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768d31a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import numpy as np
import os
from tree_sitter import Language, Parser
from src.evaluator.CodeBLEU.parser import DFG_python, DFG_java, DFG_ruby, DFG_go, DFG_php, DFG_javascript, DFG_csharp
from src.evaluator.CodeBLEU.parser import (remove_comments_and_docstrings,
tree_to_token_index,
index_to_code_token)
root_dir = os.path.dirname(__file__)
dfg_function = {
'c': DFG_java,
'python': DFG_python,
'java': DFG_java,
'ruby': DFG_ruby,
'go': DFG_go,
'php': DFG_php,
'javascript': DFG_javascript,
'c_sharp': DFG_csharp,
}
def my_dataflow_match(references, candidates, lang):
LANGUAGE = Language(root_dir + '/parser/languages.so', lang)
parser = Parser()
parser.set_language(LANGUAGE)
parser = [parser, dfg_function[lang]]
match_count = 0
total_count = 0
candidate_scores = []
for i in range(len(candidates)):
scores = []
references_sample = references[i]
candidate = candidates[i]
for reference in references_sample:
try:
candidate = remove_comments_and_docstrings(candidate, 'java')
except:
pass
try:
reference = remove_comments_and_docstrings(reference, 'java')
except:
pass
cand_dfg = get_data_flow(candidate, parser)
ref_dfg = get_data_flow(reference, parser)
normalized_cand_dfg = normalize_dataflow(cand_dfg)
normalized_ref_dfg = normalize_dataflow(ref_dfg)
if len(normalized_ref_dfg) > 0:
total_count += len(normalized_ref_dfg)
current_match_count = 0
for dataflow in normalized_ref_dfg:
if dataflow in normalized_cand_dfg:
match_count += 1
normalized_cand_dfg.remove(dataflow)
current_match_count += 1
scores.append(float(current_match_count) / len(normalized_ref_dfg))
else:
scores.append(0.)
candidate_scores.append(max(scores) if len(scores) > 0 else 0.)
return np.mean(candidate_scores) if len(candidates) > 0 else 0.
def calc_dataflow_match(references, candidate, lang):
return corpus_dataflow_match([references], [candidate], lang)
def corpus_dataflow_match(references, candidates, lang):
LANGUAGE = Language(root_dir + '/parser/languages.so', lang)
parser = Parser()
parser.set_language(LANGUAGE)
parser = [parser, dfg_function[lang]]
match_count = 0
total_count = 0
scores = []
for i in range(len(candidates)):
references_sample = references[i]
candidate = candidates[i]
for reference in references_sample:
try:
candidate = remove_comments_and_docstrings(candidate, 'java')
except:
pass
try:
reference = remove_comments_and_docstrings(reference, 'java')
except:
pass
cand_dfg = get_data_flow(candidate, parser)
ref_dfg = get_data_flow(reference, parser)
normalized_cand_dfg = normalize_dataflow(cand_dfg)
normalized_ref_dfg = normalize_dataflow(ref_dfg)
if len(normalized_ref_dfg) > 0:
total_count += len(normalized_ref_dfg)
current_match_count = 0
for dataflow in normalized_ref_dfg:
if dataflow in normalized_cand_dfg:
match_count += 1
normalized_cand_dfg.remove(dataflow)
current_match_count += 1
scores.append(float(current_match_count) / len(normalized_ref_dfg))
else:
scores.append(0.)
if total_count == 0:
print(
"WARNING: There is no reference data-flows extracted from the whole corpus, "
"and the data-flow match score degenerates to 0. Please consider ignoring this score."
)
return 0
score = match_count / total_count
return score
def get_data_flow(code, parser):
try:
tree = parser[0].parse(bytes(code, 'utf8'))
root_node = tree.root_node
tokens_index = tree_to_token_index(root_node)
code = code.split('\n')
code_tokens = [index_to_code_token(x, code) for x in tokens_index]
index_to_code = {}
for idx, (index, code) in enumerate(zip(tokens_index, code_tokens)):
index_to_code[index] = (idx, code)
try:
DFG, _ = parser[1](root_node, index_to_code, {})
except:
DFG = []
DFG = sorted(DFG, key=lambda x: x[1])
indexs = set()
for d in DFG:
if len(d[-1]) != 0:
indexs.add(d[1])
for x in d[-1]:
indexs.add(x)
new_DFG = []
for d in DFG:
if d[1] in indexs:
new_DFG.append(d)
codes = code_tokens
dfg = new_DFG
except:
codes = code.split()
dfg = []
# merge nodes
dic = {}
for d in dfg:
if d[1] not in dic:
dic[d[1]] = d
else:
dic[d[1]] = (d[0], d[1], d[2], list(set(dic[d[1]][3] + d[3])), list(set(dic[d[1]][4] + d[4])))
DFG = []
for d in dic:
DFG.append(dic[d])
dfg = DFG
return dfg
def normalize_dataflow_item(dataflow_item):
var_name = dataflow_item[0]
var_pos = dataflow_item[1]
relationship = dataflow_item[2]
par_vars_name_list = dataflow_item[3]
par_vars_pos_list = dataflow_item[4]
var_names = list(set(par_vars_name_list + [var_name]))
norm_names = {}
for i in range(len(var_names)):
norm_names[var_names[i]] = 'var_' + str(i)
norm_var_name = norm_names[var_name]
relationship = dataflow_item[2]
norm_par_vars_name_list = [norm_names[x] for x in par_vars_name_list]
return (norm_var_name, relationship, norm_par_vars_name_list)
def normalize_dataflow(dataflow):
var_dict = {}
i = 0
normalized_dataflow = []
for item in dataflow:
var_name = item[0]
relationship = item[2]
par_vars_name_list = item[3]
for name in par_vars_name_list:
if name not in var_dict:
var_dict[name] = 'var_' + str(i)
i += 1
if var_name not in var_dict:
var_dict[var_name] = 'var_' + str(i)
i += 1
normalized_dataflow.append((var_dict[var_name], relationship, [var_dict[x] for x in par_vars_name_list]))
return normalized_dataflow
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