Typhon: Automatic Recommendation of Relevant Code Cells in Jupyter Notebooks

TL;DR

Typhon is a system that automatically recommends relevant code cells in Jupyter notebooks by analyzing markdown descriptions and using text similarity measures, aiming to improve developer productivity.

Contribution

It introduces a novel method combining tokenization and text similarity metrics like BM25 and CodeBERT for code cell recommendation in notebooks.

Findings

Moderate accuracy in recommending relevant code cells

Effective use of BM25 and CodeBERT for similarity measurement

Potential for further improvements in notebook code recommendation

Abstract

At present, code recommendation tools have gained greater importance to many software developers in various areas of expertise. Having code recommendation tools has enabled better productivity and performance in developing the code in software and made it easier for developers to find code examples and learn from them. This paper proposes Typhon, an approach to automatically recommend relevant code cells in Jupyter notebooks. Typhon tokenizes developers' markdown description cells and looks for the most similar code cells from the database using text similarities such as the BM25 ranking function or CodeBERT, a machine-learning approach. Then, the algorithm computes the similarity distance between the tokenized query and markdown cells to return the most relevant code cells to the developers. We evaluated the Typhon tool on Jupyter notebooks from Kaggle competitions and found that the approach can recommend code cells with moderate accuracy. The approach and results in this paper can lead to further improvements in code cell recommendations in Jupyter notebooks.