Examining difficulties software developers encounter in the adoption of statistical machine learning

Authors:
Kayur Patel;James Fogarty;James A. Landay;Beverly Harrison
Affiliations:
Computer Science & Engineering, DUB Group, University of Washington, Seattle, WA;Computer Science & Engineering, DUB Group, University of Washington, Seattle, WA;Computer Science & Engineering, DUB Group, University of Washington, Seattle, WA and Intel Research Seattle, Seattle, WA;Intel Research Seattle, Seattle, WA
Venue:
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Year:
2008

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Abstract

Statistical machine learning continues to show promise as a tool for addressing complex problems in a variety of domains. An increasing number of developers are therefore looking to use statistical machine learning algorithms within applications. We have conducted two initial studies examining the difficulties that developers encounter when creating a statistical machine learning component of a larger application. We first interviewed researchers with experience integrating statistical machine learning into applications. We then sought to directly observe and quantify some of the behavior described in our interviews using a laboratory study of developers attempting to build a simple application that uses statistical machine learning. This paper presents the difficulties we observed in our studies, discusses current challenges to developer adoption of statistical machine learning, and proposes potential approaches to better supporting developers creating statistical machine learning components of applications.