export_speakers_csv
has a cyclomatic complexity of 20 with "high" risk359 return rows
360
361
362def export_speakers_csv(speakers):363 headers = [
364 'Speaker Name',
365 'Speaker Email',
export_sessions_csv
has a cyclomatic complexity of 23 with "high" risk229 return virtual_check_in_times
230
231
232def export_sessions_csv(sessions):233 headers = [
234 'Session Title',
235 'Session Starts At',
export_orders_csv
has a cyclomatic complexity of 25 with "high" risk 10from app.models.user_check_in import VirtualCheckIn
11
12
13def export_orders_csv(orders): 14 headers = [
15 'Order#',
16 'Order Date',
UserDetail.before_update_object
has a cyclomatic complexity of 24 with "high" risk271 else:
272 view_kwargs['id'] = None
273
274 def before_update_object(self, user, data, view_kwargs):275 # TODO: Make a celery task for this
276 # if data.get('avatar_url') and data['original_image_url'] != user.original_image_url:
277 # try:
UserDetail.before_get_object
has a cyclomatic complexity of 33 with "very-high" risk127 else:
128 self.schema = UserSchemaPublic
129
130 def before_get_object(self, view_kwargs):131 """
132 before get method for user object
133 :param view_kwargs:
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. |