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| import os
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| from .parser import DFG_python,DFG_java,DFG_ruby,DFG_go,DFG_php,DFG_javascript,DFG_csharp
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| from .parser import (remove_comments_and_docstrings,
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| tree_to_token_index,
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| index_to_code_token,
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| tree_to_variable_index)
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| from tree_sitter import Language, Parser
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| import pdb
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| dfg_function={
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| 'python':DFG_python,
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| 'java':DFG_java,
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| 'ruby':DFG_ruby,
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| 'go':DFG_go,
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| 'php':DFG_php,
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| 'javascript':DFG_javascript,
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| 'c_sharp':DFG_csharp,
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| }
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| def calc_dataflow_match(references, candidate, lang):
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| return corpus_dataflow_match([references], [candidate], lang)
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| def corpus_dataflow_match(references, candidates, lang):
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| LANGUAGE = Language(os.path.abspath(os.path.dirname(__file__)) + '/parser/my-languages.so', lang)
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| parser = Parser()
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| parser.set_language(LANGUAGE)
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| parser = [parser,dfg_function[lang]]
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| match_count = 0
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| total_count = 0
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| for i in range(len(candidates)):
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| references_sample = references[i]
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| candidate = candidates[i]
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| for reference in references_sample:
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| try:
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| candidate=remove_comments_and_docstrings(candidate,'java')
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| except:
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| pass
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| try:
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| reference=remove_comments_and_docstrings(reference,'java')
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| except:
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| pass
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| cand_dfg = get_data_flow(candidate, parser)
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| ref_dfg = get_data_flow(reference, parser)
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| normalized_cand_dfg = normalize_dataflow(cand_dfg)
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| normalized_ref_dfg = normalize_dataflow(ref_dfg)
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| if len(normalized_ref_dfg) > 0:
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| total_count += len(normalized_ref_dfg)
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| for dataflow in normalized_ref_dfg:
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| if dataflow in normalized_cand_dfg:
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| match_count += 1
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| normalized_cand_dfg.remove(dataflow)
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| if total_count == 0:
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| 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.")
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| return 0
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| score = match_count / total_count
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| return score
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| def get_data_flow(code, parser):
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| try:
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| tree = parser[0].parse(bytes(code,'utf8'))
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| root_node = tree.root_node
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| tokens_index=tree_to_token_index(root_node)
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| code=code.split('\n')
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| code_tokens=[index_to_code_token(x,code) for x in tokens_index]
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| index_to_code={}
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| for idx,(index,code) in enumerate(zip(tokens_index,code_tokens)):
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| index_to_code[index]=(idx,code)
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| try:
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| DFG,_=parser[1](root_node,index_to_code,{})
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| except:
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| DFG=[]
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| DFG=sorted(DFG,key=lambda x:x[1])
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| indexs=set()
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| for d in DFG:
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| if len(d[-1])!=0:
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| indexs.add(d[1])
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| for x in d[-1]:
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| indexs.add(x)
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| new_DFG=[]
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| for d in DFG:
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| if d[1] in indexs:
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| new_DFG.append(d)
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| codes=code_tokens
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| dfg=new_DFG
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| except:
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| codes=code.split()
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| dfg=[]
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| dic={}
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| for d in dfg:
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| if d[1] not in dic:
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| dic[d[1]]=d
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| else:
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| 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])))
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| DFG=[]
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| for d in dic:
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| DFG.append(dic[d])
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| dfg=DFG
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| return dfg
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| def normalize_dataflow_item(dataflow_item):
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| var_name = dataflow_item[0]
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| var_pos = dataflow_item[1]
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| relationship = dataflow_item[2]
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| par_vars_name_list = dataflow_item[3]
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| par_vars_pos_list = dataflow_item[4]
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| var_names = list(set(par_vars_name_list+[var_name]))
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| norm_names = {}
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| for i in range(len(var_names)):
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| norm_names[var_names[i]] = 'var_'+str(i)
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| norm_var_name = norm_names[var_name]
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| relationship = dataflow_item[2]
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| norm_par_vars_name_list = [norm_names[x] for x in par_vars_name_list]
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| return (norm_var_name, relationship, norm_par_vars_name_list)
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| def normalize_dataflow(dataflow):
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| var_dict = {}
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| i = 0
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| normalized_dataflow = []
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| for item in dataflow:
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| var_name = item[0]
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| relationship = item[2]
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| par_vars_name_list = item[3]
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| for name in par_vars_name_list:
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| if name not in var_dict:
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| var_dict[name] = 'var_'+str(i)
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| i += 1
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| if var_name not in var_dict:
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| var_dict[var_name] = 'var_'+str(i)
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| i+= 1
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| normalized_dataflow.append((var_dict[var_name], relationship, [var_dict[x] for x in par_vars_name_list]))
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| return normalized_dataflow
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