def run_query_for_prj(fold_no, query_text):
conn = sqlite3.connect(DB_CONN)
c = conn.cursor()
query_info = json.loads(query_text)
folder, filename = query_info["folder"], query_info["file"]
print "Folder-name:" + folder, "File:" + filename
call_list = []
for call in query_info["calls"]:
call_list.append(call["tgt"])
other_calls = ','.join(query_info["other_calls"])
#print other_calls
calls = ','.join(call_list)
arg_types = ','.join(extract_types(query_info['context']))
arg_values = ','.join(process_tokens(query_info['context']))
obj_name = ','.join(process_obj_name(query_info['obj']))
for fold in range(1, FOLDS + 1):
if fold != fold_no:
try:
c.execute(
"INSERT INTO TRAINSET_{fold} (obj_type, obj_name, calls, arg_types, arg_values, other_calls) VALUES (?, ?, ?, ?, ?, ?)"
.format(fold=str(fold)),
(query_info['type'], obj_name, calls, arg_types,
arg_values, other_calls))
except sqlite3.OperationalError, msg:
print msg
def run_query_for_prj(fold_no, query_text):
conn=sqlite3.connect(DB_CONN)
c=conn.cursor()
query_info=json.loads(query_text)
folder, filename = query_info["folder"],query_info["file"]
print "Folder-name:"+folder, "File:"+filename
call_list=[]
for call in query_info["calls"]:
call_list.append(call["tgt"])
other_calls=','.join(query_info["other_calls"])
#print other_calls
calls=','.join(call_list)
arg_types=','.join(extract_types(query_info['context']))
arg_values=','.join(process_tokens(query_info['context']))
obj_name=','.join(process_obj_name(query_info['obj']))
for fold in range(1,FOLDS+1):
if fold!=fold_no:
try:
c.execute(
"INSERT INTO TRAINSET_{fold} (obj_type, obj_name, calls, arg_types, arg_values, other_calls) VALUES (?, ?, ?, ?, ?, ?)".format(fold=str(fold)),
(query_info['type'], obj_name, calls, arg_types, arg_values, other_calls))
except sqlite3.OperationalError, msg:
print msg
def get_recos(query, fold_no, context_features, fname):
recommendations=[]
df_graph=None
source=[l for l in query.split('\n') if l!='']
#source=source[:-1]+process(source[-1])
"""Extract the Query Object"""
last_line=process(source[-1])
query_line=re.split('=|\(|\)|\:|\,|\\s*',last_line[-1][:-1])
query_obj=re.findall(r'([self|\w]+.*)',query_line[-1])[-1]
query_obj=query_obj.replace('\"','\'')
#print fname, "query_obj", query_obj
"""Get the data flow graph using the least compilable code in the query"""
source=source[:-1]+[source[-1]+"query_method"]
l=len(source)
i=l
try_stack=[]
parenthesis_stack=[]
is_last_loop=True
count=0
while not df_graph:
for c in source[i-1][::-1]:
if c in [')','}',']']:
parenthesis_stack.append(c)
elif c=='(':
if not parenthesis_stack or parenthesis_stack[-1]!=')':
source[l-1]=source[l-1]+')'
#parenthesis_stack.append('(')
#i=l
else:
parenthesis_stack.pop()
elif c=='{':
if not parenthesis_stack or parenthesis_stack[-1]!='}':
source[l-1]=source[l-1]+'}'
#parenthesis_stack.append('{')
#i=l
else:
parenthesis_stack.pop()
elif c=='[':
if not parenthesis_stack or parenthesis_stack[-1]!=']':
source[l-1]=source[l-1]+']'
#parenthesis_stack.append('{')
#i=l
else:
parenthesis_stack.pop()
split_str=source[i-1].split()
if split_str and is_last_loop:
if split_str[-1][-1]==':':
is_last_loop=False
if 'try:' in source[i-1].strip() \
and i!=l:
pos=source[i-1].find('try')
indent_prefix=source[i-1][:pos]
if indent_prefix not in try_stack:
source=source[:l]
source.append(indent_prefix+'except:')
source.append(indent_prefix+'\t'+ 'pass')
try_stack.append(indent_prefix)
l=l+3
else:
try_stack.remove(indent_prefix)
if 'except ' in source[i-1] or 'except:' in source[i-1]\
and i!=l:
pos=source[i-1].find('except')
indent_prefix=source[i-1][:pos]
if indent_prefix not in try_stack:
try_stack.append(source[i-1][:pos])
if is_last_loop and len(split_str)>1:
if 'if' in split_str[1:] and i==l:
source[i-1]+=" else ''"
for word in keywords:
if word == source[i-1].split()[0]:
pos=source[i-1].find(word)
indent_prefix=source[i-1][:pos]
if source[l-1][-1]!=':':
source[l-1]=source[l-1]+':'
source.append(indent_prefix+'\t'+'pass')
if word=='except':
try_stack.append(indent_prefix)
l=l+1
is_last_loop=False
break
df_graph=ASTBuilder('\n'.join(source[:l])).build_AST()
#df_graph=ASTBuilder('\n'.join(source[:i]+source[l:except_count])).build_AST()
#print '\n'.join(source[:l][-40:])
#print '\n'.join(source[:i]+source[l:except_count])
# print source[i-1], try_stack
# print '\n'.join(source[:i]+source[l:except_count][-20:])
#print '-'*40
i=i-1
count+=1
if i==0:
break
if count>500:
print fname, "INFINITE LOOP"
break
print df_graph
"""Get Nearest Neighbours using Manhattan distance"""
if df_graph:
query_obj_types=[]
query_obj_context=[]
calls=[]
other_calls=[]
sql_query=[]
assign_nodes=[]
assign_nodes, call_nodes=df_graph.find_definitions_and_calls(query_obj)
print call_nodes
if assign_nodes:
for node in assign_nodes:
print node
query_obj_types.extend(node.src)
if node.context:
print node.context, context_features
for feature in context_features:
if feature=='arg_type':
sql_query.append('arg_types')
query_obj_context.extend(
extract_types(node.context))
elif feature=='arg_value':
sql_query.append('arg_values')
query_obj_context.extend(
process_tokens(node.context))
elif feature=='object_name':
sql_query.append('obj_name')
query_obj_context.extend(
process_obj_name(node.tgt)
)
for call_type in call_nodes:
if call_type=='object':
calls.extend(call_nodes[call_type])
else:
other_calls.extend(call_nodes[call_type])
sql_query.append('other_calls')
sql_query.append('calls')
query_count=Counter(calls+query_obj_context)
conn=sqlite3.connect("pyty.db")
c=conn.cursor()
objects=[]
for type in query_obj_types:
sql_select='''SELECT {attr} FROM TRAINSET_{fold} WHERE obj_type=?'''.format(
attr=','.join(sql_query),fold=fold_no)
results=c.execute(sql_select,(type,))
if results:
for obj in results:
obj_count=Counter()
for i in range(len(obj)):
if obj[i]:
obj_count+=Counter(obj[i].split(','))
obj_calls=obj[-1].split(',') if obj[-1] else ''
score=compute_euclidean_dist(query_count, obj_count)
objects.append((obj_calls, score))
objects=sorted(objects, key=lambda tup: tup[1])
call_set=Counter()
min_score =''
for object in objects:
if min_score=='':
min_score=object[1]
if object[1]==min_score:
call_set.update(Counter(object[0])-query_count)
elif len(call_set)<MAX_RECOS:
min_score=object[1]
else:
break
total=float(sum(call_set.values()))
recommendations.extend([call[0] for call in call_set.most_common(MAX_RECOS)])
return recommendations