apple / ARKitScenes
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 139 units with 1,482 lines of code in units (61.6% of code).
    • 0 very complex units (0 lines of code)
    • 0 complex units (0 lines of code)
    • 2 medium complex units (129 lines of code)
    • 12 simple units (316 lines of code)
    • 125 very simple units (1,037 lines of code)
0% | 0% | 8% | 21% | 69%
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% | 8% | 21% | 69%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
depth_upsampling0% | 0% | 40% | 14% | 45%
threedod/benchmark_scripts/utils0% | 0% | 9% | 20% | 69%
depth_upsampling/logs0% | 0% | 0% | 89% | 10%
ROOT0% | 0% | 0% | 95% | 4%
depth_upsampling/models/mspf0% | 0% | 0% | 14% | 85%
threedod/benchmark_scripts0% | 0% | 0% | 9% | 90%
depth_upsampling/models0% | 0% | 0% | 72% | 27%
depth_upsampling/losses0% | 0% | 0% | 0% | 100%
depth_upsampling/transfroms0% | 0% | 0% | 0% | 100%
depth_upsampling/models/msg0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def compute_metrics()
in threedod/benchmark_scripts/utils/eval_utils.py
 41 16 1
def main()
in depth_upsampling/train.py
 88 12 1
def raw_files()
in download_data.py
 20 10 2
def download_data()
in download_data.py
 42 10 6
def sample_vis()
in depth_upsampling/sample_vis.py
 32 7 4
def check_file_type()
in threedod/benchmark_scripts/show_3d_bbox_annotation.py
 24 7 1
def polygon_clip()
in threedod/benchmark_scripts/utils/box_utils.py
 30 7 2
def voc_ap()
in threedod/benchmark_scripts/utils/eval_utils.py
 17 7 3
def __getitem__()
in threedod/benchmark_scripts/utils/tenFpsDataLoader.py
 43 7 2
def eval_log()
in depth_upsampling/logs/eval.py
 41 6 4
def train_log()
in depth_upsampling/logs/train.py
 25 6 5
def get_network()
in depth_upsampling/models/__init__.py
 13 6 2
def forward()
in depth_upsampling/models/mspf/blocks/dense_net.py
 16 6 2
def forward()
in depth_upsampling/models/mspf/densenet.py
 13 6 2
def rotate_image()
in depth_upsampling/dataset.py
 12 5 2
def read_mesh()
in threedod/benchmark_scripts/show_3d_bbox_annotation.py
 16 5 1
def extract_gt()
in threedod/benchmark_scripts/utils/tenFpsDataLoader.py
 48 5 1
def batch_to_cuda()
in depth_upsampling/data_utils.py
 6 4 1
def load_image()
in depth_upsampling/dataset.py
 11 4 4
def create_montage_image()
in depth_upsampling/image_utils.py
 13 4 3