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

arXiv 2510.11516 ↗First posted 13 Oct 2025cs.SE cs.AIcited by 0

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Abstract (arXiv)

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 .

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 31 references that are in the map. First posted on arXiv 13 Oct 2025.

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References · 13

  1. 2023A Comprehensive Overview of Large Language Modelscited by 639
  2. 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 216
  3. 2022Productivity Assessment of Neural Code Completioncited by 97
  4. 2023A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challengescited by 75
  5. 2023Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfacescited by 71
  6. 2023How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environmentcited by 55
  7. 2024What's Wrong with Your Code Generated by Large Language Models? An Extensive Studycited by 52
  8. 2022Designing for Responsible Trust in AI Systems: A Communication Perspectivecited by 48
  9. 2023Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questionscited by 42
  10. 2021In-IDE Code Generation from Natural Language: Promise and Challengescited by 41
  11. 2023Investigating and Designing for Trust in AI-powered Code Generation Toolscited by 34
  12. 2023AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluationcited by 20
1 more
  1. 2024A Study on Developer Behaviors for Validating and Repairing LLM-Generated Code Using Eye Tracking and IDE Actionscited by 12

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