CITATION.cff

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message: >-
  Please cite this software using the metadata from
  'preferred-citation'.
type: software
title: API-Editor
repository-code: https://github.com/Safe-DS/API-Editor
license: MIT
preferred-citation:
  type: conference-paper
  year: 2022
  conference:
    name: >-
      2022 IEEE/ACM 44th International Conference on
      Software Engineering: New Ideas and Emerging Results
  collection-title: >-
    2022 IEEE/ACM 44th International Conference on
    Software Engineering: New Ideas and Emerging Results
  title: >-
    Improving the Learnability of Machine Learning APIs
    by Semi-Automated API Wrapping
  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: >-
    A major hurdle for students and professional
    software developers who want to enter the world of
    machine learning (ML), is mastering not just the
    scientific background but also the available ML
    APIs, Therefore, we address the challenge of
    creating APIs that are easy to learn and use,
    especially by novices. However, it is not clear how
    this can be achieved without compromising
    expressiveness. We investigate this problem for
    scikit-learn, a widely used ML API. In this paper,
    we analyze its use by the Kaggle community,
    identifying unused and apparently useless parts of
    the API that can be eliminated without affecting
    client programs. In addition, we discuss usability
    issues in the remaining parts, propose related
    design improvements and show how they can be
    implemented by semi-automated wrapping of the
    existing third-party API.
  keywords:
    - APIs
    - libraries
    - usability
    - learnability
    - "machine learning"
  doi: "10.1109/ICSE-NIER55298.2022.9793507"
  identifiers:
    - type: doi
      value: "10.1109/ICSE-NIER55298.2022.9793507"
      description: "IEEE Xplore"
    - type: doi
      value: "10.48550/arXiv.2203.15491"
      description: "arXiv (preprint)"