File size: 4,892 Bytes
421645e 7e565cb 421645e |
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 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
from .parser import DFG_python,DFG_java,DFG_ruby,DFG_go,DFG_php,DFG_javascript,DFG_csharp
from .parser import (remove_comments_and_docstrings,
tree_to_token_index,
index_to_code_token,
tree_to_variable_index)
from tree_sitter import Language, Parser
import pdb
dfg_function={
'python':DFG_python,
'java':DFG_java,
'ruby':DFG_ruby,
'go':DFG_go,
'php':DFG_php,
'javascript':DFG_javascript,
'c_sharp':DFG_csharp,
}
def calc_dataflow_match(references, candidate, lang):
return corpus_dataflow_match([references], [candidate], lang)
def corpus_dataflow_match(references, candidates, lang):
LANGUAGE = Language(os.path.abspath(os.path.dirname(__file__)) + '/parser/my-languages.so', lang)
parser = Parser()
parser.set_language(LANGUAGE)
parser = [parser,dfg_function[lang]]
match_count = 0
total_count = 0
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)
for dataflow in normalized_ref_dfg:
if dataflow in normalized_cand_dfg:
match_count += 1
normalized_cand_dfg.remove(dataflow)
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
|