mozilla / standards-positions - Conditional Complexity

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

Conditional Complexity Overall

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 18 most complex units

Unit	# lines	McCabe index	# params
def validate_item() in activities.py	37	32	3
def process_body() in gh-data.py	67	19	1
def main() in gh-data.py	32	8	0
def process_labels() in gh-data.py	20	6	1
def validate_literal_block() in activities.py	13	6	4
def add_issue() in activities.py	22	6	4
def merge() in merge-data.py	8	4	2
def is_ignorable_issue() in gh-data.py	7	4	1
def get_url() in gh-data.py	8	4	1
def validate_data() in activities.py	6	4	2
def run() in activities.py	10	4	1
def process() in gh-data.py	12	3	1
def validate_top_level_keys() in activities.py	7	3	2
def __init__() in activities.py	8	2	3
def load_yaml() in activities.py	7	2	1
def save_fixes() in activities.py	5	2	1
def write_json() in gh-data.py	4	1	2
def log_error() in activities.py	2	1	3