tensorflow / fairness-indicators
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 24 units with 295 lines of code in units (44.4% 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)
    • 4 simple units (59 lines of code)
    • 20 very simple units (236 lines of code)
0% | 0% | 0% | 20% | 80%
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% | 20% | 79%
js0% | 0% | 0% | 0% | 100%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
fairness_indicators/tutorial_utils0% | 0% | 0% | 31% | 68%
fairness_indicators/remediation0% | 0% | 0% | 36% | 63%
ROOT0% | 0% | 0% | 100% | 0%
tensorboard_plugin0% | 0% | 0% | 100% | 0%
tensorboard_plugin/tensorboard_plugin_fairness_indicators0% | 0% | 0% | 0% | 100%
fairness_indicators0% | 0% | 0% | 0% | 100%
tensorboard_plugin/tensorboard_plugin_fairness_indicators/static0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def _convert_comments_data_tfrecord()
in fairness_indicators/tutorial_utils/util.py
 24 8 2
def create_percentage_difference_dictionary()
in fairness_indicators/remediation/weight_utils.py
 15 6 3
10 6 3
def select_constraint()
in tensorboard_plugin/setup.py
 10 6 3
def _convert_comments_data_csv()
in fairness_indicators/tutorial_utils/util.py
 21 5 2
def _get_metric_value()
in fairness_indicators/remediation/weight_utils.py
 15 5 4
def get_baseline_value()
in fairness_indicators/remediation/weight_utils.py
 11 5 3
def _get_evaluation_result()
in tensorboard_plugin/tensorboard_plugin_fairness_indicators/plugin.py
 17 5 2
def convert_comments_data()
in fairness_indicators/tutorial_utils/util.py
 11 4 2
def get_eval_results()
in fairness_indicators/tutorial_utils/util.py
 21 3 6
def _get_evaluation_result_from_remote_path()
in tensorboard_plugin/tensorboard_plugin_fairness_indicators/plugin.py
 17 3 2
def _get_output_file_format()
in tensorboard_plugin/tensorboard_plugin_fairness_indicators/plugin.py
 5 2 2
def train_model()
in fairness_indicators/example_model.py
 26 1 6
def evaluate_model()
in fairness_indicators/example_model.py
 32 1 6
def __init__()
in tensorboard_plugin/tensorboard_plugin_fairness_indicators/plugin.py
 2 1 2
def get_plugin_apps()
in tensorboard_plugin/tensorboard_plugin_fairness_indicators/plugin.py
 11 1 1
def frontend_metadata()
in tensorboard_plugin/tensorboard_plugin_fairness_indicators/plugin.py
 7 1 1
def is_active()
in tensorboard_plugin/tensorboard_plugin_fairness_indicators/plugin.py
 4 1 1
def _serve_js()
in tensorboard_plugin/tensorboard_plugin_fairness_indicators/plugin.py
 6 1 2
def _serve_vulcanized_js()
in tensorboard_plugin/tensorboard_plugin_fairness_indicators/plugin.py
 5 1 2