Software developers commonly engage in online communities to learn about new technologies. As revolutionary AI-powered code generation tools such as GitHub Copilot emerge, many developers are uncertain about how to trust them. While we see the promise of online communities in helping developers build appropriate trust in AI tools, we know little about how communities shape developers' trust in AI tools and how community features can facilitate trust in the design of AI tools. We investigate these questions through a two-phase study. Through an interview study with 17 developers, we unpack how developers in online communities collectively make sense of AI code generation tools by developing proper expectation, understanding, strategies, and awareness of broader implications, as well as how they leverage community signals to evaluate AI suggestions. We then surface design opportunities and conduct 11 design probe sessions to explore the design space of integrating a user community to AI code generation systems. We conclude with a series of design recommendations.
翻译:软件开发者通常参与在线社区学习新技术。 随着革命性的AI- 动力代码生成工具(如GitHub Copilot)的出现,许多开发者对如何信任这些工具并不确定。 虽然我们看到在线社区在帮助开发者建立对AI工具的适当信任方面的承诺,但我们对社区如何塑造开发者对AI工具的信任以及社区特征如何促进对AI工具设计的信任知之甚少。我们通过两阶段研究来调查这些问题。通过与17个开发者的访谈研究,我们解开在线社区的开发者如何通过开发适当的期望、理解、战略和对更广泛影响的认识,以及他们如何利用社区信号来评价AI的建议,从而共同理解AI代码生成工具。然后我们用地表设计机会和11次设计考察会议来探索将用户社区纳入AI代码生成系统的设计空间。我们最后提出了一系列设计建议。