The paper titled "Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025" examines how experienced developers employ AI agents in software development. Through field observations and surveys, it finds that developers value agents for productivity but maintain control over design and implementation to ensure quality. They use agents as collaborative tools rather than full delegation, selecting tasks based on suitability and leveraging their expertise to guide agent behavior.
By Ruanqianqian Huang, Avery Reyna, Sorin Lerner, Haijun Xia, Brian Hempel
arXiv:2411.08881v3 Announce Type: replace-cross
Abstract: AI-based systems, including Large Language Models (LLMs), impact millions by supporting diverse tasks but face issues like misinformation, bi...
By Jos\'e Antonio Siqueira de Cerqueira, Mamia Agbese, Rebekah Rousi, Nannan Xi, Juho Hamari, Pekka Abrahamsson
Spec-Driven Development (SDD) is a practice where developers and AI coding agents write specifications to guide software implementation, aiming to preserve the intent behind natural‑language instructions. This study analyzes SDD through Reddit discussions, interviews with 21 practitioners (including eight tool makers), and a large corpus of 470,795 spec files from 73,030 GitHub repositories. Findings reveal that while specs often include implementation details that aid agents, they also risk decay; tool-specific structures influence perception, and benefits vary between individuals and teams, with SDD mainly used for new features and involving higher planning and review costs.
By Shyam Agarwal, Anmol Singhal, Andrew Macvean, Bogdan Vasilescu, Travis Breaux
As multimodal AI systems are becoming our everyday thought partners, the shift towards studying how users transfer control to these systems becomes especially urgent. In this paper, we report an overv...
arXiv:2609.39065v1 Announce Type: cross
Abstract: LLM agents increasingly rely on installable skills, which are packages of instructions, code, and resources that equip them with task-specific capabi...
By Yan Wang, Zhihao Zhang, Ke Chen, Kai Chen, Yaqin Zhang, Duohe Ma, Jun Dai, Xiaoyan Sun
The study examined how users delegate control to multimodal AI by analyzing 392 video question‑answering trials with 56 AI‑literate participants. Participants rated trust after each AI response and chose one of three delegation policies—reject, verify, or fully delegate. Findings showed that higher post‑interaction trust predicted delegation choices, verification was the most common policy, and delegation increased with task order, suggesting experience influences trust beyond self‑reported ratings.
By Agnese Chiatti, Matteo Matteucci, Viola Schiaffonati
arXiv:2608.30756v1 Announce Type: cross
Abstract: Large language models (LLMs) have become an essential tool for assisting developers, yet we still lack knowledge on ways to effectively support their...
By Annemarie Wittig, Alina Mailach, Janet Siegmund, Norbert Siegmund
arXiv:2606. 05770v1 Announce Type: cross Abstract: AI is changing how software engineers work, but it often comes with hidden burdens and costs.
By Vahid Garousi
arXiv:2608. 11513v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software engineering workflows, helping developers write, debug, test, and maintain code.
By Alex Deaconu, Anubhav Gupta, Manaal Basha, Nicholas Haydu, Gema Rodr\'iguez-P\'erez
arXiv:2607. 24601v1 Announce Type: cross Abstract: Background: Large language models (LLMs) are increasingly used to automate code review, but the reasoning behind their decisions remains hard to understand.
By Zhenhan Gao, Marvin Mu\~noz Bar\'on, Umm-e Habiba, Daniel Graziotin, Stefan Wagner
arXiv:2410. 02091v4 Announce Type: replace-cross Abstract: Generative artificial intelligence (AI) facilitates content production and enhances ideation, with potentially important implications for developer productivity and participation in software development.
By Fangchen Song, Ashish Agarwal, Wen Wen
The article examines how AI policies implemented by software organisations affect developers, using insights from 19 interviews. It finds that while such policies aim to reduce risks like data leaks and unauthorized use, they can also impede developers if not properly engaged. The authors propose developer‑centric strategies to help managers and decision makers implement effective AI policies.
By Samuel Ferino, Rashina Hoda, John Grundy, Christoph Treude, Hashini Gunatilake