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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Vydáno v:Proceedings / International Conference on Software Engineering s. 629 - 640
Hlavní autoři: Garcia, Emily Arteaga, Pimentel, Joao Felipe, Feng, Zixuan, Gerosa, Marco, Steinmacher, Igor, Sarma, Anita
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 14.04.2024
Témata:
Backtracking
Codes
Conversational Agent
Design methodology
End-user programming
Machine learning
Machine learning algorithms
Reviews
Task analysis
Wizard of Oz
ISSN:1558-1225
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Shrnutí: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