microsoft / csa-misc-utils
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 25 units with 205 lines of code in units (5.0% 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)
    • 2 simple units (48 lines of code)
    • 23 very simple units (157 lines of code)
0% | 0% | 0% | 23% | 76%
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% | 23% | 76%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
sa-dsml-many-models0% | 0% | 0% | 28% | 71%
sample-UpdateManagement0% | 0% | 0% | 0% | 100%
sample-Python-KeyVault-Function0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def run()
in sa-dsml-many-models/code/aml_prs/model_train.py
 32 7 1
def transform()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 16 6 2
def forecast()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 13 4 2
def run()
in sa-dsml-many-models/code/aml_prs/prediction.py
 16 3 1
def __init__()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 6 3 3
def fit()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 14 3 3
def __init__()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 9 3 5
def _recursive_forecast()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 10 3 2
def get_automation_runas_credential()
in sample-UpdateManagement/PowerShell/AzureAutomationRunbooks/RollbackPatches/linRollBack.py
 21 2 1
def init()
in sa-dsml-many-models/code/aml_prs/model_train.py
 5 1 0
def init()
in sa-dsml-many-models/code/aml_prs/prediction.py
 5 1 0
def __init__()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 3 1 2
def fit()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 2 1 3
def transform()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 2 1 2
def fit()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 2 1 3
def transform()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 2 1 2
def fit()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 8 1 3
def __init__()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 3 1 3
def transform()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 2 1 2
def predict()
in sa-dsml-many-models/code/util/timeseries_utilities.py
 9 1 2