한국정보과학회 학술발표논문집, KCC 2022
paper
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config
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data_utils
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dataset
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emse-data
folder data model/hybrid checkpoints, data, eval, config.yaml, default.yaml, log.txt train train_ast.json, train.token.ast, train.token.code, train.token.nl test test_ast.json, test.token.ast, test.token.code, test.token.nl valid valid_ast.json, valid.token.ast, valid.token.code, valid.token.nl vocab... vocab.ast, vocab.code, vocab.nl - code
public int entrySize(Object key, Object value) throws lllegalArgumentException{ if (value==Token . TOMBSTONE) { return NUM_;} int size = HeapLRUCapacityController .this .getPerEntryOverhead(); size += sizeof (key); size += sizeof(value); return size; } >>==================================================================================================================================================================================================================================================================================== public int entrySize ( Object key , Object value ) throws IllegalArgumentException { if ( value == Token . TOMBSTONE ) { return NUM_ ; } int size = HeapLRUCapacityController . this . getPerEntryOverhead ( ) ; size += sizeof ( key ) ; size += sizeof ( value ) ; return size ; } >>====================================================================================================================================================================================================================================================================================
- ast.json
[{"id": 0, "type": "MethodDeclaration", "children": [1, 2, 4, 6, 13, 18, 23, 28], "value": "entrySize"}, {"id": 1, "type": "BasicType", "value": "int"}, {"id": 2, "type": "FormalParameter", "children": [3], "value": "key"}, {"id": 3, "type": "ReferenceType", "value": "Object"}, {"id": 4, "type": "FormalParameter", "children": [5], "value": "value"}, {"id": 5, "type": "ReferenceType", "value": "Object"}, {"id": 6, "type": "IfStatement", "children": [7, 10]}, {"id": 7, "type": "BinaryOperation", "children": [8, 9]}, {"id": 8, "type": "MemberReference", "value": "value"}, {"id": 9, "type": "MemberReference", "value": "Token.TOMBSTONE"}, {"id": 10, "type": "BlockStatement", "children": [11], "value": "None"}, {"id": 11, "type": "ReturnStatement", "children": [12], "value": "return"}, {"id": 12, "type": "MemberReference", "value": "NUM_"}, {"id": 13, "type": "LocalVariableDeclaration", "children": [14, 15], "value": "int"}, {"id": 14, "type": "BasicType", "value": "int"}, {"id": 15, "type": "VariableDeclarator", "children": [16], "value": "size"}, {"id": 16, "type": "This", "children": [17], "value": "HeapLRUCapacityController.this.getPerEntryOverhead"}, {"id": 17, "type": "MethodInvocation", "value": "."}, {"id": 18, "type": "StatementExpression", "children": [19]}, {"id": 19, "type": "Assignment", "children": [20, 21]}, {"id": 20, "type": "MemberReference", "value": "size"}, {"id": 21, "type": "MethodInvocation", "children": [22], "value": "sizeof"}, {"id": 22, "type": "MemberReference", "value": "key"}, {"id": 23, "type": "StatementExpression", "children": [24]}, {"id": 24, "type": "Assignment", "children": [25, 26]}, {"id": 25, "type": "MemberReference", "value": "size"}, {"id": 26, "type": "MethodInvocation", "children": [27], "value": "sizeof"}, {"id": 27, "type": "MemberReference", "value": "value"}, {"id": 28, "type": "ReturnStatement", "children": [29], "value": "return"}, {"id": 29, "type": "MemberReference", "value": "size"}]- token.ast
( MethodDeclaration ( BasicType ) BasicType ( FormalParameter ( ReferenceType ) ReferenceType ) FormalParameter ( FormalParameter ( ReferenceType ) ReferenceType ) FormalParameter ( IfStatement ( BinaryOperation ( MemberReference ) MemberReference ( MemberReference ) MemberReference ) BinaryOperation ( BlockStatement ( ReturnStatement ( MemberReference ) MemberReference ) ReturnStatement ) BlockStatement ) IfStatement ( LocalVariableDeclaration ( BasicType ) BasicType ( VariableDeclarator ( This ( MethodInvocation ) MethodInvocation ) This ) VariableDeclarator ) LocalVariableDeclaration ( StatementExpression ( Assignment ( MemberReference ) MemberReference ( MethodInvocation ( MemberReference ) MemberReference ) MethodInvocation ) Assignment ) StatementExpression ( StatementExpression ( Assignment ( MemberReference ) MemberReference ( MethodInvocation ( MemberReference ) MemberReference ) MethodInvocation ) Assignment ) StatementExpression ( ReturnStatement ( MemberReference ) MemberReference ) ReturnStatement ) MethodDeclaration
- token.nl
"as far as we re concerned all entries have the same size" -
img
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projects
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scripts
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source code
- __ main __ .py
- beam_search.py
- evaluations.py
- models.py
- rnn.py
- seq2seq_model.py
- translation_model.py
- utils.py
- python3 main.py config.yaml --train -v
- config.yaml 에서 hyper parameter 수정 가능
- 적절하게 수행
parser.add_argument('config', help='load a configuration file in the YAML format')
parser.add_argument('-v', '--verbose', action='store_true', help='verbose mode')
parser.add_argument('--debug', action='store_true', help='debug mode')
# using 'store_const' instead of 'store_true' so that the default value is `None` instead of `False`
parser.add_argument('--reset', action='store_const', const=True, help="reset model (don't load any checkpoint)")
parser.add_argument('--reset-learning-rate', action='store_const', const=True, help='reset learning rate')
parser.add_argument('--learning-rate', type=float, help='custom learning rate (triggers `reset-learning-rate`)')
parser.add_argument('--purge', action='store_true', help='remove previous model files')- python3 main.py config.yaml --decode “data_dir”
- data_dir : ast.json, token.ast, token.code, token.nl 있는 폴더
- python3 main.py config.yaml --eval “data_dir”
- data_dir : ast.json, token.ast, token.code, token.nl 있는 폴더
- python3 get_ast.py source.code ast.json
// code
public boolean doesNotHaveIds (){
return getIds () == null || getIds ().getIds().isEmpty();
}// AST
[
{"id": 0, "type": "MethodDeclaration", "children": [1, 2], "value": "doesNotHaveIds"},
{"id": 1, "type": "BasicType", "value": "boolean"},
{"id": 2, "type": "ReturnStatement", "children": [3], "value": "return"},
{"id": 3, "type": "BinaryOperation", "children": [4, 7]},
{"id": 4, "type": "BinaryOperation", "children": [5, 6]},
{"id": 5, "type": "MethodInvocation", "value": "getIds"},
{"id": 6, "type": "Literal", "value": "null"},
{"id": 7, "type": "MethodInvocation", "children": [8, 9], "value": "getIds"},
{"id": 8, "type": "MethodInvocation", "value": "."},
{"id": 9, "type": "MethodInvocation", "value": "."}
]- python3 ast_traversal.py
- def get_sbt_structrue
// SBT
( MethodDeclaration ( BasicType ) BasicType ( FormalParameter ( ReferenceType ) ReferenceType ) FormalParameter ( FormalParameter ( ReferenceType ) ReferenceType ) FormalParameter ( IfStatement ( BinaryOperation ( MemberReference ) MemberReference ( MemberReference ) MemberReference ) BinaryOperation ( BlockStatement ( ReturnStatement ( MemberReference ) MemberReference ) ReturnStatement ) BlockStatement ) IfStatement ( LocalVariableDeclaration ( BasicType ) BasicType ( VariableDeclarator ( This ( MethodInvocation ) MethodInvocation ) This ) VariableDeclarator ) LocalVariableDeclaration ( StatementExpression ( Assignment ( MemberReference ) MemberReference ( MethodInvocation ( MemberReference ) MemberReference ) MethodInvocation ) Assignment ) StatementExpression ( StatementExpression ( Assignment ( MemberReference ) MemberReference ( MethodInvocation ( MemberReference ) MemberReference ) MethodInvocation ) Assignment ) StatementExpression ( ReturnStatement ( MemberReference ) MemberReference ) ReturnStatement ) MethodDeclaration- python3 ast_traversal.py
- def get_sbtcode_structure
// SBT + CODE
( MethodDeclaration entrySize ( BasicType int ) BasicType ( FormalParameter key ( ReferenceType Object ) ReferenceType ) FormalParameter ( FormalParameter value ( ReferenceType Object ) ReferenceType ) FormalParameter ( IfStatement if ( BinaryOperation ( MemberReference value ) MemberReference ( MemberReference Token.TOMBSTONE ) MemberReference ) BinaryOperation ( BlockStatement { ( ReturnStatement return ( MemberReference NUM_ ) MemberReference ) ReturnStatement ) BlockStatement ) IfStatement ( LocalVariableDeclaration int ( BasicType int ) BasicType ( VariableDeclarator size ( This HeapLRUCapacityController.this.getPerEntryOverhead ( MethodInvocation . ) MethodInvocation ) This ) VariableDeclarator ) LocalVariableDeclaration ( StatementExpression size ( Assignment ( MemberReference size ) MemberReference ( MethodInvocation sizeof ( MemberReference key ) MemberReference ) MethodInvocation ) Assignment ) StatementExpression ( StatementExpression size ( Assignment ( MemberReference size ) MemberReference ( MethodInvocation sizeof ( MemberReference value ) MemberReference ) MethodInvocation ) Assignment ) StatementExpression ( ReturnStatement return ( MemberReference size ) MemberReference ) ReturnStatement ) MethodDeclaration-
config_default.yaml
# SGD parameters learning_rate: 0.5 sgd_learning_rate: 1.0 learning_rate_decay_factor: 0.99 # training parameters max_gradient_norm: 5.0 steps_per_checkpoint: 2000 steps_per_eval: 2000 eval_burn_in: 0 max_steps: 0 max_epochs: 50 keep_best: 5 feed_previous: 0.0 optimizer: sgd moving_average: null # batch iteration parameters batch_size: 100 batch_mode: random shuffle: True read_ahead: 1 reverse_input: True # model (each one of these settings can be defined specifically in 'encoders' and 'decoders', or generally here) cell_size: 512 embedding_size: 512 attn_size: 256 layers: 1 cell_type: LSTM character_level: False truncate_lines: True # data max_train_size: 0 max_dev_size: 0 max_test_size: 0 data_dir: ../emse-data(ast_only) model_dir: ../emse-data(ast_only)/model/default train_prefix: train script_dir: scripts dev_prefix: test vocab_prefix: vocab checkpoints: [] # decoding score_function: nltk_sentence_bleu post_process_script: null remove_unk: False beam_size: 1 # general **encoders: - name: ast max_len: 500 attention_type: global decoders: - name: nl max_len: 30**
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성능 확인
# 우리가 테스트 한 것 step 222000 **epoch 50** learning rate 0.306 step-time 0.791 loss 10.135 test eval: loss 36.39 starting decoding test avg_score=0.2019(**BLEU**) test 폴더 - avg_score: 0.2026 # 논문 결과 BLEU: 38.17
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config.yaml
# SGD parameters learning_rate: 0.5 # initial learning rate sgd_learning_rate: 1.0 # SGD can start at a different learning rate (useful for switching between Adam and SGD) learning_rate_decay_factor: 0.99 # decay the learning rate by this factor at a given frequency # training parameters max_gradient_norm: 5.0 # clip gradients to this norm (prevents exploding gradient) steps_per_checkpoint: 2000 # number of SGD updates between each checkpoint steps_per_eval: 2000 # number of SGD updates between each BLEU eval (on dev set) eval_burn_in: 0 # minimum number of updates before starting BLEU eval max_steps: 0 # maximum number of updates before stopping, 600000->0 max_epochs: 50 # maximum number of epochs before stopping, 100->50 keep_best: 5 # number of best checkpoints to keep (based on BLEU score on dev set) feed_previous: 0.0 # randomly feed prev output instead of ground truth to decoder during training ([0,1] proba) optimizer: sgd # which training algorithm to use ('sgd', 'adadelta', or 'adam') moving_average: null # TODO # batch iteration parameters batch_size: 128 # batch size (during training and greedy decoding), 64->128 batch_mode: standard # standard (cycle through train set) or random (sample from train set) shuffle: True # shuffle dataset at each new epoch read_ahead: 1 # number of batches to read ahead and sort by sequence length (can speed up training) reverse_input: True # reverse input sequences # model (each one of these settings can be defined specifically in 'encoders' and 'decoders', or generally here) cell_size: 256 # size of the RNN cells embedding_size: 256 # size of the embeddings attn_size: 128 # size of the attention layer layers: 1 # number of RNN layers per encoder and decoder cell_type: GRU # LSTM, GRU, DropoutGRU character_level: False # character-level sequences truncate_lines: True # if True truncate lines which are too long, otherwise just drop them # encoder settings bidir: False # use bidirectional encoders train_initial_states: True # whether the initial states of the encoder should be trainable parameters bidir_projection: False # project bidirectional encoder states to cell_size (or just keep the concatenation) time_pooling: null # perform time pooling (skip states) between the layers of the encoder (list of layers - 1 ratios) pooling_avg: True # average or skip consecutive states binary: False # use binary input for the encoder (no vocab and no embeddings, see utils.read_binary_features) attn_filters: 0 attn_filter_length: 0 input_layers: null # list of fully connected layer sizes, applied before the encoder attn_temperature: 1.0 # 1.0: true softmax (low values: uniform distribution, high values: argmax) final_state: last # last (default), concat_last, average highway_layers: 0 # number of highway layers before the encoder (after convolutions and maxout) # decoder settings tie_embeddings: False # use transpose of the embedding matrix for output projection (requires 'output_extra_proj') use_previous_word: True # use previous word when predicting a new word attn_prev_word: False # use the previous word in the attention model softmax_temperature: 1.0 # TODO: temperature of the output softmax pred_edits: False # output is a sequence of edits, apply those edits before decoding/evaluating conditional_rnn: False # two-layer decoder, where the 1st layer is used for attention, and the 2nd layer for prediction generate_first: True # generate next word before updating state (look->generate->update) update_first: False # update state before looking and generating next word rnn_feed_attn: True # feed attention context to the RNN's transition fonction use_lstm_full_state: False # use LSTM's full state for attention and next word prediction pred_embed_proj: True # project decoder output to embedding size before projecting to vocab size pred_deep_layer: False # add a non-linear transformation just before softmax pred_maxout_layer: True # use a maxout layer just before the vocabulary projection and softmax aggregation_method: sum # how to combine the attention contexts of multiple encoders (concat, sum) # data max_train_size: 0 # maximum size of the training data (0 for unlimited) max_dev_size: 0 # maximum size of the dev data max_test_size: 0 # maximum size of the test data data_dir: ../emse-data(original) # directory containing the training data model_dir: ../emse-data(original)/model/hybrid train_prefix: train # name of the training corpus script_dir: scripts # directory where the scripts are kepts (in particular the scoring scripts) dev_prefix: test # names of the development corpora vocab_prefix: vocab # name of the vocabulary files checkpoints: [] # list of checkpoints to load (in this specific order) after main checkpoint # decoding score_function: nltk_sentence_bleu # name of the main scoring function, inside 'evaluation.py' (used for selecting models) post_process_script: null # path to post-processing script (called before evaluating) remove_unk: False # remove UNK symbols from the decoder output beam_size: 5 # beam size for decoding (decoder is greedy by default) ensemble: False # use an ensemble of models while decoding (specified by the --checkpoints parameter) output: null # output file for decoding (writes to standard output by default) len_normalization: 1.0 # length normalization coefficient used in beam-search decoder early_stopping: True # reduce beam-size each time a finished hypothesis is encountered (affects decoding speed) raw_output: False # output translation hypotheses without any post-processing average: False # like ensemble, but instead of averaging the log-probs, average all parameters pred_edits: False # general **encoders: # this is a list (you can specify several encoders) - name: code # each encoder or decoder has a name (used for naming variables) and an extension (for files) max_len: 200 # max_len of api attention_type: global - name: ast max_len: 500 attention_type: global decoders: # Each encoder or decoder can redefine its own values for a number of parameters, - name: nl # including `cell_size`, `embedding_size` and `attn_size` max_len: 30**
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성능 확인
# 우리가 테스트 한 것 step 174000 **epoch 50** learning rate 0.306 step-time 0.951 loss 8.083 test eval: loss 33.39 test avg_score=0.3806(**BLEU**) test 폴더 - avg_score:0.3820
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config_park.yaml
# 위 항목 deepcom(default)와 동일 # data max_train_size: 0 # maximum size of the training data (0 for unlimited) max_dev_size: 0 # maximum size of the dev data max_test_size: 0 # maximum size of the test data data_dir: ../emse-data(sbt_code) # directory containing the training data model_dir: ../emse-data(sbt_code)/model/default train_prefix: train # name of the training corpus script_dir: scripts # directory where the scripts are kepts (in particular the scoring scripts) dev_prefix: test # names of the development corpora vocab_prefix: vocab # name of the vocabulary files checkpoints: [] # list of checkpoints to load (in this specific order) after main checkpoint # general **encoders: # this is a list (you can specify several encoders) - name: ast # each encoder or decoder has a name (used for naming variables) and an extension (for files) -> 'code' max_len: 500 # max_len of api -> '200' attention_type: global decoders: # Each encoder or decoder can redefine its own values for a number of parameters, - name: nl # including `cell_size`, `embedding_size` and `attn_size` max_len: 30**
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성능 확인
# 우리가 테스트 한 것 step 222000 epoch 50 learning rate 0.306 step-time 1.031 loss 10.060 test eval: loss 36.92 starting decoding test avg_score=0.1629**(BLUE)** test 폴더 - avg_score: 0.1626
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config_hybrid_park.yaml
# 위 항목 hybrid-deepcom(default)와 동일 # data max_train_size: 0 # maximum size of the training data (0 for unlimited) max_dev_size: 0 # maximum size of the dev data max_test_size: 0 # maximum size of the test data data_dir: ../emse-data(sbt_code_hb) # directory containing the training data model_dir: ../emse-data(sbt_code_hb)/model/hybrid train_prefix: train # name of the training corpus script_dir: scripts # directory where the scripts are kepts (in particular the scoring scripts) dev_prefix: test # names of the development corpora vocab_prefix: vocab # name of the vocabulary files checkpoints: [] # list of checkpoints to load (in this specific order) after main checkpoint # decoding score_function: nltk_sentence_bleu # name of the main scoring function, inside 'evaluation.py' (used for selecting models) post_process_script: null # path to post-processing script (called before evaluating) remove_unk: False # remove UNK symbols from the decoder output beam_size: 5 # beam size for decoding (decoder is greedy by default) ensemble: False # use an ensemble of models while decoding (specified by the --checkpoints parameter) output: null # output file for decoding (writes to standard output by default) len_normalization: 1.0 # length normalization coefficient used in beam-search decoder early_stopping: True # reduce beam-size each time a finished hypothesis is encountered (affects decoding speed) raw_output: False # output translation hypotheses without any post-processing average: False # like ensemble, but instead of averaging the log-probs, average all parameters pred_edits: False # general **encoders: # this is a list (you can specify several encoders) - name: code # each encoder or decoder has a name (used for naming variables) and an extension (for files) max_len: 200 # max_len of api attention_type: global - name: ast max_len: 500 attention_type: global decoders: # Each encoder or decoder can redefine its own values for a number of parameters, - name: nl # including `cell_size`, `embedding_size` and `attn_size` max_len: 30**
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성능 확인
step 174000 epoch 50 learning rate 0.306 step-time 1.163 loss 8.117 test eval: loss 33.40 starting decoding test avg_score=0.3788**(BLEU)** test 폴더 - avg_score: 0.3798
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config_park_simSBT.yaml
# 위 항목 deepcom(default)와 동일 # data max_train_size: 0 # maximum size of the training data (0 for unlimited) max_dev_size: 0 # maximum size of the dev data max_test_size: 0 # maximum size of the test data data_dir: ../emse-data(simsbt_code) # directory containing the training data model_dir: ../emse-data(simsbt_code)/model/default train_prefix: train # name of the training corpus script_dir: scripts # directory where the scripts are kepts (in particular the scoring scripts) dev_prefix: test # names of the development corpora vocab_prefix: vocab # name of the vocabulary files checkpoints: [] # list of checkpoints to load (in this specific order) after main checkpoint # general **encoders: # this is a list (you can specify several encoders) - name: ast # each encoder or decoder has a name (used for naming variables) and an extension (for files) -> 'code' max_len: 500 # max_len of api -> '200' attention_type: global decoders: # Each encoder or decoder can redefine its own values for a number of parameters, - name: nl # including `cell_size`, `embedding_size` and `attn_size` max_len: 30**
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성능 확인
step 222000 epoch 50 learning rate 0.306 step-time 0.620 loss 9.092 test eval: loss 36.50 starting decoding test avg_score=0.2424**(BLEU)** test 폴더 - avg_score: 0.2417
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config_hybrid_park_simSBT.yaml
# 위 항목 hybrid-deepcom(default)와 동일 # data max_train_size: 0 # maximum size of the training data (0 for unlimited) max_dev_size: 0 # maximum size of the dev data max_test_size: 0 # maximum size of the test data data_dir: ../emse-data(simsbt_code_hb) # directory containing the training data model_dir: ../emse-data(simsbt_code_hb)/model/hybrid train_prefix: train # name of the training corpus script_dir: scripts # directory where the scripts are kepts (in particular the scoring scripts) dev_prefix: test # names of the development corpora vocab_prefix: vocab # name of the vocabulary files checkpoints: [] # list of checkpoints to load (in this specific order) after main checkpoint # decoding score_function: nltk_sentence_bleu # name of the main scoring function, inside 'evaluation.py' (used for selecting models) post_process_script: null # path to post-processing script (called before evaluating) remove_unk: False # remove UNK symbols from the decoder output beam_size: 5 # beam size for decoding (decoder is greedy by default) ensemble: False # use an ensemble of models while decoding (specified by the --checkpoints parameter) output: null # output file for decoding (writes to standard output by default) len_normalization: 1.0 # length normalization coefficient used in beam-search decoder early_stopping: True # reduce beam-size each time a finished hypothesis is encountered (affects decoding speed) raw_output: False # output translation hypotheses without any post-processing average: False # like ensemble, but instead of averaging the log-probs, average all parameters pred_edits: False # general **encoders: # this is a list (you can specify several encoders) - name: code # each encoder or decoder has a name (used for naming variables) and an extension (for files) max_len: 200 # max_len of api attention_type: global - name: ast max_len: 500 attention_type: global decoders: # Each encoder or decoder can redefine its own values for a number of parameters, - name: nl # including `cell_size`, `embedding_size` and `attn_size` max_len: 30**
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성능 확인
step 174000 epoch 50 learning rate 0.306 step-time 0.756 loss 7.984 test eval: loss 33.54 starting decoding test avg_score=0.3848**(BLEU)** test 폴더 - avg_score: 0.3849
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Training Details
data_RQ1 데이터 사용하지 않고 오리지널 데이터 사용해 학습
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성능 확인
After 30000 steps, BLEU in test: 0.32178
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Training Details
data_RQ1 데이터 사용하지 않고 오리지널 데이터 사용해 학습
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성능 확인
After 501100 steps rate is 0.00001 cost is 0.00151 In iterator: 229. nowCBleu: 0.41729 maxCBlue: 0.41747 nowSBleu: 0.44359 maxSBlue: 0.44359
| Deepcom | Deepcom(sbtcode) | H-Deepcom | H-Deepcom(sbtcode) | DeepCom(our model-sim SBTcode) | H-DeepCom(our model-sim SBTcode) | seCNN(default) | seTransformer | |
|---|---|---|---|---|---|---|---|---|
| BLEU | 0.2026 | 0.1626 | 0.3820 | 0.3798 | 0.2417 | 0.3849 | 0.32178 | 0.44359 |
| METEOR | 0.3172 | 0.2741 | 0.5126 | 0.5105 | 0.3543 | 0.5164 |