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neuralCodeCompletion

The implementation of the IJCAI 2018 paper: Code Completion with Neural Attention and Pointer Networks

Descriptions for the directories

code

  • myModel_commented.py: a good commented example for our main model part, i.e., pointer mixture network.
  • attention.py: standard attention model for predicting terminals
  • attention_N.py: standard attention model for predicting non-terminals
  • attention_N_parent.py: parent attention model for predicting non-terminals
  • attention_parent.py: parent attention model for predicting terminals
  • pointer.py: our poirnter mixture network without parent attention
  • pointer_parent.py: our poirnter mixture network with parent attention
  • reader_pointer.py: reader for reading dataset (with parent)
  • reader_pointer_original.py: reader for reading dataset (original without parent)
  • vanillaLSTM.py: vanilla LSTM

preprocess_code

  • freq_dict.py: generate the frequency dictionary for terminals
  • get_non_terminal.py: process the non-terminals (utilize AST information)
  • get_terminal_dict.py: get the terminal dictionary according to the vocabulary size
  • get_terminal_whole.py: the final step to process the terminals (recording location and parent information)
  • get_total_length.py: calculate the total length of the file
  • output.txt: some statistics for the terminals
  • utils.py: some utils to process the data

Download the dataset

This is the link for you to download the raw dataset: JS & PY data If you do not want to get your hands dirty with data preprocess, you can download the pickle data after preprocessed here: pickle data

How to run the code

  1. Download the dataset
  2. Preprocess the data into pickle files and store them in a proper directory
  3. Simply adjust the parameter setting inside the code file and run using python3, e.g. python3 attention.py.