RPA.build_tree
has a cyclomatic complexity of 18 with "high" risk212 P0: Optional[Point] = None
213 scalars: Optional[List[int]] = None
214
215 def build_tree(216 self,
217 params: DomainParameters,
218 tries: int = 10,
switch_sign_propagate
has a cyclomatic complexity of 19 with "high" risk 75 pass
76
77
78def switch_sign_propagate( 79 node: CodeOpNode, variable: str, output_signs: Dict[str, int]
80):
81 if node.is_add:
Formula.__validate_assumptions
has a cyclomatic complexity of 17 with "high" risk157 )
158 params[coord + str(i + 1)] = value
159
160 def __validate_assumptions(self, field, params):161 # Validate assumptions and compute formula parameters.
162 # TODO: Should this also validate coordinate assumptions and compute their parameters?
163 is_symbolic = any(isinstance(x, SymbolicMod) for x in params.values())
_create_params
has a cyclomatic complexity of 25 with "high" risk141 return self.curves[item]
142
143
144def _create_params(curve, coords, infty):145 if curve["field"]["type"] == "Binary":
146 raise ValueError("Binary field curves are currently not supported.")
147 if curve["field"]["type"] == "Extension":
eliminate_y
has a cyclomatic complexity of 16 with "high" risk359 return poly
360
361
362def eliminate_y(poly: Poly, model: CurveModel) -> Poly:363 """
364 Eliminate the remaining ys (only power 1).
365
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. |