We propose a framework to automatically generate descriptive comments for source code blocks. While this problem has been studied by many researchers previously, their methods are mostly based on fixed template and achieves poor results. Our framework does not rely on any template, but makes use of a new recursive neural network called Code-RNN to extract features from the source code and embed them into one vector. When this vector representation is input to a new recurrent neural network (Code-GRU), the overall framework generates text descriptions of the code with accuracy (Rouge-2 value) significantly higher than other learning-based approaches such as sequence-to-sequence model. The Code-RNN model can also be used in other scenario where the representation of code is required.
@article{arxiv.1808.06880,
title = {Automatic Generation of Text Descriptive Comments for Code Blocks},
author = {Yuding Liang and Kenny Q. Zhu},
journal= {arXiv preprint arXiv:1808.06880},
year = {2018}
}