This repository contains the code for the paper: Code Completion by Feeding Trees to Transformers.

This repo is not actively maintained.

If you use our code/implementation, please cite our paper: https://arxiv.org/abs/2003.13848

generate_new_trees.py: a preprocessing script that converts the ASTs in py150 dataset (https://www.sri.inf.ethz.ch/phog) to modified ASTs, as explained in Appendix A.1.

generate_vocab.py: a script that creates the vocab corpus. In our models, we took the top 100k common vocab to be in the corpus; all other vocab is replaced by <unk_token>.

models/<model>/generate_data.py: the scripts to generate datasets can be found in the models directory, for all the differen models (DFS, DFS_UD, LeafTokens, RootPath, SrcSeq, SrcRNN). Note that the inputs for SrcSeq and SrcRNN are the same. This script will handle separating out the long trees into subtrees using a sliding window, as explained in Appendix A.2. In our implementation, n_context = 1000.

models/dfs/generate_ast_ids.py: a script to generate index locations for certain predictions in the dataset for the AST-based models (DFS, DFS_UD). Can get indices for:

• specific values (Table 9): predicting indices for attribute access, name (varialbe, module), numeric constant, function parameter name.
• specific types (Table 10): predicting indices for function call, assign, return, list, dictionary, raise.
• All values / types (Table 6): predicting indices for all values (leaf nodes) and all types (internal nodes).

models/source_code/generate_data.py: a script that is also used to generate index locations for certain predictions in the dataset for the source code-based models (SrcSeq, SrcRNN). Can get indices for:

• specific values (Table 9): same definition as above
• All values (Table 6): predicting indices for all equivalent leaf nodes of the AST (equivalent to All values, as explained above).

dataset.py: contains class objects (BaseSetup, BaseVocab, BaseDataset) for setting up the dataset and vocab for the model. Each model inherits from these class objects to fit the model's specific setup requirements. The BaseDataset object also contains the collate function for processing batches.

model.py contains the models used in this paper. SrcRNN uses LSTMModel, and the other models use TransformerModel. We used the following hyperparameters for our implementation:

• n_layer = 6
• n_embd = 300
• n_head = 6
• layer_norm_epsilon = 1e-6,
• lr = 1e-3

Code-Prediction-Transformer is CC-BY-NC 4.0 (Attr Non-Commercial Inter.) (e.g., FAIR) licensed, as found in the LICENSE file.