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import subprocess
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
def extract_paths(self, path):
#command = ['java', '-jar', self.jar_path, 'code2vec', '--lang', 'py', '--project', 'pred_files', '--output', 'cd2vec' , '--maxH', str(self.max_path_length), '--maxW', str(self.max_path_width)]
output_file = open('cd2vec/path_contexts_0.csv', 'r')
#process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
#out, err = process.communicate()
#output = out.decode().splitlines()
#if len(output_file) == 0:
# err = err.decode()
# raise ValueError(err)
hash_to_string_dict = {}
result = []
for i, line in enumerate(output_file):
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)
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