aws-deepracer / aws-deepracer-inference-pkg
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 29 units with 390 lines of code in units (63.6% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 1 medium complex units (50 lines of code)
    • 1 simple units (30 lines of code)
    • 27 very simple units (310 lines of code)
0% | 0% | 12% | 7% | 79%
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
cpp0% | 0% | 13% | 7% | 78%
py0% | 0% | 0% | 0% | 100%
hpp0% | 0% | 0% | 0% | 100%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
src0% | 0% | 13% | 7% | 78%
launch0% | 0% | 0% | 0% | 100%
include/inference_pkg0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
void RLInferenceModel::sensorCB()
in inference_pkg/src/intel_inference_eng.cpp
 50 11 1
InferenceEngine::InferRequest setMultiHeadModel()
in inference_pkg/src/intel_inference_eng.cpp
 30 9 8
template void loadStereoImg()
in inference_pkg/src/intel_inference_eng.cpp
 20 5 5
bool RLInferenceModel::loadModel()
in inference_pkg/src/intel_inference_eng.cpp
 29 5 2
void LoadModelHdl()
in inference_pkg/src/inference_node.cpp
 23 5 3
bool cvtToCVObjResize()
in inference_pkg/src/image_process.cpp
 29 5 3
void Grey::processImage()
in inference_pkg/src/image_process.cpp
 20 5 3
template void load1DImg()
in inference_pkg/src/intel_inference_eng.cpp
 17 4 5
template void loadStackImg()
in inference_pkg/src/intel_inference_eng.cpp
 16 4 5
void InferStateHdl()
in inference_pkg/src/inference_node.cpp
 23 4 3
void stack()
in inference_pkg/src/image_process.cpp
 18 4 4
void masking()
in inference_pkg/src/image_process.cpp
 11 4 3
void GreyDiff::processImage()
in inference_pkg/src/image_process.cpp
 19 4 3
void Grey::processImageVec()
in inference_pkg/src/image_process.cpp
 17 3 3
void loadLidarData()
in inference_pkg/src/intel_inference_eng.cpp
 8 2 2
void RLInferenceModel::startInference()
in inference_pkg/src/intel_inference_eng.cpp
 6 2 0
void threshold()
in inference_pkg/src/image_process.cpp
 8 2 3
def generate_launch_description()
in inference_pkg/launch/inference_pkg_launch.py
 9 1 0
void RLInferenceModel::stopInference()
in inference_pkg/src/intel_inference_eng.cpp
 3 1 0
int main()
in inference_pkg/src/inference_node.cpp
 10 1 2