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Where are my intelligent assistant's mistakes? a systematic testing approach

TitleWhere are my intelligent assistant's mistakes? a systematic testing approach
Publication TypeConference Paper
Year of Publication2011
AuthorsKulesza, T., M. M. Burnett, S. Stumpf, W-K. Wong, S. Das, A. Groce, A. Shinsel, F. Bice, and K. McIntosh
Conference NameProceedings of the Third international conference on End-user development
Pagination171–186
Date Published06/2011
PublisherSpringer-Verlag
Conference LocationBerlin, Heidelberg
ISBN Number978-3-642-21529-2
Keywordsend-user development, end-user programming, end-user software engineering, intelligent assistants, machine learning, testing
Abstract

Intelligent assistants are handling increasingly critical tasks, but until now, end users have had no way to systematically assess where their assistants make mistakes. For some intelligent assistants, this is a serious problem: if the assistant is doing work that is important, such as assisting with qualitative research or monitoring an elderly parent's safety, the user may pay a high cost for unnoticed mistakes. This paper addresses the problem with WYSIWYT/ML (What You See Is What You Test for Machine Learning), a human/computer partnership that enables end users to systematically test intelligent assistants. Our empirical evaluation shows that WYSIWYT/ML helped end users find assistants' mistakes significantly more effectively than ad hoc testing. Not only did it allow users to assess an assistant's work on an average of 117 predictions in only 10 minutes, it also scaled to a much larger data set, assessing an assistant's work on 623 out of 1,448 predictions using only the users' original 10 minutes' testing effort.

URLhttp://dl.acm.org/citation.cfm?id=2022939.2022959