facebookresearch / colorlessgreenRNNs
Unit Size

The distribution of size of units (measured in lines of code).

Intro
  • Unit size measurements show the distribution of size of units of code (methods, functions...).
  • Units are classified in four categories based on their size (lines of code): 1-20 (small units), 20-50 (medium size units), 51-100 (long units), 101+ (very long units).
  • You should aim at keeping units small (< 20 lines). Long units may become "bloaters", code that have increased to such gargantuan proportions that they are hard to work with.
Learn more...
Unit Size Overall
  • There are 111 units with 1,175 lines of code in units (66.5% of code).
    • 0 very long units (0 lines of code)
    • 1 long units (59 lines of code)
    • 12 medium size units (375 lines of code)
    • 28 small units (394 lines of code)
    • 70 very small units (347 lines of code)
0% | 5% | 31% | 33% | 29%
Legend:
101+
51-100
21-50
11-20
1-10
Unit Size per Extension
101+
51-100
21-50
11-20
1-10
py0% | 5% | 31% | 33% | 29%
Unit Size per Logical Component
primary logical decomposition
101+
51-100
21-50
11-20
1-10
src/syntactic_testsets0% | 7% | 31% | 31% | 28%
src/language_models0% | 0% | 32% | 39% | 27%
src/data/hebrew0% | 0% | 100% | 0% | 0%
src/data0% | 0% | 0% | 48% | 51%
src0% | 0% | 0% | 0% | 100%
Alternative Visuals
Longest Units
Top 20 longest units
Unit# linesMcCabe index# params
def main()
in src/syntactic_testsets/extract_dependency_patterns.py
 59 27 0
def main()
in src/syntactic_testsets/generate_nonsense.py
 46 7 0
def from_sentence()
in src/syntactic_testsets/tree_module.py
 45 10 3
def generate_morph_pattern_test()
in src/syntactic_testsets/generate_nonsense.py
 39 15 5
def remove_segmented_morphemes_hebrew()
in src/data/hebrew/preprocess_HebrewUD_morph.py
 37 9 1
def extract_sent_features()
in src/syntactic_testsets/utils.py
 34 12 4
def find_good_patterns()
in src/syntactic_testsets/extract_dependency_patterns.py
 33 18 2
def train()
in src/language_models/main.py
 25 5 0
def evaluate_perplexity()
in src/language_models/ngram_lstm.py
 24 4 2
def train()
in src/language_models/ngram_lstm.py
 24 5 0
def evaluate()
in src/language_models/evaluate_test_perplexity.py
 23 3 1
def generate_context()
in src/syntactic_testsets/generate_nonsense.py
 23 15 3
def pprint()
in src/syntactic_testsets/tree_module.py
 22 9 3
def tokenize()
in src/language_models/dictionary_corpus.py
 19 5 2
def accuracy()
in src/syntactic_testsets/conll_utils.py
 19 7 0
def create_corpus()
in src/data/data_vocab_prep.py
 19 4 3
def grep_morph_pattern()
in src/syntactic_testsets/extract_dependency_patterns.py
 18 14 5
def morph_contexts_frequencies()
in src/syntactic_testsets/extract_dependency_patterns.py
 16 11 2
def read_sentences_from_columns()
in src/syntactic_testsets/conll_utils.py
 16 7 1
def evaluate_on_mask()
in src/language_models/ngram_lstm.py
 15 4 2