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 |
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| Jazyk: | angličtina |
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ACM
14.04.2024
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| ISSN: | 1558-1225 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Feng, Zixuan Gerosa, Marco Garcia, Emily Arteaga Steinmacher, Igor Sarma, Anita Pimentel, Joao Felipe |
| Author_xml | – sequence: 1 givenname: Emily Arteaga surname: Garcia fullname: Garcia, Emily Arteaga email: arteagae@oregonstate.edu organization: Oregon State University,Corvallis,OR,USA – sequence: 2 givenname: Joao Felipe surname: Pimentel fullname: Pimentel, Joao Felipe email: joao.pimentel@nau.edu organization: Northern Arizona University,Flagstaff,AZ,USA – sequence: 3 givenname: Zixuan surname: Feng fullname: Feng, Zixuan email: fengzi@oregonstate.edu organization: Oregon State University,Corvallis,OR,USA – sequence: 4 givenname: Marco surname: Gerosa fullname: Gerosa, Marco email: marco.gerosa@nau.edu organization: Northern Arizona University,Flagstaff,AZ,USA – sequence: 5 givenname: Igor surname: Steinmacher fullname: Steinmacher, Igor email: igor.steinmacher@nau.edu organization: Northern Arizona University,Flagstaff,AZ,USA – sequence: 6 givenname: Anita surname: Sarma fullname: Sarma, Anita email: anita.sarma@oregonstate.edu organization: Oregon State University,Corvallis,OR,USA |
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| Snippet | Machine Learning (ML) is increasingly gaining significance for enduser programmer (EUP) applications. However, machine learning end-user programmers (ML-EUPs)... |
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| SubjectTerms | Backtracking Codes Conversational Agent Design methodology End-user programming Machine learning Machine learning algorithms Reviews Task analysis Wizard of Oz |
| Title | How to Support ML End-User Programmers through a Conversational Agent |
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