JSONFiles.calculate_model_statistics
has a cyclomatic complexity of 20 with "high" risk1189 }
1190
1191 @classmethod
1192 def calculate_model_statistics(1193 cls,
1194 target_value: Union[str, int, float],
1195 prob_value: Union[int, float, None] = None,
JSONFiles.write_model_properties_json
has a cyclomatic complexity of 26 with "very-high" risk 280 return False
281
282 @classmethod
283 def write_model_properties_json( 284 cls,
285 model_name: str,
286 target_variable: str,
_build_crud_funcs
has a cyclomatic complexity of 24 with "high" risk2176 return RestObj(json)
2177
2178
2179def _build_crud_funcs(path, single_term=None, plural_term=None, service_name=None):2180 """Utility method for defining simple functions to perform CRUD operations on a REST endpoint.
2181
2182 Parameters
Session._request_token_with_oauth
has a cyclomatic complexity of 17 with "high" risk1242 # Extract access token and return as an Oauth token
1243 return OAuth2Token(match.group(0))
1244
1245 def _request_token_with_oauth(1246 self,
1247 username=None,
1248 password=None,
Session.__init__
has a cyclomatic complexity of 26 with "very-high" risk 282
283 PROFILE_PATH = "~/.sas/viya-api-profiles.yaml"
284
285 def __init__( 286 self,
287 hostname,
288 username=None,
A function with high cyclomatic complexity can be hard to understand and maintain. Cyclomatic complexity is a software metric that measures the number of independent paths through a function. A higher cyclomatic complexity indicates that the function has more decision points and is more complex.
Functions with high cyclomatic complexity are more likely to have bugs and be harder to test. They may lead to reduced code maintainability and increased development time.
To reduce the cyclomatic complexity of a function, you can:
def number_to_name():
number = input()
if not number.isdigit():
print("Enter a valid number")
return
number = int(number)
if number >= 10:
print("Number is too big")
return
if number == 1:
print("one")
elif number == 2:
print("two")
elif number == 3:
print("three")
elif number == 4:
print("four")
elif number == 5:
print("five")
elif number == 6:
print("six")
elif number == 7:
print("seven")
elif number == 8:
print("eight")
elif number == 9:
print("nine")
def number_to_name():
number = input()
if not number.isdigit():
print("Enter a valid number")
return
number = int(number)
if number >= 10:
print("Number is too big")
return
names = {
1: "one",
2: "two",
3: "three",
4: "four",
5: "five",
6: "six",
7: "seven",
8: "eight",
9: "nine",
}
print(names[number])
Cyclomatic complexity threshold can be configured using the
cyclomatic_complexity_threshold
meta field in the
.deepsource.toml
config file.
Configuring this is optional. If you don't provide a value, the Analyzer will
raise issues for functions with complexity higher than the default threshold,
which is medium
for the Python Analyzer.
Here's the mapping of the risk category to the cyclomatic complexity score to help you configure this better:
Risk category | Cyclomatic complexity range | Recommended action |
---|---|---|
low | 1-5 | No action needed. |
medium | 6-15 | Review and monitor. |
high | 16-25 | Review and refactor. Recommended to add comments if the function is absolutely needed to be kept as it is. |
very-high | 26-50 | Refactor to reduce the complexity. |
critical | >50 | Must refactor this. This can make the code untestable and very difficult to understand. |