import subprocess

import tempfile

from time import time

import redis

import uuid

import json

class Extractor:

def __init__(self, config, jar_path, max_path_length, max_path_width):

self.config = config

self.max_path_length = max_path_length

self.max_path_width = max_path_width

self.jar_path = jar_path

self.publish_r = redis.Redis(host='localhost', port=6379, db=0)

self.p = self.publish_r.pubsub()

def extract_paths_with_redis(self, code_string, file_name_str):

hash_to_string_dict = {}

result = []

req_uuid = str(uuid.uuid1())

try:

request = {'uuid': req_uuid, 'code': code_string}

self.publish_r.publish('requests', json.dumps(request))

self.p.subscribe(req_uuid)

while True:

resp = self.p.get_message()

if resp:

type_msg = resp['type']

if type_msg.strip() == 'message':

message = resp['data'].decode()

break

else:

continue

else:

continue

output = message.splitlines()

if len(output) == 0:

err = err.decode()

raise ValueError(err)

for i, line in enumerate(output):

parts = line.rstrip().split(' ')

method_name = parts[0]

current_result_line_parts = [method_name]

contexts = parts[1:]

for context in contexts[:self.config.MAX_CONTEXTS]:

context_parts = context.split(',')

context_word1 = context_parts[0]

context_path = context_parts[1]

context_word2 = context_parts[2]

hashed_path = str(self.java_string_hashcode(context_path))

hash_to_string_dict[hashed_path] = context_path

current_result_line_parts += ['%s,%s,%s' % (context_word1, hashed_path, context_word2)]

space_padding = ' ' * (self.config.MAX_CONTEXTS - len(contexts))

result_line = ' '.join(current_result_line_parts) + space_padding

result.append(result_line)

finally:

return result, hash_to_string_dict

def extract_paths(self, code_string, file_name_str):

tmp = tempfile.NamedTemporaryFile(delete=False)

out = None

java_start_time = time()

try:

tmp.write(bytes(code_string, encoding='utf-8'))

tmp.close()

command = ['java', '-cp', self.jar_path, 'JavaExtractor.App', '--max_path_length',

str(self.max_path_length), '--max_path_width', str(self.max_path_width), '--file', tmp.name, '--no_hash']

command_str = ' '.join(command)

print(f'Running {command_str}')

process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

out, err = process.communicate()

output = out.decode().splitlines()

if len(output) == 0:

err = err.decode()

raise ValueError(err)

hash_to_string_dict = {}

result = []

for i, line in enumerate(output):

parts = line.rstrip().split(' ')

method_name = parts[0]

current_result_line_parts = [method_name]

contexts = parts[1:]

for context in contexts[:self.config.MAX_CONTEXTS]:

context_parts = context.split(',')

context_word1 = context_parts[0]

context_path = context_parts[1]

context_word2 = context_parts[2]

hashed_path = str(self.java_string_hashcode(context_path))

hash_to_string_dict[hashed_path] = context_path

current_result_line_parts += ['%s,%s,%s' % (context_word1, hashed_path, context_word2)]

space_padding = ' ' * (self.config.MAX_CONTEXTS - len(contexts))

result_line = ' '.join(current_result_line_parts) + space_padding

result.append(result_line)

finally:

return result, hash_to_string_dict

@staticmethod

def java_string_hashcode(s):

"""

Imitating Java's String#hashCode, because the model is trained on hashed paths but we wish to

Present the path attention on un-hashed paths.

"""

h = 0

for c in s:

h = (31 * h + ord(c)) & 0xFFFFFFFF

return ((h + 0x80000000) & 0xFFFFFFFF) - 0x80000000