aws-samples / sagemaker-end-to-end-workshop
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 27 units with 556 lines of code in units (61.8% 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)
    • 27 very simple units (556 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
4-Deployment/Batch/config0% | 0% | 0% | 0% | 100%
4-Deployment/RealTime/config0% | 0% | 0% | 0% | 100%
6-Pipelines/config0% | 0% | 0% | 0% | 100%
5-Monitoring/config0% | 0% | 0% | 0% | 100%
5-Monitoring0% | 0% | 0% | 0% | 100%
ROOT0% | 0% | 0% | 0% | 100%
3-Evaluation/solutions0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def get_estimator_from_lab2()
in 4-Deployment/Batch/config/solution_lab2.py
 80 5 2
def main()
in 4-Deployment/Batch/config/xgboost_customer_churn.py
 42 5 0
def model_fn()
in 4-Deployment/Batch/config/xgboost_customer_churn.py
 17 5 1
def main()
in 4-Deployment/RealTime/config/xgboost_customer_churn.py
 42 5 0
def model_fn()
in 4-Deployment/RealTime/config/xgboost_customer_churn.py
 15 5 1
def model_fn()
in 5-Monitoring/config/inference.py
 15 5 1
def get_endpoint_from_lab4()
in 5-Monitoring/config/solution_lab4.py
 56 5 0
def main()
in 6-Pipelines/config/xgboost_customer_churn.py
 42 5 0
def model_fn()
in 6-Pipelines/config/xgboost_customer_churn.py
 15 5 1
def get_dataset()
in 6-Pipelines/config/evaluate.py
 10 4 2
def output_fn()
in 5-Monitoring/config/inference.py
 9 3 2
def input_fn()
in 4-Deployment/Batch/config/xgboost_customer_churn.py
 6 2 2
def get_estimator_from_lab2()
in 4-Deployment/RealTime/config/solution_lab2.py
 76 2 2
def input_fn()
in 5-Monitoring/config/inference.py
 8 2 2
def parse_args()
in 4-Deployment/Batch/config/xgboost_customer_churn.py
 20 1 0
def create_smdebug_hook()
in 4-Deployment/Batch/config/xgboost_customer_churn.py
 10 1 5
def predict_fn()
in 4-Deployment/Batch/config/xgboost_customer_churn.py
 5 1 2
def parse_args()
in 4-Deployment/RealTime/config/xgboost_customer_churn.py
 20 1 0
def create_smdebug_hook()
in 4-Deployment/RealTime/config/xgboost_customer_churn.py
 10 1 5
def pip_install()
in 3-Evaluation/solutions/evaluate_with_experiments.py
 3 1 1