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This content will become publicly available on February 6, 2025

Title: How to Support ML End-User Programmers through a Conversational Agent
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 conversa- tional 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 effec- tively 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.  more » « less
Award ID(s):
2236198 2303042 2303043 2235601
NSF-PAR ID:
10493929
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
Proceedings of the International Conference on Software Engineering
ISSN:
1819-3781
Page Range / eLocation ID:
1 to 12
Format(s):
Medium: X
Location:
Lisbon Portugal
Sponsoring Org:
National Science Foundation
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