Investigating and Designing for Trust in AI-powered Code Generation Tools
Year: 2024
Type: conference-paper
Abstract: Trust is a crucial factor for the adoption and responsible usage of generative AI tools in complex tasks such as software engineering. However, we have a limited understanding of how software developers evaluate the trustworthiness of AI-powered code generation tools in real-world settings. To address this gap, we conducted Study 1, an interview study with 17 developers who use AI-powered code generation tools in professional or personal settings. We found that developers’ trust is rooted in the AI tool’s perceived ability, integrity, and benevolence, and is situational, varying according to the context of usage. Existing AI code generation tools lack the affordances for developers to efficiently and effectively evaluate the trustworthiness of AI-powered code generation tools. To explore designs that can augment the existing interface of AI-powered code generation tools, we explored three sets of design concepts (suggestion quality indicators, usage stats, and control mechanisms) that derived from Study 1 findings. In Study 2, a design probe study with 12 developers, we investigated the potential of these design concepts to help developers make effective trust judgments. We discuss the implication of our findings on the design of AI-powered code generation tools and future research on trust in AI.
Institutions: University of Washington, Microsoft (United States)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4399361983