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

• 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

• 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

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

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.