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Code for the model presented in the paper: "code2seq: Generating Sequences from Structured Representations of Code"


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This is an official implementation of the model described in:

Uri Alon, Shaked Brody, Omer Levy and Eran Yahav, "code2seq: Generating Sequences from Structured Representations of Code" [PDF]

Appeared in ICLR'2019 (poster available here)

An online demo is available at

This is a TensorFlow implementation of the network, with Java and C# extractors for preprocessing the input code. It can be easily extended to other languages, since the TensorFlow network is agnostic to the input programming language (see Extending to other languages. Contributions are welcome.

See also:

  • Structural Language Models for Code (ICML'2020) is a new paper that learns to generate the missing code within a larger code snippet. This is similar to code completion, but is able to predict complex expressions rather than a single token at a time. See PDF, demo at
  • Adversarial Examples for Models of Code is a new paper that shows how to slightly mutate the input code snippet of code2vec and GNNs models (thus, introducing adversarial examples), such that the model (code2vec or GNNs) will output a prediction of our choice. See PDF (code: soon).
  • Neural Reverse Engineering of Stripped Binaries is a new paper that learns to predict procedure names in stripped binaries, thus use neural networks for reverse engineering. See PDF (code: soon).
  • code2vec (POPL'2019) is our previous model. It can only generate a single label at a time (rather than a sequence as code2seq), but it is much faster to train (because of its simplicity). See PDF, demo at and code.

Table of Contents


python3 -c 'import tensorflow as tf; print(tf.__version__)'


Step 0: Cloning this repository

git clone
cd code2seq

Step 1: Creating a new dataset from Java sources

To obtain a preprocessed dataset to train a network on, you can either download our preprocessed dataset, or create a new dataset from Java source files.

Download our preprocessed dataset Java-large dataset (~16M examples, compressed: 11G, extracted 125GB)

mkdir data
cd data
tar -xvzf java-large-preprocessed.tar.gz

This will create a data/java-large/ sub-directory, containing the files that hold training, test and validation sets, and a dict file for various dataset properties.

Creating and preprocessing a new Java dataset

To create and preprocess a new dataset (for example, to compare code2seq to another model on another dataset):

  • Edit the file using the instructions there, pointing it to the correct training, validation and test directories.
  • Run the file:


Step 2: Training a model

You can either download an already trained model, or train a new model using a preprocessed dataset.

Downloading a trained model (137 MB)

We already trained a model for 52 epochs on the data that was preprocessed in the previous step. This model is the same model that was used in the paper and the same model that serves the demo at

tar -xvzf java-large-model.tar.gz

This trained model is in a "released" state, which means that we stripped it from its training parameters.

Training a model from scratch

To train a model from scratch:

  • Edit the file to point it to the right preprocessed data. By default, it points to our "java-large" dataset that was preprocessed in the previous step.
  • Before training, you can edit the configuration hyper-parameters in the file, as explained in Configuration.
  • Run the script:

Step 3: Evaluating a trained model

After config.PATIENCE iterations of no improvement on the validation set, training stops by itself.

Suppose that iteration #52 is our chosen model, run:

python3 --load models/java-large-model/model_iter52.release --test data/java-large/java-large.test.c2s

While evaluating, a file named "log.txt" is written to the same dir as the saved models, with each test example name and the model's prediction.

Step 4: Manual examination of a trained model

To manually examine a trained model, run:

python3 --load models/java-large-model/model_iter52.release --predict

After the model loads, follow the instructions and edit the file and enter a Java method or code snippet, and examine the model's predictions and attention scores.


Due to TensorFlow's limitations, if using beam search (config.BEAM_WIDTH > 0), then BEAM_WIDTH hypotheses will be printed, but without attention weights. If not using beam search (config.BEAM_WIDTH == 0), then a single hypothesis will be printed with the attention weights in every decoding timestep.


Changing hyper-parameters is possible by editing the file

Here are some of the parameters and their description:

config.NUM_EPOCHS = 3000

The max number of epochs to train the model.


The frequency, in epochs, of saving a model and evaluating on the validation set during training.

config.PATIENCE = 10

Controlling early stopping: how many epochs of no improvement should training continue before stopping.

config.BATCH_SIZE = 512

Batch size during training.

config.TEST_BATCH_SIZE = 256

Batch size during evaluation. Affects only the evaluation speed and memory consumption, does not affect the results.

config.SHUFFLE_BUFFER_SIZE = 10000

The buffer size that the reader uses for shuffling the training data. Controls the randomness of the data. Increasing this value might hurt training throughput.

config.CSV_BUFFER_SIZE = 100 * 1024 * 1024

The buffer size (in bytes) of the CSV dataset reader.

config.MAX_CONTEXTS = 200

The number of contexts to sample in each example during training (resampling a different subset of this size every training iteration).


The max size of the subtoken vocabulary.

config.TARGET_VOCAB_MAX_SIZE = 27000

The max size of the target words vocabulary.

config.EMBEDDINGS_SIZE = 128

Embedding size for subtokens, AST nodes and target symbols.

config.RNN_SIZE = 128 * 2

The total size of the two LSTMs that are used to embed the paths if config.BIRNN is True, or the size of the single LSTM if config.BIRNN is False.

config.DECODER_SIZE = 320

Size of each LSTM layer in the decoder.


Number of decoder LSTM layers. Can be increased to support long target sequences.

config.MAX_PATH_LENGTH = 8 + 1

The max number of nodes in a path

config.MAX_NAME_PARTS = 5

The max number of subtokens in an input token. If the token is longer, only the first subtokens will be read.


The max number of symbols in the target sequence. Set to 6 by default for method names, but can be increased for learning datasets with longer sequences.

config.BIRNN = True

If True, use a bidirectional LSTM to encode each path. If False, use a unidirectional LSTM only.


When True, sample MAX_CONTEXT from every example every training iteration. When False, take the first MAX_CONTEXTS only.

config.BEAM_WIDTH = 0

Beam width in beam search. Inactive when 0.

config.USE_MOMENTUM = True

If True, use Momentum optimizer with nesterov. If False, use Adam (Adam converges in fewer epochs; Momentum leads to slightly better results).

Releasing a trained model

If you wish to keep a trained model for inference only (without the ability to continue training it) you can release the model using:

python3 --load models/java-large-model/model_iter52 --release

This will save a copy of the trained model with the '.release' suffix. A "released" model usually takes ~3x less disk space.

Extending to other languages

This project currently supports Java and C# as the input languages.

March 2020 - a code2seq extractor for C++ based on LLVM was developed by @Kolkir and is available here:

January 2020 - a code2seq extractor for Python (specifically targeting the Python150k dataset) was contributed by @stasbel. See:

January 2020 - an extractor for predicting TypeScript type annotations for JavaScript input using code2vec was developed by @izosak and Noa Cohen, and is available here:

June 2019 - an extractor for C that is compatible with our model was developed by CMU SEI team. - removed by CMU SEI team.

June 2019 - a code2vec extractor for Python, Java, C, C++ by JetBrains Research is available here: PathMiner.

To extend code2seq to other languages other than Java and C#, a new extractor (similar to the JavaExtractor) should be implemented, and be called by Basically, an extractor should be able to output for each directory containing source files:

  • A single text file, where each row is an example.
  • Each example is a space-delimited list of fields, where:
  1. The first field is the target label, internally delimited by the "|" character (for example: compare|ignore|case)
  2. Each of the following field are contexts, where each context has three components separated by commas (","). None of these components can include spaces nor commas.

We refer to these three components as a token, a path, and another token, but in general other types of ternary contexts can be considered.

Each "token" component is a token in the code, split to subtokens using the "|" character.

Each path is a path between two tokens, split to path nodes (or other kinds of building blocks) using the "|" character. Example for a context:


Here my|key and get|value are tokens, and StringExression|MethodCall|Name is the syntactic path that connects them.



To download the Java-small, Java-med and Java-large datasets used in the Code Summarization task as raw *.java files, use:

To download the preprocessed datasets, use:


The C# dataset used in the Code Captioning task can be downloaded from the CodeNN repository.


Using the trained model

For the NMT baselines (BiLSTM, Transformer) we used the implementation of OpenNMT-py. The trained BiLSTM model is available here:

Test+validation sources and targets:

The command line for "translating" a "source" file to a "target" is: python3 -model -src test_source.txt -output translation_epoch16.txt -gpu 0

This results in a translation_epoch16.txt which we compare to test_target.txt to compute the score. The file test_expected_actual.txt is a line-by-line concatenation of the true reference ("expected") with the corresponding prediction (the "actual").

Creating data for the baseline

We first modified the JavaExtractor (the same one as in this) to locate the methods to train on and print them to a file where each method is a single line. This modification is currently not checked in, but instead of extracting paths, it just prints node.toString() and replaces "\n" with space, where node is the object holding the AST node of type MethodDeclaration.

Then, we tokenized (including sub-tokenization of identifiers, i.e., "ArrayList"-> ["Array","List"]) each method body using javalang, using this script (which can be run on this input example). So a program of:

void methodName(String fooBar) {
    System.out.println("hello world");

should be printed by the modified JavaExtractor as:

method name|void (String fooBar){ System.out.println("hello world");}

and the tokenization script would turn it into:

void ( String foo Bar ) { System . out . println ( " hello world " ) ; }

and the label to be predicted, i.e., "method name", into a separate file.

OpenNMT-py can then be trained over these training source and target files.


code2seq: Generating Sequences from Structured Representations of Code

    title={code2seq: Generating Sequences from Structured Representations of Code},
    author={Uri Alon and Shaked Brody and Omer Levy and Eran Yahav},
    booktitle={International Conference on Learning Representations},


Code for the model presented in the paper: "code2seq: Generating Sequences from Structured Representations of Code"








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