PointSystem.give
has a cyclomatic complexity of 18 with "high" risk109 description=lang[default_language]["points_give_description"],
110 )
111 @auth_guard.check_permissions("point/give")
112 async def give(113 self,
114 interaction: nextcord.Interaction,
115 recipient: nextcord.Member = nextcord.SlashOption(
AuthGuard.check_permissions
has a cyclomatic complexity of 21 with "high" risk132 self.db.execute(statement, tuple(self.command_id_list))
133 self.command_id_list.clear()
134
135 def check_permissions(self, command_id):136 """
137 Decorator to check permissions for a command.
138
Spinner.is_winning
has a cyclomatic complexity of 36 with "very-high" risk 64 return [random.choice(self.options) for _ in range(4)]
65
66 @staticmethod
67 def is_winning(columns: List[str]) -> tuple[Any, bool, bool]: 68 """
69 Determines if the given columns result in a win.
70
Spinner.is_winning
has a cyclomatic complexity of 19 with "high" risk 65 return [random.choice(self.options) for _ in range(3)]
66
67 @staticmethod
68 def is_winning(columns: List[str]) -> tuple[Any, bool, bool]: 69 """
70 Determines if the given columns result in a win.
71
create_top_songs_poster
has a cyclomatic complexity of 16 with "high" risk 29from PIL import Image, ImageDraw, ImageFont
30
31
32def create_top_songs_poster( 33 songs: List[dict],
34 title: str,
35 description: str,
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. |