def estimate(args):
input_model_file_name = args.input_model_file_name
input_exe_file_name = args.input_feature_file_name
extraction_method = args.extraction_method
label_type = args.label_type
# generate an .asm file from the executable file
generator = IDAAsmGenerator()
generator.generate(input_exe_file_name)
# append the information of the input file to the database
datproc = DataProcessor()
datproc.update_database_from_file()
# extract feature vector
ams_file_name = os.path.join(os.path.splittext(input_file_name)[0], '.asm')
datproc.extract_data_from_file(asm_file_name, extraction_method,
label_type)
# load classification model
estimator = CompilerEstimator()
estimator.load_model(input_model_file_name)
# estimate
result = estimator.estimate(feature_vector)
print result
def updatedb(args):
file_name = args.file_name
dir_name = args.dir_name
datproc = DataProcessor()
if file_name is not None and dir_name is not None:
sys.stderr.write('Error: please assign only one file name or directory name')
elif file_name is not None:
datproc.update_database_from_file(file_name)
elif dir_name is not None:
datproc.update_database_from_dir(dir_name)
else:
sys.stderr.write('Error: no file name or directory name specified')
