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arxivcs.SEcs.AIcs.HC2026-07-01

Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLI

Emerson Murphy-Hill, Jenna Butler, Alexandra Savelieva

Organizations rolling out agentic command line tools like Anthropic's Claude Code and GitHub's Copilot CLI need to know who will try them, who will keep using them, and whether the tools produce enough output to justify their cost. At organizational scale, token spend can run into millions of dollars annually, so misreading adoption, retention, or impact can make a rollout expensive without changing engineering velocity. Studying tens of thousands of engineers at Microsoft over its early-2026 rollout, we find that first use spread primarily through social networks, retention was associated more with engineers' coding activity than with demographics, and adopters merged roughly 24% more pull requests than they would have otherwise. We use merged pull requests as our proxy for output -- acknowledging that a merged PR is not the same as the value it delivers -- and the lift persists across our four-month window. These results suggest that CLI coding agents are neither uniformly adopted nor mere novelty effects and that organizations should treat visible peer use as central to rollout strategy.

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Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development

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arxivcs.AIcs.HCcs.SE2026-06-29

Rehearsed Multi-Agent Live Product Demonstrations with Real-Time Voice Question Answering

Rahul Khedar, Mayank Malhotra, Avinash Karn, Mouli V, Prakhar Mehrotra

Live product demonstrations are a recurring, high-cost activity in software organizations: a human presenter must select features, dispatch the corresponding interactions on a running application, narrate them coherently, and answer questions in real time. Existing automation add…

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arxivcs.HCcs.AIcs.SE2026-07-07

Plainbook: Data Science, in Plain Language

Luca de Alfaro, Mathis Aubert, Ranjit Jhala, Eliana Pastor, Elena Baralis

Jupyter Notebooks have become widely adopted in data science, as they allow the sharing of reproducible computational analysis. They are, however, accessible only to people who understand computer code. To reach the broader audience of scientists interested in data analysis and c…

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