A Static Analysis Framework for Data Science Notebooks

Pavle Subotic, Lazar Milikic, Milan Stojic

Abstract: Notebooks provide an interactive environment for programmers to develop code, analyse data and inject interleaved visualisations in a single environment. Despite their flexibility, a major pitfall that data scientists encounter is unexpected behaviour caused by the unique out-of-order execution model of notebooks. As a result, data scientists face various challenges ranging from notebook correctness, reproducibility and cleaning. In this paper, we propose a framework that performs static analysis on notebooks, incorporating their unique execution semantics. Our framework is general in the sense that it accommodates a wide range of analyses, useful for various notebook use cases. We have instantiated our framework on a diverse set of analyses and have evaluated them on 2211 real world notebooks. Our evaluation demonstrates that the vast majority (98.7%) of notebooks can be analysed in less than a second, well within the time frame required by interactive notebook clients.

External IDs:dblp:conf/icse/SuboticMS22