def request_handler(request):
"""
Handles a Request for descriptions.
"""
print "REDB: request_handler called"
unpickled_request = redb_server_com.Request()
unpickled_request.from_json(request.FILES['pickled_request'].read())
# Adding more graph attributes:
unpickled_request.matching_grade_attributes = \
_process_matching_grade_attrs(unpickled_request.\
matching_grade_attributes)
# Building a temporary function
second_function = Function()
second_function.primary_attributes = \
json.dumps(unpickled_request.primary_attributes)
second_function.filtering_attributes = \
json.dumps(unpickled_request.filtering_attributes)
second_function.matching_grade_attributes = \
json.dumps(unpickled_request.matching_grade_attributes)
second_function.func_md5 = \
unpickled_request.primary_attributes["_func_md5_attr"]
second_function.exe_md5 = \
unpickled_request.primary_attributes["_exe_md5_attr"]
second_function.exe_name = \
unpickled_request.primary_attributes["_exe_name_attr"]
second_function.first_addr = \
unpickled_request.primary_attributes["_first_addr_attr"]
second_function.ins_num = \
unpickled_request.primary_attributes["_ins_num_attr"]
num_req_descs = unpickled_request.num_of_returned_comments
if num_req_descs <= 0 or num_req_descs > MAX_REQ_CMTS:
return _error_http_response("Error: required descriptions" +
"number not in range")
filtered_functions = []
for function_obj in Function.objects.all():
filter_grade = redb_similarity_grading.similarity_grading().\
filter_grade(function_obj, second_function)
if filter_grade >= redb_similarity_grading.FILTERING_THRESHOLD:
filtered_functions.append(function_obj)
function_grade_pairs = []
for filtered_function in filtered_functions:
matching_grade = redb_similarity_grading.similarity_grading().\
matching_grade(filtered_function, second_function)
if matching_grade >= redb_similarity_grading.MATCHING_THRESHOLD:
pair = (filtered_function, matching_grade)
function_grade_pairs.append(pair)
sorted_functions = sorted(function_grade_pairs, key=lambda func: func[1])
sorted_functions.reverse()
suggested_descriptions = []
for func, grade in sorted_functions:
if len(suggested_descriptions) == num_req_descs:
break
fitting_descriptions = Description.objects.filter(function=func)
for desc in fitting_descriptions:
func_name_and_cmts = json.loads(desc.func_name_and_cmts,
object_hook=_decode_dict)
suggested_description = \
SuggestedDecsription(func_name_and_cmts=func_name_and_cmts,
matching_grade=grade,
can_be_embedded=(func.ins_num ==
second_function.ins_num),
date=desc.date)
suggested_description_dict = suggested_description.to_dict()
suggested_descriptions.append(suggested_description_dict)
if len(suggested_descriptions) == num_req_descs:
break
response = redb_server_com.Response(\
suggested_descriptions_list=suggested_descriptions)
response = response.to_json()
http_response = HttpResponse(response)
print "REDB: request_handler finished"
return http_response
def request_handler(request):
"""
Handles a Request for descriptions.
"""
print "REDB: request_handler called"
unpickled_request = redb_server_com.Request()
unpickled_request.from_json(request.FILES['pickled_request'].read())
# Adding more graph attributes:
unpickled_request.matching_grade_attributes = \
_process_matching_grade_attrs(unpickled_request.\
matching_grade_attributes)
# Building a temporary function
second_function = Function()
second_function.primary_attributes = \
json.dumps(unpickled_request.primary_attributes)
second_function.filtering_attributes = \
json.dumps(unpickled_request.filtering_attributes)
second_function.matching_grade_attributes = \
json.dumps(unpickled_request.matching_grade_attributes)
second_function.func_md5 = \
unpickled_request.primary_attributes["_func_md5_attr"]
second_function.exe_md5 = \
unpickled_request.primary_attributes["_exe_md5_attr"]
second_function.exe_name = \
unpickled_request.primary_attributes["_exe_name_attr"]
second_function.first_addr = \
unpickled_request.primary_attributes["_first_addr_attr"]
second_function.ins_num = \
unpickled_request.primary_attributes["_ins_num_attr"]
num_req_descs = unpickled_request.num_of_returned_comments
if num_req_descs <= 0 or num_req_descs > MAX_REQ_CMTS:
return _error_http_response("Error: required descriptions" +
"number not in range")
filtered_functions = []
for function_obj in Function.objects.all():
filter_grade = redb_similarity_grading.similarity_grading().\
filter_grade(function_obj, second_function)
if filter_grade >= redb_similarity_grading.FILTERING_THRESHOLD:
filtered_functions.append(function_obj)
function_grade_pairs = []
for filtered_function in filtered_functions:
matching_grade = redb_similarity_grading.similarity_grading().\
matching_grade(filtered_function, second_function)
if matching_grade >= redb_similarity_grading.MATCHING_THRESHOLD:
pair = (filtered_function, matching_grade)
function_grade_pairs.append(pair)
sorted_functions = sorted(function_grade_pairs, key=lambda func: func[1])
sorted_functions.reverse()
suggested_descriptions = []
for func, grade in sorted_functions:
if len(suggested_descriptions) == num_req_descs:
break
fitting_descriptions = Description.objects.filter(function=func)
for desc in fitting_descriptions:
func_name_and_cmts = json.loads(desc.func_name_and_cmts,
object_hook=_decode_dict)
suggested_description = \
SuggestedDecsription(func_name_and_cmts=func_name_and_cmts,
matching_grade=grade,
can_be_embedded=(func.ins_num ==
second_function.ins_num),
date=desc.date)
suggested_description_dict = suggested_description.to_dict()
suggested_descriptions.append(suggested_description_dict)
if len(suggested_descriptions) == num_req_descs:
break
response = redb_server_com.Response(\
suggested_descriptions_list=suggested_descriptions)
response = response.to_json()
http_response = HttpResponse(response)
print "REDB: request_handler finished"
return http_response
