Description: We want to create a deep Neural Network that can automatically generate comments for code snippets passed to it. The motivation behind this is that in software development and maintenance, developers spend around 59% of their time on program comprehension activities. Having comments that are generated automatically will hopefully cut this time down. In order to do this we will combine the recent paper Code2Vec: Learning Distributed Representations of Code by Alon et al. with the paper Deep Code Comment Generation in order to make a better performing model using the newer Code2Vec encoding that was not used in the Deep Code Comment Generation paper. Dataset: The dataset that we will use is the same dataset used by the Deep Code Comment Generation paper, this is a dataset of more than 500,000 code snippets including comments. This also gives us a baseline against which to compare. Papers: Deep Code: https://xin-xia.github.io/publication/icpc182.pdf Code2Vec: https://arxiv.org/abs/1803.09473
Implementation of 'A Convolutional Attention Network for Extreme Summarization of Source Code' in PyTorch using TorchText
A reimplementation of "A Syntactic Neural Model for General-Purpose Code Generation"
Replication of the paper "Structured Neural Summarization" which uses Graph Neural Networks and Seq2Seq models to summarize natural language and source code.
Implementation of "Automatic Source Code Summarization with Extended Tree-LSTM"
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
A set of tools for extracting tables from PDF files helping to do data mining on (OCR-processed) scanned documents.
Modify from https://github.com/ankush-me/SynthText.git to generate chinese character
