Survey of 162 vibe coders finds perceptions of AI code quality similar across experience levels but motivations, interaction styles, and quality assurance practices diverge, revealing a perception-action gap.
Perceived usefulness, perceived ease of use, and user acceptance of information technology
9 Pith papers cite this work, alongside 141 external citations. Polarity classification is still indexing.
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representative citing papers
RECAP captures, replays, and analyzes AI-assisted programming sessions by linking prompts, edits, and developer actions in a single timeline.
Longitudinal surveys show AI coding assistants reduce time on code writing but increase supervisory verification tasks, with stable productivity perceptions yet rising reports of worsened developer experience.
A multisite biometric study finds lower cognitive engagement under AI assistance via EEG and blink rate, with physiological-performance links present only in the non-AI condition.
LLM assistance shortens idea-generation periods and reduces creative moments during programming tasks while yielding solutions with comparable idea counts and greater functional correctness.
Comparative review of AI coding tool ToS shows responsibility for code quality and compliance shifted to users, with policy misalignment for autonomous agents, plus a research roadmap.
Exploratory lab study finds shared LLM use builds shared understanding in design teams while parallel use risks context drift, with professionals reflecting on outputs for insights but sometimes anchoring early.
Higher AI tool usage correlates with better perceived productivity and code quality among developers, revealing three adoption segments and links to organizational context.
A survey of user studies on LLM use in programming that identifies interaction behaviors, mixed benefits and weaknesses, and factors influencing human and task performance.
citing papers explorer
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From Prompting to Verification: How Experience Shapes Vibe Coding Practices
Survey of 162 vibe coders finds perceptions of AI code quality similar across experience levels but motivations, interaction styles, and quality assurance practices diverge, revealing a perception-action gap.
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RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions
RECAP captures, replays, and analyzes AI-assisted programming sessions by linking prompts, edits, and developer actions in a single timeline.
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The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study
Longitudinal surveys show AI coding assistants reduce time on code writing but increase supervisory verification tasks, with stable productivity perceptions yet rising reports of worsened developer experience.
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Using Biometrics to Understand AI-Assisted Coding Performance and its Perception
A multisite biometric study finds lower cognitive engagement under AI assistance via EEG and blink rate, with physiological-performance links present only in the non-AI condition.
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"Like Taking the Path of Least Resistance": Exploring the Impact of LLM Interaction on the Creative Process of Programming
LLM assistance shortens idea-generation periods and reduces creative moments during programming tasks while yielding solutions with comparable idea counts and greater functional correctness.
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Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmap
Comparative review of AI coding tool ToS shows responsibility for code quality and compliance shifted to users, with policy misalignment for autonomous agents, plus a research roadmap.
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The Role of LLMs in Collaborative Software Design
Exploratory lab study finds shared LLM use builds shared understanding in design teams while parallel use risks context drift, with professionals reflecting on outputs for insights but sometimes anchoring early.
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AI Tools in Software Development: Developer Perceptions and Usage Patterns
Higher AI tool usage correlates with better perceived productivity and code quality among developers, revealing three adoption segments and links to organizational context.
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Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks
A survey of user studies on LLM use in programming that identifies interaction behaviors, mixed benefits and weaknesses, and factors influencing human and task performance.