class MiningErrfuncShell:
def __init__(self):
self.db_provider = DBContentsProvider()
parser = argparse.ArgumentParser(description='识别源代码项目中的返回值敏感型函数')
parser.add_argument(
'-t',
"--type",
required=True,
type=str,
choices=["select", "all"],
help='select:只对config.py中设置的函数进行识别\n all:对源代码项目中所有的函数进行识别')
parser.add_argument("-prj",
"--projectname",
required=True,
type=str,
help="待识别函数所在的源代码项目，将以此建立同名数据文件夹")
self.args = parser.parse_args()
gl.G_prjdata_dir = "%s/%s" % (gl.G_alldata_dir, self.args.projectname)
gl.G_result_path = "%s/%s" % (gl.G_prjdata_dir, "xp_err.txt")
gl.G_debuginfo_path = "%s/%s" % (gl.G_prjdata_dir, "degbug.txt")
gl.G_result_xls = "%s/%s" % (gl.G_prjdata_dir, "report.xls")
def set_fuctions_bechecked(self):
func_list = []
if self.args.type == "all":
func_list = self.db_provider.query_allCallee_name()
elif self.args.type == "select":
func_list = gl.G_func_list
# 删除列表中出现在gl.G_func_unnormal中的异常函数，即Joern无法处理的函数
for item in gl.G_func_unnormal:
if item in func_list:
func_list.remove(item)
return func_list
#result = [isVar funcname callee_counts,is_err,
# ratio_ft_check, ratio_ft_path,ratio_ft_stmt,ratio_ft_notusedTwoside]
def is_erFunc(self, function_name):
datapath = gl.G_prjdata_dir + "/%s.data" % function_name
if os.path.exists(datapath):
#filename = "Data/42153.data"
return
else:
extract_errfun_feature = ExtractErrFunFeatures(function_name)
#patterns = extract_check_patterns.run(False, callee_ids)
feature_callees, flag_rvar_func = extract_errfun_feature.run(
flag_thread=False)
return
def run(self):
# 根据用户输入的项目名称，创建保存程序输出的文件夹
result = make_dir(gl.G_prjdata_dir)
write_info(gl.G_debuginfo_path, result)
allCallee_name = self.set_fuctions_bechecked()
num_func = len(allCallee_name)
tmp_index = 0
for function_name in allCallee_name:
print "%d-%d 识别%s\n" % (num_func, tmp_index, function_name)
tmp_index = tmp_index + 1
self.is_erFunc(function_name)
return
class MiningErrfuncShell:
def __init__(self):
self.db_provider = DBContentsProvider()
parser = argparse.ArgumentParser(description='识别源代码项目中的返回值敏感型函数')
parser.add_argument(
'-t',
"--type",
required=True,
type=str,
choices=["select", "all"],
help='select:只对config.py中设置的函数进行识别\n all:对源代码项目中所有的函数进行识别')
parser.add_argument("-prj",
"--projectname",
required=True,
type=str,
help="待识别函数所在的源代码项目，将以此建立同名数据文件夹")
self.args = parser.parse_args()
gl.G_prjdata_dir = "%s/%s" % (gl.G_alldata_dir, self.args.projectname)
gl.G_result_path = "%s/%s" % (gl.G_prjdata_dir, "xp_err.txt")
gl.G_debuginfo_path = "%s/%s" % (gl.G_prjdata_dir, "degbug.txt")
gl.G_result_xls = "%s/%s" % (gl.G_prjdata_dir, "report.xls")
def set_fuctions_bechecked(self):
func_list = []
if self.args.type == "all":
func_list = self.db_provider.query_allCallee_name()
elif self.args.type == "select":
func_list = gl.G_func_list
# 删除列表中出现在gl.G_func_unnormal中的异常函数，即Joern无法处理的函数
for item in gl.G_func_unnormal:
if item in func_list:
func_list.remove(item)
return func_list
#result = [isVar funcname callee_counts,is_err,
# ratio_ft_check, ratio_ft_path,ratio_ft_stmt,ratio_ft_notusedTwoside]
def is_erFunc(self, function_name):
#function_name_str = function_name.encode('gbk')
#获取特征数据
savedinfo = "获取%s的特征数据\n" % function_name
write_info(gl.G_debuginfo_path, savedinfo)
datapath = gl.G_prjdata_dir + "/%s.data" % function_name
if os.path.exists(datapath):
#filename = "Data/42153.data"
datatmp = ObjDataAndBinFile.binfile2objdata(datapath)
feature_callees = datatmp[0]
flag_rvar_func = datatmp[1]
else:
extract_errfun_feature = ExtractErrFunFeatures(function_name)
#patterns = extract_check_patterns.run(False, callee_ids)
feature_callees, flag_rvar_func = extract_errfun_feature.run(
flag_thread=False)
# 挖掘返回值敏感型函数
write_info(gl.G_debuginfo_path, "获取%s的识别结果\n" % function_name)
obj_MiningErrFunc = MiningErrFunc(feature_callees)
#mining_result = [callee_counts,is_err,
# ratio_ft_check, ratio_ft_path,ratio_ft_stmt,ratio_ft_notusedTwoside]
mining_result = obj_MiningErrFunc.run()
# 修正返回值函数判断,若存在检查比例，显然具有返回值。其余保持原结果
if mining_result[2] > 0:
flag_rvar_func = 1
xp_tmp = []
xp_tmp.append(flag_rvar_func)
xp_tmp.append(function_name)
xp_tmp.extend(mining_result)
#infoSaved = "%s %s %s %s %s %s %s"%(xp_tmp[1],xp_tmp[2],xp_tmp[3],xp_tmp[0],
# xp_tmp[3],xp_tmp[4],xp_tmp[5],xp_tmp[6])
#infoSaved = str(xp_tmp[1]) + str(" ") + str(xp_tmp[2]) + " " + str(xp_tmp[0])\
# + " " + str(xp_tmp[3])+ " " + str(xp_tmp[4])+ " " + str(xp_tmp[5])
write_info(gl.G_debuginfo_path, xp_tmp)
return xp_tmp
def run(self):
# 根据用户输入的项目名称，创建保存程序输出的文件夹
result = make_dir(gl.G_prjdata_dir)
write_info(gl.G_debuginfo_path, result)
allCallee_name = self.set_fuctions_bechecked()
xpoint_reasult = []
num_func = len(allCallee_name)
num_alalysed_func = 0
infoSaved = "\n# *%s* 中返回值敏感型函数识别：\n起始时间 = %s 待识别函数数量 = %s"\
%(self.args.projectname, datetime.datetime.now(),num_func)
write_info(gl.G_result_path, infoSaved)
tmp_index = 0
for function_name in allCallee_name:
print "%d 识别%s\n" % (tmp_index, function_name)
tmp_index = tmp_index + 1
xp_tmp = []
# 识别function_name
#is_erFunc = [isVar funcname callee_counts,is_err,
# ratio_ft_check, ratio_ft_path,ratio_ft_stmt,ratio_ft_notusedTwoside]
xp_tmp = self.is_erFunc(function_name)
xpoint_reasult.append(xp_tmp)
num_alalysed_func = num_alalysed_func + 1
infoSaved = "结束时间 = %s 已分析函数数量 = %s\n"\
%(datetime.datetime.now(),num_alalysed_func)
write_info(gl.G_result_path, infoSaved)
xpoint_reasult = sorted(xpoint_reasult,
key=lambda l: (l[3], l[1]),
reverse=True)
# 输出结果及统计数据
num_rvarFun = 0 # 返回值函数个数
num_errFun = 0 # 返回值敏感型函数个数
write_info(gl.G_result_path, "识别结果：")
write_info(
gl.G_result_path, "函数名 counts, is_rVar, is_err, ratio_ft_path,"
"ratio_ft_stmt,ratio_ft_usedOneside, is_rVarF")
for xp_tmp in xpoint_reasult:
infoSaved = "%s: %s %s %s %s %s %s %s"\
%(xp_tmp[1],xp_tmp[2],xp_tmp[0],
xp_tmp[3],xp_tmp[4],xp_tmp[5],xp_tmp[6],xp_tmp[7])
write_info(gl.G_result_path, infoSaved)
if xp_tmp[0] == 1:
num_rvarFun = num_rvarFun + 1
if xp_tmp[3] == 1:
num_errFun = num_errFun + 1
infoSaved = "返回值函数个数=%s, 返回值敏感型函数个数=%s\n" % (num_rvarFun, num_errFun)
write_info(gl.G_result_path, infoSaved)
wXLS = writeXLS()
wXLS.write_excel(gl.G_result_xls, xpoint_reasult)
"""
##debug：缺陷检测部分,检测前面num_detect个fun
num_detect = 50
index = 0
for func_item in xpoint_reasult:
function_name_str = func_item[0].encode('gbk')
extract_errfun_feature = ExtractErrFunFeatures(function_name_str)
#patterns = extract_check_patterns.run(False, callee_ids)
feature_callees,flag_rvar_func = extract_errfun_feature.run(flag_thread=False)
for ft_callee in feature_callees:
ft_info = "%s %s %s %s %s %s %s %s"%(func_item[0],ft_callee[0],ft_callee[2][0],ft_callee[2][1],
ft_callee[2][2],ft_callee[3][0], ft_callee[3][1],ft_callee[3][2])
f_dectect= open("Data/detect.txt",'a' )
f_dectect.write(ft_info)
f_dectect.write("\n")
index =index +1
if index > num_func:
break
##
"""
return
class MiningErrfuncShell:
def __init__(self):
self.db_provider = DBContentsProvider()
parser = argparse.ArgumentParser(description='识别源代码项目中的返回值敏感型函数')
parser.add_argument(
'-t',
"--type",
required=True,
type=str,
choices=["select", "all"],
help='select:只对config.py中设置的函数进行识别\n all:对源代码项目中所有的函数进行识别')
parser.add_argument("-prj",
"--projectname",
required=True,
type=str,
help="待识别函数所在的源代码项目，将以此建立同名数据文件夹")
self.args = parser.parse_args()
gl.G_prjdata_dir = "%s/%s" % (gl.G_alldata_dir, self.args.projectname)
gl.G_result_path = "%s/%s" % (gl.G_prjdata_dir, "xp_err.txt")
gl.G_debuginfo_path = "%s/%s" % (gl.G_prjdata_dir, "degbug.txt")
gl.G_xcallee_txt = "%s/%s" % (gl.G_prjdata_dir, "xcallee.txt")
def run_gremlin_query(self, query):
return self.db_provider.run_gremlin_query(query)
def query_loc_callsite(self, callee_id):
query = """
g.v(%s).statements.transform{[g.v(it.functionId).functionToFile.filepath, it.location]}
""" % callee_id
result = self.run_gremlin_query(query)
loc = "%s: %s" % (result[0][0][0], result[0][1])
return loc
def set_fuctions_bechecked(self):
func_list = []
if self.args.type == "all":
func_list = self.db_provider.query_allCallee_name()
elif self.args.type == "select":
func_list = gl.G_func_list
# 删除列表中出现在gl.G_func_unnormal中的异常函数，即Joern无法处理的函数
for item in gl.G_func_unnormal:
if item in func_list:
func_list.remove(item)
return func_list
def query_loc_callsite(self, callee_id):
query = """
g.v(%s).statements.transform{[g.v(it.functionId).functionToFile.filepath, it.location]}
""" % callee_id
result = self.run_gremlin_query(query)
loc = "%s: %s" % (result[0][0][0], result[0][1])
return loc
#result = [isVar funcname callee_counts,is_err,
# ratio_ft_check, ratio_ft_path,ratio_ft_stmt,ratio_ft_notusedTwoside]
def is_xCallee(self, function_name):
#function_name_str = function_name.encode('gbk')
#获取特征数据
savedinfo = "获取%s的特征数据\n" % function_name
write_info(gl.G_debuginfo_path, savedinfo)
datapath = gl.G_prjdata_dir + "/%s.data" % function_name
if os.path.exists(datapath):
#filename = "Data/42153.data"
datatmp = ObjDataAndBinFile.binfile2objdata(datapath)
feature_callees = datatmp[0]
flag_rvar_func = datatmp[1]
else:
extract_errfun_feature = ExtractErrFunFeatures(function_name)
#patterns = extract_check_patterns.run(False, callee_ids)
feature_callees, flag_rvar_func = extract_errfun_feature.run(
flag_thread=False)
# 挖掘返回值敏感型函数
write_info(gl.G_debuginfo_path, "获取%s的识别结果\n" % function_name)
obj_MiningErrFunc = MiningxCalleeErr(feature_callees)
#mining_result = [callee_counts,is_err,
# ratio_ft_check, ratio_ft_path,ratio_ft_stmt,ratio_ft_notusedTwoside]
mining_result = obj_MiningErrFunc.run()
# 修正返回值函数判断,若存在检查比例，显然具有返回值。其余保持原结果
write_info(gl.G_debuginfo_path, mining_result)
return mining_result
def run(self):
# 根据用户输入的项目名称，创建保存程序输出的文件夹
result = make_dir(gl.G_prjdata_dir)
write_info(gl.G_debuginfo_path, result)
allCallee_name = self.set_fuctions_bechecked()
xpoint_reasult = []
num_func = len(allCallee_name)
num_alalysed_func = 0
infoSaved = "\n# *%s* 中返回值敏感型函数识别：\n起始时间 = %s 待识别函数数量 = %s"\
%(self.args.projectname, datetime.datetime.now(),num_func)
write_info(gl.G_result_path, infoSaved)
tmp_index = 0
for function_name in allCallee_name:
print "%d 识别%s\n" % (tmp_index, function_name)
tmp_index = tmp_index + 1
xp_tmp = []
# 识别function_name
#is_erFunc = [isVar funcname callee_counts,is_err,
# ratio_ft_check, ratio_ft_path,ratio_ft_stmt,ratio_ft_notusedTwoside]
xp_tmp = self.is_xCallee(function_name)
xpoint_reasult.append(xp_tmp)
num_alalysed_func = num_alalysed_func + 1
for item in xp_tmp:
loc = self.query_loc_callsite(item[0])
print item, loc
return