Cover Image

Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Weisz, Justin D., Kumar, Shraddha, Muller, Michael, Browne, Karen-Ellen, Goldberg, Arielle, Heintze, Ellice, Bajpai, Shagun
Format:	Preprint
Published:	2024
Subjects:	Human-Computer Interaction Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Story Arena: A Multi-Agent Environment for Envisioning the Future of Software Engineering
by: Weisz, Justin D., et al.
Published: (2025)

CodeA11y: Making AI Coding Assistants Useful for Accessible Web Development
by: Mowar, Peya, et al.
Published: (2025)

The Evolution of Information Seeking in Software Development: Understanding the Role and Impact of AI Assistants
by: Haque, Ebtesam Al, et al.
Published: (2024)

Generating User Experience Based on Personas with AI Assistants
by: Huang, Yutan
Published: (2024)

Developer Needs and Feasible Features for AI Assistants in IDEs
by: Sergeyuk, Agnia, et al.
Published: (2024)

Towards Decoding Developer Cognition in the Age of AI Assistants
by: Haque, Ebtesam Al, et al.
Published: (2025)

Expedient Assistance and Consequential Misunderstanding: Envisioning an Operationalized Mutual Theory of Mind
by: Weisz, Justin D., et al.
Published: (2024)

The Impact of AI-Assisted Development on Software Security: A Study of Gemini and Developer Experience
by: Jost, Nadine, et al.
Published: (2026)

GeoPandas-AI: A Smart Class Bringing LLM as Stateful AI Code Assistant
by: Merten, Gaspard, et al.
Published: (2025)

From Teacher to Colleague: How Coding Experience Shapes Developer Perceptions of AI Tools
by: Zakharov, Ilya, et al.
Published: (2025)

The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Study
by: Mohamed, Amr, et al.
Published: (2025)

Why AI Agents Still Need You: Findings from Developer-Agent Collaborations in the Wild
by: Kumar, Aayush, et al.
Published: (2025)

Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
by: Becker, Joel, et al.
Published: (2025)

Envisioning the Next-Generation AI Coding Assistants: Insights & Proposals
by: Nghiem, Khanh, et al.
Published: (2024)

"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistants
by: Lyu, Yunbo, et al.
Published: (2025)

Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025
by: Huang, Ruanqianqian, et al.
Published: (2025)

Evolution of Programmers' Trust in Generative AI Programming Assistants
by: Shah, Anshul, et al.
Published: (2025)

The Fast and Spurious: Developer Productivity with GenAI
by: Afroz, Sadia, et al.
Published: (2025)

Security Concerns in Generative AI Coding Assistants: Insights from Online Discussions on GitHub Copilot
by: Ferreyra, Nicolás E. Díaz, et al.
Published: (2026)

An Exploratory Study of ML Sketches and Visual Code Assistants
by: Gomes, Luís F., et al.
Published: (2024)

The SPACE of AI: Real-World Lessons on AI's Impact on Developers
by: Houck, Brian, et al.
Published: (2025)

Exploring Developer Experience Factors in Software Ecosystems
by: Zacarias, Rodrigo Oliveira, et al.
Published: (2025)

Unlocking Adaptive User Experience with Generative AI
by: Huang, Yutan, et al.
Published: (2024)

Human-AI Experience in Integrated Development Environments: A Systematic Literature Review
by: Sergeyuk, Agnia, et al.
Published: (2025)

"Always Nice and Confident, Sometimes Wrong": Developer's Experiences Engaging Large Language Models (LLMs) Versus Human-Powered Q&A Platforms for Coding Support
by: Li, Jiachen, et al.
Published: (2023)

Assessing Consensus of Developers' Views on Code Readability
by: Sergeyuk, Agnia, et al.
Published: (2024)

Exploring Human-AI Collaboration in Agile: Customised LLM Meeting Assistants
by: Cabrero-Daniel, Beatriz, et al.
Published: (2024)

Towards an Understanding of Developer Experience-Driven Transparency in Software Ecosystems
by: Zacarias, Rodrigo Oliveira, et al.
Published: (2025)

Evaluating Privacy Perceptions, Experience, and Behavior of Software Development Teams
by: Prybylo, Maxwell, et al.
Published: (2024)

Beyond the Commit: Developer Perspectives on Productivity with AI Coding Assistants
by: Chen, Valerie, et al.
Published: (2026)

Exploring Interaction Patterns for Debugging: Enhancing Conversational Capabilities of AI-assistants
by: Chopra, Bhavya, et al.
Published: (2024)

Usage, Effects and Requirements for AI Coding Assistants in the Enterprise: An Empirical Study
by: Vukovic, Maja, et al.
Published: (2026)

"I'm Not Reading All of That": Understanding Software Engineers' Level of Cognitive Engagement with Agentic Coding Assistants
by: Catalan, Carlos Rafael, et al.
Published: (2026)

Patterns of Developer Adoption of LLM-Generated Code Refactoring Suggestions
by: Schön, David, et al.
Published: (2026)

Investigating and Designing for Trust in AI-powered Code Generation Tools
by: Wang, Ruotong, et al.
Published: (2023)

In-IDE Human-AI Experience in the Era of Large Language Models; A Literature Review
by: Sergeyuk, Agnia, et al.
Published: (2024)

What's in a Proof? Analyzing Expert Proof-Writing Processes in F* and Verus
by: Jain, Rijul, et al.
Published: (2025)

AI in Software Engineering: Perceived Roles and Their Impact on Adoption
by: Zakharov, Ilya, et al.
Published: (2025)

How Do Developers Interact with AI? An Exploratory Study on Modeling Developer Programming Behavior
by: Wu, Yinan, et al.
Published: (2026)

Vibe Coding: Toward an AI-Native Paradigm for Semantic and Intent-Driven Programming
by: Bamil, Vinay
Published: (2025)