facebookresearch / privacy_lint
Conditional Complexity

The distribution of complexity of units (measured with McCabe index).

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).
Learn more...
Conditional Complexity Overall
  • There are 36 units with 222 lines of code in units (53.8% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 0 medium complex units (0 lines of code)
    • 0 simple units (0 lines of code)
    • 36 very simple units (222 lines of code)
0% | 0% | 0% | 0% | 100%
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
py0% | 0% | 0% | 0% | 100%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
privacy_lint0% | 0% | 0% | 0% | 100%
privacy_lint/attacks0% | 0% | 0% | 0% | 100%
privacy_lint/dataset0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def flatten()
in privacy_lint/dataset/masks.py
 13 5 2
def __init__()
in privacy_lint/dataset/__init__.py
 14 3 3
def group()
in privacy_lint/attack_results.py
 16 3 3
def default_compute_accuracies()
in privacy_lint/attacks/gap.py
 10 3 2
def compute_softmax()
in privacy_lint/attacks/shadow.py
 10 3 2
def multiply_round()
in privacy_lint/dataset/masks.py
 7 2 2
def generate_splits()
in privacy_lint/dataset/masks.py
 10 2 2
def balance()
in privacy_lint/attack_results.py
 11 2 1
def train_attack_models()
in privacy_lint/attacks/shadow.py
 9 2 1
def __getitem__()
in privacy_lint/dataset/__init__.py
 2 1 2
def __len__()
in privacy_lint/dataset/__init__.py
 2 1 1
def idx_to_mask()
in privacy_lint/dataset/masks.py
 4 1 2
def generate_subsets()
in privacy_lint/dataset/masks.py
 2 1 0
def __init__()
in privacy_lint/attack_results.py
 3 1 3
def _upsample()
in privacy_lint/attack_results.py
 6 1 2
def _get_balanced_scores()
in privacy_lint/attack_results.py
 2 1 0
def _get_scores_and_labels_ordered()
in privacy_lint/attack_results.py
 9 1 1
def _get_area_under_curve()
in privacy_lint/attack_results.py
 5 1 2
def get_max_accuracy_threshold()
in privacy_lint/attack_results.py
 11 1 1
def get_accuracy()
in privacy_lint/attack_results.py
 6 1 2