aws-samples / sagemaker-end-to-end-distributed-tensorflow2
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 13 units with 211 lines of code in units (77.3% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 1 medium complex units (77 lines of code)
    • 3 simple units (71 lines of code)
    • 9 very simple units (63 lines of code)
0% | 0% | 36% | 33% | 29%
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% | 36% | 33% | 29%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
training0% | 0% | 44% | 26% | 28%
inference0% | 0% | 0% | 64% | 35%
Most Complex Units
Top 13 most complex units
Unit# linesMcCabe index# params
def main()
in source_directory/training/training_script.py
 77 12 1
def load_dataset()
in source_directory/training/training_script.py
 32 10 3
def _process_input()
in source_directory/inference/inference.py
 25 8 2
def get_files_for_processor()
in source_directory/training/training_script.py
 14 8 1
def _dataset_parser()
in source_directory/training/training_script.py
 14 4 1
def _process_output()
in source_directory/inference/inference.py
 7 2 2
def create_model()
in source_directory/training/training_script.py
 14 2 0
def handler()
in source_directory/inference/inference.py
 5 1 2
def _return_error()
in source_directory/inference/inference.py
 2 1 2
def __init__()
in source_directory/training/training_script.py
 4 1 3
def on_epoch_end()
in source_directory/training/training_script.py
 2 1 3
def image_augmentation()
in source_directory/training/training_script.py
 10 1 2
def save_model()
in source_directory/training/training_script.py
 5 1 2