Facilitating Dependency Exploration in Computational Notebooks

Computational notebooks promote exploration by structuring code, output, and explanatory text, into cells. The input code and rich outputs help users iteratively investigate ideas as they explore or analyze data. The links between these cells--how the cells depend on each other--are important in understanding how analyses have been developed and how the results can be reproduced. Specifically, a code cell that uses a particular identifier depends on the cell where that identifier is defined or mutated. Because notebooks promote fluid editing where cells can be moved and run in any order, cell dependencies are not always clear or easy to follow. We examine different tools that seek to address this problem by extending Jupyter notebooks and evaluate how well they support users in accomplishing tasks that require understanding dependencies. We also evaluate visualization techniques that provide views of the dependencies to help users navigate cell dependencies.