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  Please cite this software using the metadata from
  'preferred-citation'.
type: software
title: Safe-DS DSL
repository-code: https://github.com/Safe-DS/DSL
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: >-
    Safe-DS: A Domain Specific Language to Make Data Science Safe
  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: >-
    Due to the long runtime of Data Science (DS) pipelines,
    even small programming mistakes can be very costly, if
    they are not detected statically. However, even basic
    static type checking of DS pipelines is difficult because
    most are written in Python. Static typing is available in
    Python only via external linters. These require static type
    annotations for parameters or results of functions, which
    many DS libraries do not provide. In this paper, we show how
    the wealth of Python DS libraries can be used in a
    statically safe way via Safe-DS, a domain specific language
    (DSL) for DS. Safe-DS catches conventional type errors plus
    errors related to range restrictions, data manipulation, and
    call order of functions, going well beyond the abilities of
    current Python linters. Python libraries are integrated into
    Safe-DS via a stub language for specifying the interface of
    its declarations, and an API-Editor that is able to extract
    type information from the code and documentation of Python
    libraries, and automatically generate suitable stubs.
    Moreover, Safe-DS complements textual DS pipelines with a
    graphical representation that eases safe development by
    preventing syntax errors. The seamless synchronization of
    textual and graphic view lets developers always choose the
    one best suited for their skills and current task. We think
    that Safe-DS can make DS development easier, faster, and
    more reliable, significantly reducing development costs.
  keywords:
    - "data science"
    - "machine learning"
    - "static safety"
    - "refinement types"
    - "schema types"
    - "domain specific language"
  doi: "10.1109/ICSE-NIER58687.2023.00019"
  identifiers:
    - type: doi
      value: "10.1109/ICSE-NIER58687.2023.00019"
      description: "IEEE Xplore"
    - type: doi
      value: "10.48550/arXiv.2302.14548"
      description: "arXiv (preprint)"