Jupyter notebooks are now widely adopted by data analysts as they provide a convenient environment for presenting computational results in a literate-programming document that combines code snippets, rich text, and inline visualizations. Literate-programming documents are intended to be computational narratives that are supplemented with self-explanatory text, but, recent studies have shown that this is lacking in practice.
Efforts in the software engineering community to increase code comprehension in literate programming are limited. To address this, as a first step, this paper presents a prototype Jupyter notebook annotator, HeaderGen, that automatically creates a narrative structure in notebooks by classifying and annotating code cells based on the machine learning workflow. HeaderGen generates a markdown cell header for each code cell by statically analyzing the notebook, and in addition, associates these cell headers with a clickable table of contents for easier navigation.
Further, we discuss our vision and opportunities based on this prototype.