The use of AI code-generation tools is becoming increasingly common, making it important to understand how software developers are adopting these tools. In this study, we investigate how developers engage with Amazon's CodeWhisperer, an LLM-based code-generation tool. We conducted two user studies with two groups of 10 participants each, interacting with CodeWhisperer - the first to understand which interactions were critical to capture and the second to collect low-level interaction data using a custom telemetry plugin. Our mixed-methods analysis identified four behavioral patterns: 1) incremental code refinement, 2) explicit instruction using natural language comments, 3) baseline structuring with model suggestions, and 4) integrative use with external sources. We provide a comprehensive analysis of these patterns .

翻译：AI代码生成工具的使用日益普遍，理解软件开发人员如何采纳这些工具变得至关重要。本研究探讨了开发者如何与亚马逊基于大语言模型的代码生成工具CodeWhisperer进行交互。我们开展了两项用户研究，每组各10名参与者与CodeWhisperer进行交互——首次研究旨在确定需要捕获的关键交互行为，第二次研究则通过定制遥测插件收集细粒度交互数据。我们的混合方法分析识别出四种行为模式：1）渐进式代码优化，2）使用自然语言注释进行显式指令，3）基于模型建议的基线架构构建，4）结合外部资源的整合式运用。本文对这些模式进行了全面分析。