How to Support ML End-User Programmers through a Conversational Agent

Emily Arteaga Garcia, Marco Gerosa, João Felipe Pimentel, Igor Steinmacher, Zixuan Feng, Anita Sarma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine Learning (ML) is increasingly gaining significance for end-user programmer (EUP) applications. However, machine learning end-user programmers (ML-EUPs) without the right background face a daunting learning curve and a heightened risk of mistakes and flaws in their models. In this work, we designed a conversational agent named “Newton” as an expert to support ML-EUPs. Newton’s design was shaped by a comprehensive review of existing literature, from which we identified six primary challenges faced by ML-EUPs and five strategies to assist them. To evaluate the efficacy of Newton’s design, we conducted a Wizard of Oz within-subjects study with 12 ML-EUPs. Our findings indicate that Newton effectively assisted ML-EUPs, addressing the challenges highlighted in the literature. We also proposed six design guidelines for future conversational agents, which can help other EUP applications and software engineering activities.

Original languageEnglish (US)
Title of host publicationICSE 2024 - Proceedings of the 46th IEEE/ACM International Conference on Software Engineering
PublisherIEEE Computer Society
ISBN (Electronic)9798400702174
DOIs
StatePublished - Feb 6 2024
Event46th IEEE/ACM International Conference on Software Engineering, ICSE 2024 - Lisbon, Portugal
Duration: Apr 14 2024Apr 20 2024

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257

Conference

Conference46th IEEE/ACM International Conference on Software Engineering, ICSE 2024
Country/TerritoryPortugal
CityLisbon
Period4/14/244/20/24

Keywords

  • Conversational Agent
  • End-user programming
  • Wizard of Oz

ASJC Scopus subject areas

  • Software

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