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arXiv · 2510.11516

Cracking CodeWhisperer: Analyzing Developers' Interactions and Patterns During Programming Tasks

Jeena Javahar·Tanya Budhrani·Manaal Basha·Cleidson R. B. de Souza·Ivan Beschastnikh·Gema Rodriguez-Perez
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Abstract

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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Jeena Javahar, Tanya Budhrani, Manaal Basha, Cleidson R. B. de Souza, Ivan Beschastnikh, Gema Rodriguez-Perez. 2025-10-13. Cracking CodeWhisperer: Analyzing Developers' Interactions and Patterns During Programming Tasks. https://arxiv.org/abs/2510.11516

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