facebookresearch / colorlessgreenRNNs
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 111 units with 1,175 lines of code in units (66.5% of code).
    • 0 very complex units (0 lines of code)
    • 1 complex units (59 lines of code)
    • 7 medium complex units (176 lines of code)
    • 13 simple units (270 lines of code)
    • 90 very simple units (670 lines of code)
0% | 5% | 14% | 22% | 57%
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% | 5% | 14% | 22% | 57%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
src/syntactic_testsets0% | 7% | 22% | 30% | 39%
src/data/hebrew0% | 0% | 0% | 100% | 0%
src/language_models0% | 0% | 0% | 0% | 100%
src/data0% | 0% | 0% | 0% | 100%
src0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def main()
in src/syntactic_testsets/extract_dependency_patterns.py
 59 27 0
def find_good_patterns()
in src/syntactic_testsets/extract_dependency_patterns.py
 33 18 2
def generate_morph_pattern_test()
in src/syntactic_testsets/generate_nonsense.py
 39 15 5
def generate_context()
in src/syntactic_testsets/generate_nonsense.py
 23 15 3
def grep_morph_pattern()
in src/syntactic_testsets/extract_dependency_patterns.py
 18 14 5
def extract_sent_features()
in src/syntactic_testsets/utils.py
 34 12 4
def morph_contexts_frequencies()
in src/syntactic_testsets/extract_dependency_patterns.py
 16 11 2
def read_blankline_block()
in src/syntactic_testsets/conll_utils.py
 13 11 1
def from_sentence()
in src/syntactic_testsets/tree_module.py
 45 10 3
def is_projective_arc()
in src/syntactic_testsets/tree_module.py
 9 9 2
def pprint()
in src/syntactic_testsets/tree_module.py
 22 9 3
def is_attr()
in src/syntactic_testsets/utils.py
 10 9 4
def remove_segmented_morphemes_hebrew()
in src/data/hebrew/preprocess_HebrewUD_morph.py
 37 9 1
def remove_node()
in src/syntactic_testsets/tree_module.py
 15 7 2
def load_trees_from_conll()
in src/syntactic_testsets/tree_module.py
 14 7 2
def read_sentences_from_columns()
in src/syntactic_testsets/conll_utils.py
 16 7 1
def accuracy()
in src/syntactic_testsets/conll_utils.py
 19 7 0
def choose_random_forms()
in src/syntactic_testsets/generate_nonsense.py
 15 7 6
def main()
in src/syntactic_testsets/generate_nonsense.py
 46 7 0
def __eq__()
in src/syntactic_testsets/tree_module.py
 7 6 2