_
has a cyclomatic complexity of 16 with "high" risk272
273
274@bot.on(admin_cmd(pattern="unrar"))
275async def _(event):276 if event.fwd_from:
277 return
278 mone = await event.edit("Processing ...")
_
has a cyclomatic complexity of 16 with "high" risk168
169
170@bot.on(admin_cmd(pattern="untar"))
171async def _(event):172 if event.fwd_from:
173 return
174 mone = await event.edit("Processing ...")
_
has a cyclomatic complexity of 16 with "high" risk 69
70
71@bot.on(admin_cmd(pattern="unzip"))
72async def _(event): 73 if event.fwd_from:
74 return
75 mone = await event.edit("Processing ...")
download_video
has a cyclomatic complexity of 25 with "high" risk 32
33
34@bot.on(admin_cmd(pattern="playlist(a|v) (.*)"))
35async def download_video(v_url): 36 """ For .ytdl command, download media from YouTube and many other sites. """
37 url = v_url.pattern_match.group(2)
38 type = v_url.pattern_match.group(1).lower()
download_video
has a cyclomatic complexity of 27 with "very-high" risk 91
92
93@bot.on(admin_cmd(pattern="yt(a|v) (.*)"))
94async def download_video(v_url): # sourcery skip: avoid-builtin-shadow 95 """ For .ytdl command, download media from YouTube and many other sites. """
96 url = v_url.pattern_match.group(2)
97 type = v_url.pattern_match.group(1).lower()
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. |