2021/03/04 by Jiawei Wang, Li Li, Wang, Jiawei +3 · 4 citations
Computer Science · Decision Sciences · #Advanced Data Storage Technologies #FOS: Computer and information sciences #Scientific Computing and Data Management #Software Engineering (cs.SE) #Software Engineering Research
paper · pdf · doi:10.48550/arxiv.2103.02959
openalex publication_date 2021/03/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
More than ninety percent of published Jupyter notebooks do not state dependencies on external packages. This makes them non-executable and thus hinders reproducibility of scientific results. We present SnifferDog, an approach that 1) collects the APIs of Python packages and versions, creating a database of APIs; 2) analyzes notebooks to determine candidates for required packages and versions; and 3) checks which packages are required to make the notebook executable (and ideally, reproduce its stored results). In its evaluation, we show that SnifferDog precisely restores execution environments for the largest majority of notebooks, making them immediately executable for end users.
Davos: a Python "smuggler" for constructing lightweight reproducible notebooks