Machine learning for source code suggestion, completion

Image by StockSnap from Pixabay

This is a great paper demonstrating the use of NLP techniques for completion of software source code. It uses recurrent networks and can reduce the size of the vocabulary compared to previous approaches.

As the authors say: “The CodeGRU introduces a novel approach which can correctly capture the source code context by leveraging the token type information.”

I like the approach because it can extract the information that is important for the analysis of source code – what kind of token is analysed and how it is used.

Conclusions (quote from the abstract): “Our experiment confirms that the source code’s contextual information can be vital and can help improve the software language models. The extensive evaluation of CodeGRU shows that it outperforms the state-of-the-art models. The results further suggest that the proposed approach can help reduce the vocabulary size and is of practical use for software developers.”

I’m kind of keen to check this approach in our work. See if we can use this to improve the quality of source code.

Author: Miroslaw Staron

I’m professor in Software Engineering at IT faculty. I usually blog about interesting articles (for me) and my own reflections on the development of Software Engineering, AI, computer science and automotive software.