aws-samples / aws-greengrass-samples
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 23 units with 337 lines of code in units (60.9% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 2 medium complex units (120 lines of code)
    • 1 simple units (35 lines of code)
    • 20 very simple units (182 lines of code)
0% | 0% | 35% | 10% | 54%
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% | 35% | 10% | 54%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
traffic-light-example-python0% | 0% | 58% | 17% | 24%
iot-blog/image-classification-connector-and-feedback/part_20% | 0% | 0% | 0% | 100%
iot-blog/image-classification-connector-and-feedback/part_10% | 0% | 0% | 0% | 100%
hello-world-python0% | 0% | 0% | 0% | 100%
hello-world-counter-python0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def discoverGGC()
in traffic-light-example-python/trafficLight.py
60 13 5
def discoverGGC()
in traffic-light-example-python/lightController.py
60 13 5
def function_handler()
in traffic-light-example-python/carAggregator.py
35 6 2
def customShadowCallback_Update()
in traffic-light-example-python/trafficLight.py
11 5 3
def customShadowCallback_Update()
in traffic-light-example-python/lightController.py
11 5 3
def get_inference()
in iot-blog/image-classification-connector-and-feedback/part_2/beverageclassifier.py
24 4 1
def get_inference()
in iot-blog/image-classification-connector-and-feedback/part_1/beverageclassifier.py
24 4 1
def create_s3_filename()
in iot-blog/image-classification-connector-and-feedback/part_2/beverageclassifier.py
4 3 3
def function_handler()
in iot-blog/image-classification-connector-and-feedback/part_2/beverageclassifier.py
19 3 2
def greengrass_hello_world_run()
in hello-world-python/greengrassHelloWorld.py
15 3 0
def customShadowCallback_Delta()
in traffic-light-example-python/trafficLight.py
12 2 3
def isIpAddress()
in traffic-light-example-python/trafficLight.py
5 2 1
def isIpAddress()
in traffic-light-example-python/lightController.py
5 2 1
def upload_to_s3()
in iot-blog/image-classification-connector-and-feedback/part_2/beverageclassifier.py
6 2 2
def function_handler()
in hello-world-counter-python/greengrassHelloWorldCounter.py
16 2 2
def getGGCAddr()
in traffic-light-example-python/trafficLight.py
3 1 1
def getGGCAddr()
in traffic-light-example-python/lightController.py
3 1 1
def capture_and_save_image_as()
in iot-blog/image-classification-connector-and-feedback/part_2/beverageclassifier.py
2 1 1
def create_image_filename()
in iot-blog/image-classification-connector-and-feedback/part_2/beverageclassifier.py
4 1 0
def capture_and_save_image_as()
in iot-blog/image-classification-connector-and-feedback/part_1/beverageclassifier.py
2 1 1