分析开发者如何使用CodeWhisperer，揭示四类编程交互模式。
Cracking CodeWhisperer: Analyzing Developers' Interactions and Patterns During Programming Tasks
- 通过用户实验发现开发者常用自然语言提示和逐步优化代码。
- 识别出四种核心行为模式，包括指令式提示与外部资源结合。
- 适合研究AI辅助编程或人机协作的开发者与研究人员。
AI代码生成工具日益普及，理解开发者如何使用这些工具至关重要。本研究调查了亚马逊推出的基于大模型的代码生成工具CodeWhisperer的使用情况。我们进行了两轮用户研究，每轮10名参与者：第一轮旨在识别关键交互行为，第二轮通过自定义遥测插件收集低层操作数据。混合方法分析揭示了四种行为模式：1）增量式代码优化；2）使用自然语言注释进行显式指令；3）以模型建议为基础构建代码框架；4）与外部资源协同使用。研究提供了对这些模式的全面分析。
原文摘要 · Abstract (English)
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 .
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