facebookresearch / UNLU
Duplication

Places in code with 6 or more lines that are exactly the same.

Intro
  • For duplication, we look at places in code where there are 6 or more lines of code that are exactly the same.
  • Before duplication is calculated, the code is cleaned to remove empty lines, comments, and frequently duplicated constructs such as imports.
  • You should aim at having as little as possible (<5%) of duplicated code as high-level of duplication can lead to maintenance difficulties, poor factoring, and logical contradictions.
Learn more...
Duplication Overall
  • 58% duplication:
    • 9,251 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
    • 5,399 duplicated lines
  • 435 duplicates
system58% (5,399 lines)
Duplication per Extension
py59% (5,335 lines)
yaml30% (64 lines)
Duplication per Component (primary)
anli/src77% (2,782 lines)
codes/rnn_training74% (749 lines)
infersent_comp64% (583 lines)
infersent_comp/encoder92% (554 lines)
codes18% (352 lines)
dataset_utils87% (272 lines)
ROOT29% (76 lines)
dataset_utils/mnli19% (15 lines)
dataset_utils/snli19% (9 lines)
dataset_utils/ocnli20% (7 lines)
anli0% (0 lines)
utils0% (0 lines)
infersent_comp/dataset0% (0 lines)

Duplication Between Components (50+ lines)

G infersent_comp/encoder infersent_comp/encoder infersent_comp infersent_comp infersent_comp/encoder--infersent_comp 1108 codes codes codes/rnn_training codes/rnn_training codes--codes/rnn_training 229 dataset_utils dataset_utils codes--dataset_utils 200 codes/rnn_training--infersent_comp 69 anli/src anli/src anli/src--codes 111

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Longest Duplicates
The list of 20 longest duplicates.
See data for all 435 duplicates...
Size#FoldersFilesLinesCode
554 x 2 infersent_comp/encoder
infersent_comp
models.py
models.py
 18:894 (100%)
18:894 (100%)
 view
370 x 2 anli/src/nli
anli/src/nli
train_with_confidence.py
training.py
 64:502 (41%)
58:496 (40%)
 view
275 x 2 anli/src/nli
anli/src/nli
train_with_confidence.py
training.py
 596:934 (30%)
501:839 (30%)
 view
205 x 2 anli/src/nli
anli/src/nli
train_with_scramble.py
training.py
 163:415 (20%)
173:425 (22%)
 view
205 x 2 anli/src/nli
anli/src/nli
train_with_confidence.py
train_with_scramble.py
 179:431 (22%)
163:415 (20%)
 view
167 x 2 anli/src/nli
anli/src/nli
train_with_scramble.py
training.py
 1040:1253 (16%)
928:1142 (18%)
 view
105 x 2 anli/src/nli
anli/src/nli
train_with_confidence.py
training.py
 1019:1151 (11%)
1030:1162 (11%)
 view
104 x 2 anli/src/nli
anli/src/nli
train_with_scramble.py
training.py
 738:861 (10%)
680:803 (11%)
 view
104 x 2 anli/src/nli
anli/src/nli
train_with_confidence.py
train_with_scramble.py
 775:898 (11%)
738:861 (10%)
 view
89 x 2 anli/src/nli
anli/src/nli
train_with_scramble.py
training.py
 25:141 (9%)
27:142 (9%)
 view
86 x 2 anli/src/nli
anli/src/nli
train_with_confidence.py
train_with_scramble.py
 1019:1131 (9%)
1141:1253 (8%)
 view
85 x 2 anli/src/nli
anli/src/nli
train_with_scramble.py
training.py
 477:591 (8%)
476:590 (9%)
 view
82 x 2 anli/src/nli
anli/src/nli
train_with_confidence.py
train_with_scramble.py
 64:148 (9%)
57:141 (8%)
 view
76 x 2 codes/rnn_training
codes/rnn_training
train_nli_ray.py
train_nli_w2v.py
 145:229 (17%)
144:228 (19%)
 view
71 x 2 codes/rnn_training
codes/rnn_training
train_nli_ray.py
train_nli_w2v.py
 52:143 (16%)
52:143 (17%)
 view
69 x 2 anli/src/nli
anli/src/nli
train_with_confidence.py
train_with_scramble.py
 596:685 (7%)
502:591 (7%)
 view
56 x 2 dataset_utils
dataset_utils
data_iterator.py
data_iterator.py
 86:145 (18%)
222:281 (18%)
 view
52 x 2 codes/rnn_training
codes/rnn_training
train_nli_ray.py
train_nli_w2v.py
 233:296 (12%)
231:294 (13%)
 view
44 x 2 anli/src/nli
anli/src/nli
train_with_scramble.py
training.py
 975:1036 (4%)
863:924 (4%)
 view
40 x 2 anli/src/nli
anli/src/nli
train_with_confidence.py
train_with_scramble.py
 719:764 (4%)
665:710 (4%)
 view
Duplicated Units
The list of top 20 duplicated units.
See data for all 47 unit duplicates...
Size#FoldersFilesLinesCode
143 x 2 anli/src/nli
anli/src/nli
train_with_confidence.py
training.py
 0:0 
0:0 
 view
47 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
53 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
36 x 3 anli/src/nli
anli/src/nli
anli/src/nli
train_with_confidence.py
train_with_scramble.py
training.py
 0:0 
0:0 
0:0 
 view
25 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
35 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
37 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
24 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
29 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
20 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
20 x 3 anli/src/nli
anli/src/nli
anli/src/nli
train_with_confidence.py
train_with_scramble.py
training.py
 0:0 
0:0 
0:0 
 view
20 x 3 anli/src/nli
anli/src/nli
anli/src/nli
train_with_confidence.py
train_with_scramble.py
training.py
 0:0 
0:0 
0:0 
 view
21 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
20 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
27 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
17 x 3 anli/src/nli
anli/src/nli
anli/src/nli
train_with_confidence.py
train_with_scramble.py
training.py
 0:0 
0:0 
0:0 
 view
16 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
15 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
16 x 2 infersent_comp
infersent_comp/encoder
models.py
models.py
 0:0 
0:0 
 view
16 x 3 anli/src/nli
anli/src/nli
anli/src/nli
train_with_confidence.py
train_with_scramble.py
training.py
 0:0 
0:0 
0:0 
 view