aws-samples / lookout-for-equipment-demo
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 43 units with 578 lines of code in units (53.6% 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)
    • 5 simple units (159 lines of code)
    • 38 very simple units (419 lines of code)
0% | 0% | 0% | 27% | 72%
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% | 27% | 72%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
getting_started/utils0% | 0% | 0% | 29% | 70%
blogs/wind-turbine-engie0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def _get_time_ranges()
in getting_started/utils/lookout_equipment_utils.py
 14 8 1
def plot_histograms()
in getting_started/utils/lookout_equipment_utils.py
 51 7 3
def set_parameters()
in getting_started/utils/lookout_equipment_utils.py
 41 7 11
def list_inference_executions()
in getting_started/utils/lookout_equipment_utils.py
 27 6 5
def create()
in getting_started/utils/lookout_equipment_utils.py
 26 6 1
def plot_signals()
in getting_started/utils/lookout_equipment_utils.py
 45 5 3
def get_predictions()
in getting_started/utils/lookout_equipment_utils.py
 14 4 1
def delete()
in getting_started/utils/lookout_equipment_utils.py
 12 4 1
def map_features()
in blogs/wind-turbine-engie/utils.py
 6 4 2
def _create_data_schema_map()
in getting_started/utils/lookout_equipment_utils.py
 11 3 1
def _create_component_schema()
in getting_started/utils/lookout_equipment_utils.py
 19 3 2
def get_labels()
in getting_started/utils/lookout_equipment_utils.py
 10 3 2
def locate_features_with_too_many_missing_values()
in blogs/wind-turbine-engie/utils.py
 9 3 2
def __init__()
in getting_started/utils/lookout_equipment_utils.py
 24 2 4
def _load_model_response()
in getting_started/utils/lookout_equipment_utils.py
 17 2 1
def get_predictions()
in getting_started/utils/lookout_equipment_utils.py
 4 2 1
def _poll_event()
in getting_started/utils/lookout_equipment_utils.py
 11 2 4
def create()
in getting_started/utils/lookout_equipment_utils.py
 7 2 2
def start()
in getting_started/utils/lookout_equipment_utils.py
 6 2 2
def stop()
in getting_started/utils/lookout_equipment_utils.py
 6 2 2