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

Machine Learning (ML) is increasingly gaining significance for enduser 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 des...

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Bibliographic Details
Published in:	Proceedings / International Conference on Software Engineering pp. 629 - 640
Main Authors:	Garcia, Emily Arteaga, Pimentel, Joao Felipe, Feng, Zixuan, Gerosa, Marco, Steinmacher, Igor, Sarma, Anita
Format:	Conference Proceeding
Language:	English
Published:	ACM 14.04.2024
Subjects:	Backtracking Codes Conversational Agent Design methodology End-user programming Machine learning Machine learning algorithms Reviews Task analysis Wizard of Oz
ISSN:	1558-1225
Online Access:	Get full text
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Summary:	Machine Learning (ML) is increasingly gaining significance for enduser 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.
ISSN:	1558-1225
DOI:	10.1145/3597503.3608130