CITATION.cff

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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: "reimann@cs.uni-bonn.de"
      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)"