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docs: add preferred citation
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lars-reimann committed Nov 30, 2023
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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
message: >-
Please cite this software using the metadata from
'preferred-citation'.
type: software
title: Safe-DS Runner
repository-code: https://github.com/Safe-DS/Runner
license: MIT
preferred-citation:
type: conference-paper
year: 2023
conference:
name: >-
2023 IEEE/ACM 45th International Conference on
Software Engineering: New Ideas and Emerging Results
collection-title: >-
2023 IEEE/ACM 45th International Conference on
Software Engineering: New Ideas and Emerging Results
title: >-
An Alternative to Cells for Selective Execution of Data Science Pipelines
authors:
- given-names: Lars
family-names: Reimann
email: "[email protected]"
affiliation: >-
Institute for Computer Science III, University
of Bonn, Germany
orcid: "https://orcid.org/0000-0002-5129-3902"
- affiliation: >-
Institute for Computer Science III, University
of Bonn, Germany
given-names: Günter
family-names: Kniesel-Wünsche
abstract: >-
Data Scientists often use notebooks to develop Data Science (DS) pipelines,
particularly since they allow to selectively execute parts of the pipeline.
However, notebooks for DS have many well-known flaws. We focus on the
following ones in this paper: (1) Notebooks can become littered with code
cells that are not part of the main DS pipeline but exist solely to make
decisions (e.g. listing the columns of a tabular dataset). (2) While users
are allowed to execute cells in any order, not every ordering is correct,
because a cell can depend on declarations from other cells. (3) After making
changes to a cell, this cell and all cells that depend on changed
declarations must be rerun. (4) Changes to external values necessitate
partial re-execution of the notebook. (5) Since cells are the smallest unit
of execution, code that is unaffected by changes, can inadvertently be
re-executed. To solve these issues, we propose to replace cells as the basis
for the selective execution of DS pipelines. Instead, we suggest populating
a context-menu for variables with actions fitting their type (like listing
columns if the variable is a tabular dataset). These actions are executed
based on a data-flow analysis to ensure dependencies between variables are
respected and results are updated properly after changes. Our solution
separates pipeline code from decision making code and automates dependency
management, thus reducing clutter and the risk of making errors.
keywords:
- "notebook"
- "usability"
- "data science"
- "machine learning"
doi: "10.1109/ICSE-NIER58687.2023.00029"
identifiers:
- type: doi
value: "10.1109/ICSE-NIER58687.2023.00029"
description: "IEEE Xplore"
- type: doi
value: "10.48550/arXiv.2302.14556"
description: "arXiv (preprint)"

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