"It would work for me too": How Online Communities Shape Software Developers' Trust in AI-Powered Code Generation Tools
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ID: 283240
2022
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Abstract
While revolutionary AI-powered code generation tools have been rising
rapidly, we know little about how and how to help software developers form
appropriate trust in those AI tools. Through a two-phase formative study, we
investigate how online communities shape developers' trust in AI tools and how
we can leverage community features to facilitate appropriate user trust.
Through interviewing 17 developers, we find that developers collectively make
sense of AI tools using the experiences shared by community members and
leverage community signals to evaluate AI suggestions. We then surface design
opportunities and conduct 11 design probe sessions to explore the design space
of using community features to support user trust in AI code generation
systems. We synthesize our findings and extend an existing model of user trust
in AI technologies with sociotechnical factors. We map out the design
considerations for integrating user community into the AI code generation
experience.
| Reference Key |
ford2022it
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|---|---|
| Authors | Ruijia Cheng; Ruotong Wang; Thomas Zimmermann; Denae Ford |
| Journal | arXiv |
| Year | 2022 |
| DOI |
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