file_contents has a cyclomatic complexity of 165 with "critical" risk142 trailing_commas: Set[str]
143
144
145def file_contents(contents: str, config: Config = DEFAULT_CONFIG) -> ParsedContent:146 """Parses a python file taking out and categorizing imports."""
147 line_separator: str = config.line_ending or _infer_line_separator(contents)
148 in_lines = contents.splitlines()skip_line has a cyclomatic complexity of 18 with "high" risk 79 return import_string.replace("{ ", "{|").replace(" }", "|}")
80
81
82def skip_line( 83 line: str,
84 in_quote: str,
85 index: int,line has a cyclomatic complexity of 30 with "very-high" risk 68 return statement
69
70
71def line(content: str, line_separator: str, config: Config = DEFAULT_CONFIG) -> str: 72 """Returns a line wrapped to the specified line-length, if possible."""
73 wrap_mode = config.multi_line_output
74 if len(content) > config.line_length and wrap_mode != Modes.NOQA: # type: ignoreimports has a cyclomatic complexity of 47 with "very-high" risk 39 )
40
41
42def imports( 43 input_stream: TextIO,
44 config: Config = DEFAULT_CONFIG,
45 file_path: Optional[Path] = None,_get_config_data has a cyclomatic complexity of 41 with "very-high" risk829 return trie_root
830
831
832def _get_config_data(file_path: str, sections: Tuple[str, ...]) -> Dict[str, Any]:833 settings: Dict[str, Any] = {}
834
835 if file_path.endswith(".toml"):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. |