amazon-research / transformer-gan
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
  • 9% duplication:
    • 5,002 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
    • 477 duplicated lines
  • 30 duplicates
system9% (477 lines)
Duplication per Extension
py8% (396 lines)
yml38% (81 lines)
Duplication per Component (primary)
model9% (264 lines)
model/utils6% (56 lines)
model/training_config29% (49 lines)
model/inference_config69% (32 lines)
BERT4% (29 lines)
metrics20% (29 lines)
data5% (18 lines)
ROOT0% (0 lines)

Duplication Between Components (50+ lines)

G BERT BERT metrics metrics BERT--metrics 58

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Longest Duplicates
The list of 20 longest duplicates.
See data for all 30 duplicates...
Size#FoldersFilesLinesCode
19 x 2 model
model
lamb.py
lamb.py
 52:83 (16%)
175:206 (16%)
 view
16 x 2 model/training_config
model/training_config
experiment_baseline.yml
experiment_spanbert.yml
 3:18 (35%)
3:18 (21%)
 view
16 x 2 model/training_config
model/training_config
experiment_cnn.yml
experiment_spanbert.yml
 4:19 (36%)
4:19 (21%)
 view
15 x 2 model/training_config
model/training_config
experiment_baseline.yml
experiment_cnn.yml
 4:18 (33%)
4:18 (34%)
 view
15 x 2 BERT
metrics
main.py
bert_score.py
 499:515 (2%)
185:202 (10%)
 view
14 x 2 model
model
lamb.py
lamb.py
 20:49 (12%)
143:172 (12%)
 view
14 x 2 model
model
train.py
train.py
 1221:1234 (1%)
1239:1252 (1%)
 view
13 x 2 model/utils
model/utils
bleu.py
classifier.py
 45:61 (14%)
19:35 (8%)
 view
13 x 2 model
model
mem_transformer.py
mem_transformer.py
 580:597 (2%)
630:646 (2%)
 view
12 x 2 model
model
data_utils.py
data_utils.py
 211:222 (2%)
308:321 (2%)
 view
10 x 2 model/inference_config
model/inference_config
inference_conditional.yml
inference_unconditional.yml
 1:10 (43%)
1:10 (43%)
 view
10 x 2 model
model
data_utils.py
data_utils.py
 224:234 (2%)
324:334 (2%)
 view
9 x 2 model
model
data_utils.py
data_utils.py
 284:293 (2%)
356:365 (2%)
 view
8 x 2 model
model
train.py
train.py
 909:916 (<1%)
1080:1087 (<1%)
 view
8 x 2 BERT
metrics
main.py
bert_score.py
 50:61 (1%)
49:60 (5%)
 view
8 x 2 model
model
train.py
train.py
 1239:1246 (<1%)
1259:1266 (<1%)
 view
8 x 2 model
model
train.py
train.py
 1221:1228 (<1%)
1259:1266 (<1%)
 view
8 x 2 model/utils
model/utils
adaptive_softmax.py
proj_adaptive_softmax.py
 56:66 (13%)
117:127 (7%)
 view
7 x 2 model
model
data_utils.py
data_utils.py
 217:223 (1%)
375:381 (1%)
 view
7 x 2 model/utils
model/utils
classifier.py
classifier.py
 153:161 (4%)
190:198 (4%)
 view