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:

Break the function into smaller, more manageable functions.
Refactor complex logic into separate functions or classes.
Avoid multiple return paths and deeply nested control expressions.

Bad practice

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")

Recommended

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])

Issue configuration

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.

isudatateam / datateam

No ignore rules found