((_deps_unpack(data)[0], size))
        return pos+1


Instruction:
Write a Python 3 function for
reading some data from the file and stores it into memory.

Response:
def read(self, pos, size, **kwargs):
        """
        Reads some data from the file, storing it into memory.

        :param pos:     The address to write the read data into memory
        :param size:    The requested length of the read
        :return:        The real length of the read
        """
        data, realsize = self.read_data(size, **kwargs)
        if not self.state.solver.is_true(realsize == 0):
            self.state.memory.store(pos, data, size=realsize)
        return realsize


Instruction:
Write a Python 3 function for
writing some data from the state memory into the file.

Response:
def write(self, pos, size, **kwargs):
        """
        Writes some data, loaded from the state, into the file.

        :param pos:     The address to read the data to write from in memory
        :param size:    The requested size of the write
        :return:        The real length of the write
        """
        if type(pos) is str:
            raise TypeError("SimFileDescriptor.write takes an address and size. Did you mean write_data?")

        # Find a reasonable concrete size for the load since we don't want to concretize anything
        # This is copied from SimFile.read
        # TODO: refactor into a generic concretization strategy?
        if self.state.solver.symbolic(size):
            try:
                passed_max_size = self.state.solver.max(size, extra_constraints=(size < self.state.libc.max_packet_size,))
            except SimSolverError:
                passed_max_size = self.state.solver.min(size)
                l.warning("Symbolic write size is too large for threshold - concretizing to min (%d)", passed_max_size)
                self.state.solver.add(size == passed_max_size)
        else:
            passed_max_size = self.state.solver.eval(size)
            if passed_max_size > 2**13:
                l.warning("Program performing extremely large write")

        data = self.state.memory.load(pos, passed_max_size)
        return self.write_data(data, size, **kwargs)


Instruction:
Implement a Python 3 function for
returning a concretization of the underlying files as a tuple of read file and write file.

Response:
def concretize(self, **kwargs):
        """
        Return a concretization of the underlying files, as a tuple of (read file, write file).
        """
        return (self._read_file.concretize(**kwargs), self._write_file.concretize(**kwargs))


Instruction:
Write a Python 3 function that can
find a sinkhole which can hold length bytes.

Response:
def get_max_sinkhole(self, length):
        """
        Find a sinkhole which is large enough to support `length` bytes.

        This uses first-fit. The first sinkhole (ordered in descending order by their address)
        which can hold `length` bytes is chosen. If there are more than `length` bytes in the
        sinkhole, a new sinkhole is created representing the remaining bytes while the old
        sinkhole is removed.
        """

        ordered_sinks = sorted(list(self.sinkholes), key=operator.itemgetter(0), reverse=True)
        max_pair = None
        for addr, sz in ordered_sinks:
            if sz >= length:
                max_pair = (addr, sz)
                break

        if max_pair is None:
            return None

        remaining = max_pair[1] - length
        max_addr = max_pair[0] + remaining
        max_length = remaining

        self.sinkholes.remove(max_pair)

        if remaining:
            self.sinkholes.add((max_pair[0], max_length))

        return max_addr


Instruction:
How would you implement a function in Python 3 that
appends a new state to this VFGNode.

Response:
def append_state(self, s, is_widened_state=False):
        """
        Appended a new state to this VFGNode.
        :param s: The new state to append
        :param is_widened_state: Whether it is a widened state or not.
        """

        if not is_widened_state:
            self.all_states.append(s)
            self.state = s

        else:
            self.widened_state = s


Instruction:
Can you write a function in Python 3 where it
returns the first FunctionAnalysis task in the stack or None if there isn t any.

Response:
def _top_function_analysis_task(self):
        """
        Get the first FunctionAnalysis task in the stack.

        :return: The top function analysis task in the stack, or None if there isn't any.
        :rtype: FunctionAnalysis
        """

        for r in reversed(self._task_stack):
            if isinstance(r, FunctionAnalysis):
                return r
        return None


Instruction:
Given the following Python 3 function, write the documentation
def get_any_node(self, addr):
        for n in self.graph.nodes():
            if n.addr == addr:
                return n

Response: Get any VFG node corresponding to the basic block at addr.


Instruction:
Explain what the following Python 3 code does
def _pre_analysis(self):

        l.debug("Starting from %#x", self._start)

        # initialize the task stack
        self._task_stack = [ ]

        # initialize the execution counter dict
        self._execution_counter = defaultdict(int)

        # Generate a CFG if no CFG is provided
        if not self._cfg:
            l.debug("Generating a CFG, since none was given...")
            # TODO: can we use a fast CFG instead? note that fast CFG does not care of context sensitivity at all, but
            # TODO: for state merging, we also don't really care about context sensitivity.
            self._cfg = self.project.analyses.CFGEmulated(context_sensitivity_level=self._context_sensitivity_level,
                starts=(self._start,)
            )

        if not self._cfg.normalized:
            l.warning("The given CFG is not normalized, which might impact the performance/accuracy of the VFG "
                      "analysis.")

        # Prepare the state
        initial_state = self._prepare_initial_state(self._start, self._initial_state)
        initial_state.ip = self._start

        if self.project.arch.name.startswith('MIPS'):
            initial_state.regs.t9 = self._start

        # clear function merge points cache
        self._function_merge_points = {}

        # Create the initial state
        state = initial_state.copy()

        if self._start_at_function:
            # set the return address to an address so we can catch it and terminate the VSA analysis
            # TODO: Properly pick an address that will not conflict with any existing code and data in the program
            self._final_address = 0x4fff0000
            self._set_return_address(state, self._final_address)

        call_stack = None
        if not self._start_at_function:
            # we should build a custom call stack
            call_stack = CallStack()
            call_stack = call_stack.call(None, self._function_start, retn_target=self._final_address)

        job = VFGJob(state.addr, state, self._context_sensitivity_level,
                     jumpkind='Ijk_Boring', final_return_address=self._final_address,
                     call_stack=call_stack
                     )
        block_id = BlockID.new(state.addr, job.get_call_stack_suffix(), job.jumpkind)
        job._block_id = block_id

        self._insert_job(job)

        # create the task
        function_analysis_task = FunctionAnalysis(self._function_start, self._final_address)
        function_analysis_task.jobs.append(job)
        self._task_stack.append(function_analysis_task)

Response: Called before the analysis starts.


Instruction:
Can you create a Python 3 function that
gets the sorting key of a VFGJob instance.

Response:
def _job_sorting_key(self, job):
        """
        Get the sorting key of a VFGJob instance.

        :param VFGJob job: the VFGJob object.
        :return: An integer that determines the order of this job in the queue.
        :rtype: int
        """

        MAX_BLOCKS_PER_FUNCTION = 1000000

        task_functions = list(reversed(
            list(task.function_address for task in self._task_stack if isinstance(task, FunctionAnalysis))
            ))
        try:
            function_pos = task_functions.index(job.func_addr)
        except ValueError:
            # not in the list
            # it might be because we followed the wrong path, or there is a bug in the traversal algorithm
            # anyways, do it first
            l.warning('Function address %#x is not found in task stack.', job.func_addr)
            return 0

        try:
            block_in_function_pos = self._ordered_node_addrs(job.func_addr).index(job.addr)
        except ValueError:
            # block not found. what?
            block_in_function_pos = min(job.addr - job.func_addr, MAX_BLOCKS_PER_FUNCTION - 1)

        return block_in_function_pos + MAX_BLOCKS_PER_FUNCTION * function_pos


Instruction:
Explain what the following Python 3 code does
def _pre_job_handling(self, job):

        # did we reach the final address?
        if self._final_address is not None and job.addr == self._final_address:
            # our analysis should be termianted here
            l.debug("%s is viewed as a final state. Skip.", job)
            raise AngrSkipJobNotice()

        l.debug("Handling VFGJob %s", job)

        if not self._top_task:
            l.debug("No more tasks available. Skip the job.")
            raise AngrSkipJobNotice()

        assert isinstance(self._top_task, FunctionAnalysis)

        if job not in self._top_task.jobs:
            # it seems that all jobs of the top task has been done. unwind the task stack
            # make sure this job is at least recorded somewhere
            unwind_count = None
            for i, task in enumerate(reversed(self._task_stack)):
                if isinstance(task, FunctionAnalysis):
                    if job in task.jobs:
                        # nice
                        unwind_count = i

            if unwind_count is None:
                l.debug("%s is not recorded. Skip the job.", job)
                raise AngrSkipJobNotice()
            else:
                # unwind the stack till the target, unless we see any pending jobs for each new top task
                for i in range(unwind_count):
                    if isinstance(self._top_task, FunctionAnalysis):
                        # are there any pending job belonging to the current function that we should handle first?
                        pending_job_key = self._get_pending_job(self._top_task.function_address)
                        if pending_job_key is not None:
                            # ah there is
                            # analyze it first
                            self._trace_pending_job(pending_job_key)
                            l.debug("A pending job is found for function %#x. Delay %s.",
                                    self._top_task.function_address, job)
                            raise AngrDelayJobNotice()

                    task = self._task_stack.pop()

                    if not task.done:
                        l.warning("Removing an unfinished task %s. Might be a bug.", task)

                assert job in self._top_task.jobs

        # check if this is considered to be a final state
        if self._final_state_callback is not None and self._final_state_callback(job.state, job.call_stack):
            l.debug("%s.state is considered as a final state. Skip the job.", job)
            self.final_states.append(job.state)
            raise AngrSkipJobNotice()

        # increment the execution counter
        self._execution_counter[job.addr] += 1

        self._top_task.jobs.remove(job)

        # set up some essential variables and parameters
        job.call_stack_suffix = job.get_call_stack_suffix()
        job.jumpkind = 'Ijk_Boring' if job.state.history.jumpkind is None else \
            job.state.history.jumpkind

        src_block_id = job.src_block_id
        src_exit_stmt_idx = job.src_exit_stmt_idx

        addr = job.state.solver.eval(job.state.regs.ip)
        input_state = job.state
        block_id = BlockID.new(addr, job.call_stack_suffix, job.jumpkind)

        if self._tracing_times[block_id] > self._max_iterations:
            l.debug('%s has been traced too many times. Skip', job)
            raise AngrSkipJobNotice()

        self._tracing_times[block_id] += 1

        if block_id not in self._nodes:
            vfg_node = VFGNode(addr, block_id, state=input_state)
            self._nodes[block_id] = vfg_node

        else:
            vfg_node = self._nodes[block_id]

        job.vfg_node = vfg_node
        # log the current state
        vfg_node.state = input_state

        # Execute this basic block with input state, and get a new SimSuccessors instance
        # unused result var is `error_occured`
        job.sim_successors, _, restart_analysis = self._get_simsuccessors(input_state, addr)

        if restart_analysis:
            # We should restart the analysis because of something must be changed in the very initial state
            raise AngrVFGRestartAnalysisNotice()

        if job.sim_successors is None:
            # Ouch, we cannot get the SimSuccessors for some reason
            # Skip this guy
            l.debug('Cannot create SimSuccessors for %s. Skip.', job)
            raise AngrSkipJobNotice()

        self._graph_add_edge(src_block_id,
                             block_id,
                             jumpkind=job.jumpkind,
                             src_exit_stmt_idx=src_exit_stmt_idx
                             )

Response: This function is called before actually processing the job.


Instruction:
Can you generate the documentation for the following Python 3 function
def _handle_successor(self, job, successor, all_successors):

        # Initialize parameters
        addr = job.addr
        jumpkind = successor.history.jumpkind

        #
        # Get instruction pointer
        #

        if job.is_return_jump:
            ret_target = job.call_stack.current_return_target
            if ret_target is None:
                # We have no where to go according to our call stack. However, the callstack might be corrupted
                l.debug("According to the call stack, we have nowhere to return to.")
                return [ ]

            successor.ip = ret_target

        # this try-except block is to handle cases where the instruction pointer is symbolic
        try:
            successor_addrs = successor.solver.eval_upto(successor.ip, 2)
        except SimValueError:
            # TODO: Should fall back to reading targets from CFG
            # It cannot be concretized currently. Maybe we could handle
            # it later, maybe it just cannot be concretized
            return [ ]

        if len(successor_addrs) > 1:
            # multiple concrete targets
            if job.is_return_jump:
                # It might be caused by state merging
                # We may retrieve the correct ip from call stack
                successor.ip = job.call_stack.current_return_target

            else:
                return self._handle_successor_multitargets(job, successor, all_successors)

        # Now there should be one single target for the successor
        successor_addr = successor.solver.eval_one(successor.ip)

        # Get the fake ret successor
        fakeret_successor = None
        if self._is_call_jumpkind(jumpkind):
            fakeret_successor = all_successors[-1]

            # If the function we're calling into doesn't return, we should discard it
            if self._cfg is not None:
                func = self.kb.functions.function(addr=job.call_target)
                if func is not None and func.returning is False and len(all_successors) == 2:
                    del all_successors[-1]
                    fakeret_successor = None

        if self._is_call_jumpkind(jumpkind):
            # Create a new call stack for the successor
            new_call_stack = self._create_callstack(job, successor_addr, jumpkind, fakeret_successor)
            if new_call_stack is None:
                l.debug("Cannot create a new callstack for address %#x", successor_addr)
                job.dbg_exit_status[successor] = ""
                return [ ]
            new_call_stack_suffix = new_call_stack.stack_suffix(self._context_sensitivity_level)

            new_function_key = FunctionKey.new(successor_addr, new_call_stack_suffix)
            # Save the initial state for the function
            self._save_function_initial_state(new_function_key, successor_addr, successor.copy())

            # bail out if we hit the interfunction_level cap
            if len(job.call_stack) >= self._interfunction_level:
                l.debug('We are not tracing into a new function %#08x as we hit interfunction_level limit', successor_addr)

                # mark it as skipped
                job.dbg_exit_status[successor] = "Skipped"

                job.call_skipped = True
                job.call_function_key = new_function_key

                job.call_task.skipped = True

                return [ ]

        elif jumpkind == 'Ijk_Ret':
            # Pop the current function out from the call stack
            new_call_stack = self._create_callstack(job, successor_addr, jumpkind, fakeret_successor)
            if new_call_stack is None:
                l.debug("Cannot create a new callstack for address %#x", successor_addr)
                job.dbg_exit_status[successor] = ""
                return [ ]
            new_call_stack_suffix = new_call_stack.stack_suffix(self._context_sensitivity_level)

        else:
            new_call_stack = job.call_stack
            new_call_stack_suffix = job.call_stack_suffix

        # Generate the new block ID
        new_block_id = BlockID.new(successor_addr, new_call_stack_suffix, jumpkind)

        #
        # Generate new VFG jobs
        #

        if jumpkind == "Ijk_Ret":
            assert not job.is_call_jump

            # Record this return
            self._return_target_sources[successor_addr].append(job.call_stack_suffix + (addr,))

            # Check if this return is inside our pending returns list
            if new_block_id in self._pending_returns:
                del self._pending_returns[new_block_id]

        # Check if we have reached a fix-point
        if jumpkind != 'Ijk_FakeRet' and \
                new_block_id in self._nodes:
            last_state = self._nodes[new_block_id].state

            _, _, merged = last_state.merge(successor, plugin_whitelist=self._mergeable_plugins)

            if merged:
                l.debug("%s didn't reach a fix-point", new_block_id)
            else:
                l.debug("%s reaches a fix-point.", new_block_id)
                job.dbg_exit_status[successor] = "Merged due to reaching a fix-point"
                return [ ]

        new_jobs = self._create_new_jobs(job, successor, new_block_id, new_call_stack)

        return new_jobs

Response: Process each successor generated by the job and return a list of new jobs.


Instruction:
Create a Python 3 function to
generate new jobs for all possible successor targets when there are more than one possible concrete value for successor.ip :param VFGJob job: The VFGJob instance. :param SimState successor: The succeeding state. :param list all_successors: All succeeding states from the same VFGJob. :return: A list of new succeeding jobs :rtype: list

Response:
def _handle_successor_multitargets(self, job, successor, all_successors):
        """
        Generate new jobs for all possible successor targets when there are more than one possible concrete value for
        successor.ip

        :param VFGJob job: The VFGJob instance.
        :param SimState successor: The succeeding state.
        :param list all_successors: All succeeding states from the same VFGJob.
        :return: A list of new succeeding jobs
        :rtype: list
        """

        new_jobs = [ ]

        # Currently we assume a legit jumping target cannot have more than 256 concrete values
        # TODO: make it a setting on VFG
        MAX_NUMBER_OF_CONCRETE_VALUES = 256

        all_possible_ips = successor.solver.eval_upto(successor.ip, MAX_NUMBER_OF_CONCRETE_VALUES + 1)

        if len(all_possible_ips) > MAX_NUMBER_OF_CONCRETE_VALUES:
            l.warning("IP can be concretized to more than %d values, which means it might be corrupted.",
                      MAX_NUMBER_OF_CONCRETE_VALUES)
            return [ ]

        # Call this function to generate a successor for each possible IP
        for ip in all_possible_ips:
            concrete_successor = successor.copy()
            concrete_successor.ip = ip

            concrete_jobs = self._handle_successor(job, concrete_successor, all_successors)

            if job.is_call_jump:  # TODO: take care of syscalls
                for new_job in concrete_jobs:
                    # TODO: correctly fill the return address. The return address can be found from the
                    # TODO: fakeret successor in the `successors` list
                    function_analysis_task = FunctionAnalysis(new_job.addr, None)
                    # log the new job
                    function_analysis_task.jobs.append(new_job)
                    # put it onto the stack
                    self._task_stack.append(function_analysis_task)
                    # log it in the call_task
                    job.call_task.register_function_analysis(function_analysis_task)

            new_jobs.extend(concrete_jobs)

        return new_jobs


Instruction:
Write a Python 3 function that can
merge two given states and return a new one.

Response:
def _merge_states(self, old_state, new_state):
        """
        Merge two given states, and return a new one.

        :param old_state:
        :param new_state:
        :returns: The merged state, and whether a merging has occurred
        """

        # print old_state.dbg_print_stack()
        # print new_state.dbg_print_stack()

        merged_state, _, merging_occurred = old_state.merge(new_state, plugin_whitelist=self._mergeable_plugins)

        # print "Merged: "
        # print merged_state.dbg_print_stack()

        return merged_state, merging_occurred


Instruction:
How would you explain what the following Python 3 function does
def _widen_states(old_state, new_state):

        # print old_state.dbg_print_stack()
        # print new_state.dbg_print_stack()

        l.debug('Widening state at IP %s', old_state.ip)

        widened_state, widening_occurred = old_state.widen(new_state)

        # print "Widened: "
        # print widened_state.dbg_print_stack()

        return widened_state, widening_occurred

Response: Perform widen operation on the given states and return a new state.


Instruction:
Can you generate the documentation for the following Python 3 function
def _narrow_states(node, old_state, new_state, previously_widened_state):  # pylint:disable=unused-argument,no-self-use

        l.debug('Narrowing state at IP %s', previously_widened_state.ip)

        s = previously_widened_state.copy()

        narrowing_occurred = False

        # TODO: Finish the narrowing logic

        return s, narrowing_occurred

Response: Narrow the state of a node.


Instruction:
Write a Python 3 script to
prepare the initial state for the analysis of the function.

Response:
def _prepare_initial_state(self, function_start, state):
        """
        Get the state to start the analysis for function.

        :param int function_start: Address of the function
        :param SimState state: The program state to base on.
        """

        if state is None:
            state = self.project.factory.blank_state(mode="static",
                                                     remove_options=self._state_options_to_remove
                                                     )

        # make room for arguments passed to the function
        sp = state.regs.sp
        sp_val = state.solver.eval_one(sp)
        state.memory.set_stack_address_mapping(sp_val,
                                               state.memory.stack_id(function_start) + '_pre',
                                               0
                                               )
        state.registers.store('sp', sp - 0x100)

        # Set the stack address mapping for the initial stack
        state.memory.set_stack_size(state.arch.stack_size)
        initial_sp = state.solver.eval(state.regs.sp) # FIXME: This is bad, as it may lose tracking of multiple sp values
        initial_sp -= state.arch.bytes
        state.memory.set_stack_address_mapping(initial_sp,
                                               state.memory.stack_id(function_start),
                                               function_start
                                               )

        return state


Instruction:
Here you have a function in Python 3, explain what it does
def _set_return_address(self, state, ret_addr):

        # TODO: the following code is totally untested other than X86 and AMD64. Don't freak out if you find bugs :)
        # TODO: Test it

        ret_bvv = state.solver.BVV(ret_addr, self.project.arch.bits)

        if self.project.arch.name in ('X86', 'AMD64'):
            state.stack_push(ret_bvv)
        elif is_arm_arch(self.project.arch):
            state.regs.lr = ret_bvv
        elif self.project.arch.name in ('MIPS32', 'MIPS64'):
            state.regs.ra = ret_bvv
        elif self.project.arch.name in ('PPC32', 'PPC64'):
            state.regs.lr = ret_bvv
        else:
            l.warning('Return address cannot be set for architecture %s. Please add corresponding logic to '
                      'VFG._set_return_address().', self.project.arch.name
                      )

Response: Set the return address of the current state to a specific address.


Instruction:
Given the following Python 3 function, write the documentation
def _create_graph(self, return_target_sources=None):
        if return_target_sources is None:
            # We set it to a defaultdict in order to be consistent with the
            # actual parameter.
            return_target_sources = defaultdict(list)

        cfg = networkx.DiGraph()
        # The corner case: add a node to the graph if there is only one block
        if len(self._nodes) == 1:
            cfg.add_node(self._nodes[next(iter(self._nodes.keys()))])

        # Adding edges
        for tpl, targets in self._exit_targets.items():
            basic_block = self._nodes[tpl] # Cannot fail :)
            for ex, jumpkind in targets:
                if ex in self._nodes:
                    target_bbl = self._nodes[ex]
                    cfg.add_edge(basic_block, target_bbl, jumpkind=jumpkind)

                    # Add edges for possibly missing returns
                    if basic_block.addr in return_target_sources:
                        for src_irsb_key in \
                                return_target_sources[basic_block.addr]:
                            cfg.add_edge(self._nodes[src_irsb_key],
                                               basic_block, jumpkind="Ijk_Ret")
                else:
                    # Debugging output
                    def addr_formalize(addr):
                        if addr is None:
                            return "None"
                        else:
                            return "%#08x" % addr

                    s = "(["
                    for addr in ex[:-1]:
                        s += addr_formalize(addr) + ", "
                    s += "] %s)" % addr_formalize(ex[-1])
                    l.warning("Key %s does not exist.", s)

        return cfg

Response: Create a DiGraph out of the existing edge map.


Instruction:
Can you create a Python 3 function that
gets an existing VFGNode instance from the graph.

Response:
def _graph_get_node(self, block_id, terminator_for_nonexistent_node=False):
        """
        Get an existing VFGNode instance from the graph.

        :param BlockID block_id:                     The block ID for the node to get.
        :param bool terminator_for_nonexistent_node: True if a Terminator (which is a SimProcedure stub) should be
                                                     created when there is no existing node available for the given
                                                     block ID.
        :return:                                     A node in the graph, or None.
        :rtype:                                      VFGNode
        """

        if block_id not in self._nodes:
            l.error("Trying to look up a node that we don't have yet. Is this okay????")
            if not terminator_for_nonexistent_node:
                return None
            # Generate a PathTerminator node
            addr = block_id.addr
            func_addr = block_id.func_addr
            if func_addr is None:
                # We'll have to use the current block address instead
                # TODO: Is it really OK?
                func_addr = addr

            input_state = self.project.factory.entry_state()
            input_state.ip = addr
            pt = VFGNode(addr, block_id, input_state)
            self._nodes[block_id] = pt

            if isinstance(self.project.arch, archinfo.ArchARM) and addr % 2 == 1:
                self._thumb_addrs.add(addr)
                self._thumb_addrs.add(addr - 1)

            l.debug("Block ID %s does not exist. Create a PathTerminator instead.",
                    repr(block_id))

        return self._nodes[block_id]


Instruction:
Implement a function in Python 3 to
add an edge from one source IRSB to another.

Response:
def _graph_add_edge(self, src_block_id, dst_block_id, **kwargs):
        """
        Add an edge onto the graph.

        :param BlockID src_block_id: The block ID for source node.
        :param BlockID dst_block_id: The block Id for destination node.
        :param str jumpkind:         The jumpkind of the edge.
        :param exit_stmt_idx:        ID of the statement in the source IRSB where this edge is created from. 'default'
                                     refers to the default exit.
        :return: None
        """

        dst_node = self._graph_get_node(dst_block_id, terminator_for_nonexistent_node=True)

        if src_block_id is None:
            self.graph.add_node(dst_node)

        else:
            src_node = self._graph_get_node(src_block_id, terminator_for_nonexistent_node=True)
            self.graph.add_edge(src_node, dst_node, **kwargs)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _create_new_jobs(self, job, successor, new_block_id, new_call_stack):

        # TODO: basic block stack is probably useless

        jumpkind = successor.history.jumpkind
        stmt_idx = successor.scratch.stmt_idx
        ins_addr = successor.scratch.ins_addr
        # Make a copy of the state in case we use it later
        successor_state = successor.copy()
        successor_addr = successor_state.solver.eval(successor_state.ip)

        new_jobs = [ ]

        if jumpkind == "Ijk_FakeRet":
            assert job.is_call_jump

            # This is the default "fake" return successor generated at each call, if and only if the target function
            # returns.

            # if the call is skipped (for whatever reason, like we reached the interfunction tracing limit), we use
            # this FakeRet successor as the final state of the function. Otherwise we save the FakeRet state in case the
            # callee does not return normally, but don't process them right away.

            # Clear the useless values (like return addresses, parameters) on stack if needed
            if self._cfg is not None:
                current_function = self.kb.functions.function(job.call_target)
                if current_function is not None:
                    sp_difference = current_function.sp_delta
                else:
                    sp_difference = 0
                reg_sp_offset = successor_state.arch.sp_offset
                reg_sp_expr = successor_state.registers.load(reg_sp_offset) + sp_difference
                successor_state.registers.store(successor_state.arch.sp_offset, reg_sp_expr)

                # Clear the return value with a TOP
                top_si = successor_state.solver.TSI(successor_state.arch.bits)
                successor_state.registers.store(successor_state.arch.ret_offset, top_si)

            if job.call_skipped:

                # TODO: Make sure the return values make sense
                #if self.project.arch.name == 'X86':
                #    successor_state.regs.eax = successor_state.solver.BVS('ret_val', 32, min=0, max=0xffffffff, stride=1)

                new_job = VFGJob(successor_addr,
                                 successor_state,
                                 self._context_sensitivity_level,
                                 block_id=new_block_id,
                                 jumpkind='Ijk_Ret',
                                 call_stack=new_call_stack,
                                 src_block_id=job.block_id,
                                 src_exit_stmt_idx=stmt_idx,
                                 src_ins_addr=ins_addr,
                                 )

                new_jobs.append(new_job)
                assert isinstance(self._task_stack[-2], FunctionAnalysis)
                self._task_stack[-2].jobs.append(new_job)
                job.dbg_exit_status[successor] = "Pending"

            else:
                self._pending_returns[new_block_id] = PendingJob(new_block_id, successor_state, new_call_stack,
                                                                 job.block_id, stmt_idx, ins_addr)
                job.dbg_exit_status[successor] = "Pending"

        else:
            if sim_options.ABSTRACT_MEMORY in successor.options:
                if self._is_call_jumpkind(successor.history.jumpkind):
                    # If this is a call, we create a new stack address mapping
                    reg_sp_si = self._create_stack_region(successor_state, successor_addr)

                    # Save the new sp register
                    new_reg_sp_expr = successor_state.solver.ValueSet(successor_state.arch.bits,
                                                                       'global',
                                                                       0,
                                                                       reg_sp_si
                                                                       )
                    successor_state.regs.sp = new_reg_sp_expr

                elif successor.history.jumpkind == "Ijk_Ret":
                    # Remove the existing stack address mapping
                    # FIXME: Now we are assuming the sp is restored to its original value
                    reg_sp_expr = successor_state.regs.sp

                    if isinstance(reg_sp_expr._model_vsa, claripy.vsa.StridedInterval):
                        reg_sp_si = reg_sp_expr._model_vsa
                        reg_sp_val = reg_sp_si.min
                    elif isinstance(reg_sp_expr._model_vsa, claripy.vsa.ValueSet):
                        reg_sp_si = next(iter(reg_sp_expr._model_vsa.items()))[1]
                        reg_sp_val = reg_sp_si.min
                        # TODO: Finish it!

            new_job = VFGJob(successor_addr,
                             successor_state,
                             self._context_sensitivity_level,
                             block_id=new_block_id,
                             jumpkind=successor_state.history.jumpkind,
                             call_stack=new_call_stack,
                             src_block_id=job.block_id,
                             src_exit_stmt_idx=stmt_idx,
                             src_ins_addr=ins_addr,
                             )

            if successor.history.jumpkind == 'Ijk_Ret':
                # it's returning to the return site

                # save the state as a final state of the function that we are returning from
                if self._record_function_final_states:
                    # key of the function that we are returning from
                    source_function_key = FunctionKey.new(job.func_addr,
                                                          job.call_stack_suffix
                                                          )
                    self._save_function_final_state(source_function_key, job.func_addr, successor_state)

                # TODO: add an assertion that requires the returning target being the same as the return address we
                # TODO: stored before

                current_task = self._top_task
                if current_task.call_analysis is not None:
                    current_task.call_analysis.add_final_job(new_job)

                    job.dbg_exit_status[successor] = "Appended to the call analysis task"
                else:
                    job.dbg_exit_status[successor] = "Discarded (no call analysis task)"

            else:
                if self._is_call_jumpkind(successor.history.jumpkind):
                    # create a function analysis task
                    # TODO: the return address
                    task = FunctionAnalysis(new_job.addr, None)
                    self._task_stack.append(task)
                    # link it to the call analysis
                    job.call_task.register_function_analysis(task)

                else:
                    task = self._top_task

                # register the job to the function task
                task.jobs.append(new_job)
                # insert the new job into the new job array
                new_jobs.append(new_job)

                job.dbg_exit_status[successor] = "Appended"

        if not job.is_call_jump or jumpkind != "Ijk_FakeRet":
            new_target = (new_block_id, jumpkind)
        else:
            new_target = (new_block_id, "Ijk_FakeRet")  # This is the fake return!
        self._exit_targets[job.call_stack_suffix + (job.addr,)].append(new_target)

        return new_jobs

Response: Create a list of new VFG jobs for the successor state.


Instruction:
Can you create a Python 3 function that
removes all pending returns that are related to the current job.

Response:
def _remove_pending_return(self, job, pending_returns):
        """
        Remove all pending returns that are related to the current job.
        """

        # Build the tuples that we want to remove from the dict fake_func_retn_exits
        tpls_to_remove = [ ]
        call_stack_copy = job.call_stack_copy()
        while call_stack_copy.current_return_target is not None:
            ret_target = call_stack_copy.current_return_target
            # Remove the current call stack frame
            call_stack_copy = call_stack_copy.ret(ret_target)
            call_stack_suffix = call_stack_copy.stack_suffix(self._context_sensitivity_level)
            tpl = call_stack_suffix + (ret_target,)
            tpls_to_remove.append(tpl)

        # Remove those tuples from the dict
        for tpl in tpls_to_remove:
            if tpl in pending_returns:
                del pending_returns[tpl]
                l.debug("Removed (%s) from FakeExits dict.",
                        ",".join([hex(i) if i is not None else 'None' for i in tpl]))


Instruction:
Can you create a Python 3 function that
prints out debugging information after handling a VFGJob and generating the succeeding jobs.

Response:
def _post_job_handling_debug(self, job, successors):
        """
        Print out debugging information after handling a VFGJob and generating the succeeding jobs.

        :param VFGJob job: The VFGJob instance.
        :param list successors: A list of succeeding states.
        :return: None
        """

        func = self.project.loader.find_symbol(job.addr)
        function_name = func.name if func is not None else None
        module_name = self.project.loader.find_object_containing(job.addr).provides

        l.debug("VFGJob @ %#08x with callstack [ %s ]", job.addr,
                job.callstack_repr(self.kb),
                )
        l.debug("(Function %s of %s)", function_name, module_name)
        l.debug("-  is call jump: %s", job.is_call_jump)
        for suc in successors:
            if suc not in job.dbg_exit_status:
                l.warning("- %s is not found. FIND OUT WHY.", suc)
                continue

            try:
                l.debug("-  successor: %#08x of %s [%s]", suc.solver.eval_one(suc.ip),
                        suc.history.jumpkind, job.dbg_exit_status[suc])
            except SimValueError:
                l.debug("-  target cannot be concretized. %s [%s]", job.dbg_exit_status[suc], suc.history.jumpkind)
        l.debug("Remaining/pending jobs: %d/%d", len(self._job_info_queue), len(self._pending_returns))
        l.debug("Remaining jobs: %s", [ "%s %d" % (ent.job, id(ent.job)) for ent in self._job_info_queue])
        l.debug("Task stack: %s", self._task_stack)


Instruction:
Here you have a function in Python 3, explain what it does
def _save_function_initial_state(self, function_key, function_address, state):

        l.debug('Saving the initial state for function %#08x with function key %s',
                function_address,
                function_key
                )
        if function_key in self._function_initial_states[function_address]:
            existing_state = self._function_initial_states[function_address][function_key]
            merged_state, _, _ = existing_state.merge(state)
            self._function_initial_states[function_address][function_key] = merged_state

        else:
            self._function_initial_states[function_address][function_key] = state

Response: Save the initial state of a function.


Instruction:
Write a Python 3 script to
save the final state of a function.

Response:
def _save_function_final_state(self, function_key, function_address, state):
        """
        Save the final state of a function, and merge it with existing ones if there are any.

        :param FunctionKey function_key: The key to this function.
        :param int function_address: Address of the function.
        :param SimState state: Initial state of the function.
        :return: None
        """

        l.debug('Saving the final state for function %#08x with function key %s',
                function_address,
                function_key
                )

        if function_key in self._function_final_states[function_address]:
            existing_state = self._function_final_states[function_address][function_key]
            merged_state = existing_state.merge(state, plugin_whitelist=self._mergeable_plugins)[0]
            self._function_final_states[function_address][function_key] = merged_state

        else:
            self._function_final_states[function_address][function_key] = state


Instruction:
Write a Python 3 script to
get the possible simple paths between two addresses or node instances.

Response:
def _get_nx_paths(self, begin, end):
        """
        Get the possible (networkx) simple paths between two nodes or addresses
        corresponding to nodes.
        Input: addresses or node instances
        Return: a list of lists of nodes representing paths.
        """
        if type(begin) is int and type(end) is int:  # pylint:disable=unidiomatic-typecheck
            n_begin = self.get_any_node(begin)
            n_end = self.get_any_node(end)

        elif isinstance(begin, VFGNode) and isinstance(end, VFGNode):  # pylint:disable=unidiomatic-typecheck
            n_begin = begin
            n_end = end
        else:
            raise AngrVFGError("from and to should be of the same type")

        return networkx.all_simple_paths(self.graph, n_begin, n_end)


Instruction:
Create a Python 3 function for
returning the ordered merge points for a specific function.

Response:
def _merge_points(self, function_address):
        """
        Return the ordered merge points for a specific function.

        :param int function_address: Address of the querying function.
        :return: A list of sorted merge points (addresses).
        :rtype: list
        """

        # we are entering a new function. now it's time to figure out how to optimally traverse the control flow
        # graph by generating the sorted merge points
        try:
            new_function = self.kb.functions[function_address]
        except KeyError:
            # the function does not exist
            return [ ]

        if function_address not in self._function_merge_points:
            ordered_merge_points = CFGUtils.find_merge_points(function_address, new_function.endpoints,
                                                           new_function.graph)
            self._function_merge_points[function_address] = ordered_merge_points

        return self._function_merge_points[function_address]


Instruction:
Implement a Python 3 function for
returning the ordered widening points for a specific function.

Response:
def _widening_points(self, function_address):
        """
        Return the ordered widening points for a specific function.

        :param int function_address: Address of the querying function.
        :return: A list of sorted merge points (addresses).
        :rtype: list
        """

        # we are entering a new function. now it's time to figure out how to optimally traverse the control flow
        # graph by generating the sorted merge points
        try:
            new_function = self.kb.functions[function_address]
        except KeyError:
            # the function does not exist
            return [ ]

        if function_address not in self._function_widening_points:
            if not new_function.normalized:
                new_function.normalize()
            widening_points = CFGUtils.find_widening_points(function_address, new_function.endpoints,
                                                                    new_function.graph)
            self._function_widening_points[function_address] = widening_points

        return self._function_widening_points[function_address]


Instruction:
Can you tell what is the following Python 3 function doing
def _ordered_node_addrs(self, function_address):

        try:
            function = self.kb.functions[function_address]
        except KeyError:
            # the function does not exist
            return [ ]

        if function_address not in self._function_node_addrs:
            sorted_nodes = CFGUtils.quasi_topological_sort_nodes(function.graph)
            self._function_node_addrs[function_address] = [ n.addr for n in sorted_nodes ]

        return self._function_node_addrs[function_address]

Response: Returns a list of all nodes in an optimal traversal order.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def assign(self, dst_addr_ast):

        if dst_addr_ast.uc_alloc_depth > self._max_alloc_depth:
            raise SimUCManagerAllocationError('Current allocation depth %d is greater than the cap (%d)' % \
                (dst_addr_ast.uc_alloc_depth, self._max_alloc_depth))

        abs_addr = self._region_base + self._pos
        ptr = self.state.solver.BVV(abs_addr, self.state.arch.bits)
        self._pos += self._region_size

        self._alloc_depth_map[(abs_addr - self._region_base) // self._region_size] = dst_addr_ast.uc_alloc_depth

        l.debug("Assigned new memory region %s", ptr)
        return ptr

Response: Assign a new region for under - constrained symbolic execution.


Instruction:
Given the following Python 3 function, write the documentation
def is_bounded(self, ast):

        return len(ast.variables.intersection(self.state.solver._solver.variables)) != 0

Response: Test whether an AST is bounded by any existing constraint in the related solver.


Instruction:
Can you generate the documentation for the following Python 3 function
def resolve(self, cfg, addr, func_addr, block, jumpkind):

        project = self.project

        b = Blade(cfg.graph, addr, -1, cfg=cfg, project=project, ignore_sp=True, ignore_bp=True,
                  ignored_regs=('gp',)
                  )

        sources = [n for n in b.slice.nodes() if b.slice.in_degree(n) == 0]
        if not sources:
            return False, []

        source = sources[0]
        source_addr = source[0]
        annotated_cfg = AnnotatedCFG(project, None, detect_loops=False)
        annotated_cfg.from_digraph(b.slice)

        state = project.factory.blank_state(addr=source_addr, mode="fastpath",
                                            remove_options=options.refs
                                            )
        func = cfg.kb.functions.function(addr=func_addr)

        gp_offset = project.arch.registers['gp'][0]
        if 'gp' not in func.info:
            sec = project.loader.find_section_containing(func.addr)
            if sec is None or sec.name != '.plt':
                # this might a special case: gp is only used once in this function, and it can be initialized right before
                # its use site.
                # TODO: handle this case
                l.debug('Failed to determine value of register gp for function %#x.', func.addr)
                return False, [ ]
        else:
            state.regs.gp = func.info['gp']

        def overwrite_tmp_value(state):
            state.inspect.tmp_write_expr = state.solver.BVV(func.info['gp'], state.arch.bits)

        # Special handling for cases where `gp` is stored on the stack
        got_gp_stack_store = False
        for block_addr_in_slice in set(slice_node[0] for slice_node in b.slice.nodes()):
            for stmt in project.factory.block(block_addr_in_slice).vex.statements:
                if isinstance(stmt, pyvex.IRStmt.Put) and stmt.offset == gp_offset and \
                        isinstance(stmt.data, pyvex.IRExpr.RdTmp):
                    tmp_offset = stmt.data.tmp  # pylint:disable=cell-var-from-loop
                    # we must make sure value of that temporary variable equals to the correct gp value
                    state.inspect.make_breakpoint('tmp_write', when=BP_BEFORE,
                                                  condition=lambda s, bbl_addr_=block_addr_in_slice,
                                                                   tmp_offset_=tmp_offset:
                                                  s.scratch.bbl_addr == bbl_addr_ and s.inspect.tmp_write_num == tmp_offset_,
                                                  action=overwrite_tmp_value
                                                  )
                    got_gp_stack_store = True
                    break
            if got_gp_stack_store:
                break

        simgr = self.project.factory.simulation_manager(state)
        simgr.use_technique(Slicecutor(annotated_cfg))
        simgr.run()

        if simgr.cut:
            target = simgr.cut[0].addr

            if self._is_target_valid(cfg, target):
                l.debug("Indirect jump at %#x is resolved to target %#x.", addr, target)
                return True, [ target ]

            l.debug("Indirect jump at %#x is resolved to target %#x, which seems to be invalid.", addr, target)
            return False, [ ]

        l.debug("Indirect jump at %#x cannot be resolved by %s.", addr, repr(self))
        return False, [ ]

Response: Resolves the indirect jump in MIPS ELF binaries where all external function calls are indexed using gp.


Instruction:
Implement a Python 3 function for
executing a procedure and returns a new state.

Response:
def process(self, state, procedure=None, force_addr=None, **kwargs):
        """
        Perform execution with a state.

        :param state:       The state with which to execute
        :param procedure:   An instance of a SimProcedure to run, optional
        :param ret_to:      The address to return to when this procedure is finished
        :param inline:      This is an inline execution. Do not bother copying the state.
        :param force_addr:  Force execution to pretend that we're working at this concrete address
        :returns:           A SimSuccessors object categorizing the execution's successor states
        """
        addr = state.addr if force_addr is None else force_addr

        if procedure is None:
            if addr not in self.project._sim_procedures:
                if state.arch.name.startswith('ARM') and addr & 1 == 1 and addr - 1 in self.project._sim_procedures:
                    procedure = self.project._sim_procedures[addr - 1]
                else:
                    return SimSuccessors.failure()
            else:
                procedure = self.project._sim_procedures[addr]

        if isinstance(addr, SootAddressDescriptor):
            l.debug("Running %s (originally at %r)", repr(procedure), addr)
        else:
            l.debug("Running %s (originally at %#x)", repr(procedure), addr)
        return self.project.factory.procedure_engine.process(state, procedure, force_addr=force_addr, **kwargs)


Instruction:
Can you write a function in Python 3 where it
returns a new instance with only the options that are in boolean_switches.

Response:
def difference(self, boolean_switches):
        """
        [COMPATIBILITY]
        Make a copy of the current instance, and then discard all options that are in boolean_switches.

        :param set boolean_switches:    A collection of Boolean switches to disable.
        :return:                        A new SimStateOptions instance.
        """

        ops = SimStateOptions(self)
        for key in boolean_switches:
            ops.discard(key)
        return ops


Instruction:
Create a Python 3 function for
returning a string representation of all state options.

Response:
def tally(self, exclude_false=True, description=False):
        """
        Return a string representation of all state options.

        :param bool exclude_false:  Whether to exclude Boolean switches that are disabled.
        :param bool description:    Whether to display the description of each option.
        :return:                    A string representation.
        :rtype:                     str
        """

        total = [ ]

        for o in sorted(self.OPTIONS.values(), key=lambda x: x.name):
            try:
                value = self[o.name]
            except SimStateOptionsError:
                value = "<Unset>"

            if exclude_false and o.one_type() is bool and value is False:
                # Skip Boolean switches that are False
                continue

            s = "{option}: {value}".format(option=o.name, value=value)
            if description:
                s += " | {description}".format(description=o.description)

            total.append(s)

        return "\n".join(total)


Instruction:
Create a Python 3 function for
registering a state option.

Response:
def register_option(cls, name, types, default=None, description=None):
        """
        Register a state option.

        :param str name:        Name of the state option.
        :param types:           A collection of allowed types of this state option.
        :param default:         The default value of this state option.
        :param str description: The description of this state option.
        :return:                None
        """

        if name in cls.OPTIONS:
            raise SimStateOptionsError("A state option with the same name has been registered.")

        if isinstance(types, type):
            types = { types }

        o = StateOption(name, types, default=default, description=description)
        cls.OPTIONS[name] = o


Instruction:
Implement a Python 3 function for
registering a Boolean switch as state option.

Response:
def register_bool_option(cls, name, description=None):
        """
        Register a Boolean switch as state option.
        This is equivalent to cls.register_option(name, set([bool]), description=description)

        :param str name:        Name of the state option.
        :param str description: The description of this state option.
        :return:                None
        """

        cls.register_option(name, { bool }, default=False, description=description)


Instruction:
Implement a function in Python 3 to
return a 5 - tuple of strings sufficient for formatting with the procedure

Response:
def _describe_me(self):
        """
        return a 5-tuple of strings sufficient for formatting with ``%s%s%s%s%s`` to verbosely describe the procedure
        """
        return (
            self.display_name,
            ' (cont: %s)' % self.run_func if self.is_continuation else '',
            ' (syscall)' if self.is_syscall else '',
            ' (inline)' if not self.use_state_arguments else '',
            ' (stub)' if self.is_stub else '',
        )