arXiv cs.AI· Samuel Ferino, Rashina Hoda, John Grundy, Christoph Treude, Hashini Gunatilake·· 13 小时前AI 评分31
开发者向 LLM 委派任务的四种模式与信任因素：一项混合方法研究
Developer Task Delegation to LLMs and the Trust Factor: A Mixed Methods Study
AI 导读
一项基于 22 次访谈和 160 名软件从业者问卷的混合方法研究发现，开发者向 LLM 委派任务存在完全委派、部分委派、自我委派和任务回避四种模式。统计分析显示，信任是与任务委派显著相关的因素，任务难度、任务风险与开发者动机亦被纳入关联分析。该研究已被 ICSE'27 接收。
正文 · 原文
Abstract:With the rise of Large Language Models (LLMs), software developers are presented with a powerful new approach: delegating software development tasks to LLMs. Existing studies have explored task delegation for a subset of tasks and factors. Critically, what tasks developers delegate to LLMs, to what extent, and why they delegate them, is not yet well understood. We explore these questions through 22 interviews and a survey with 160 software practitioners. Developers reported employing four modes of task delegation to LLMs: full delegation, partial delegation, self-delegation, and task avoidance. Drawing on our qualitative findings, we discuss the rationales behind these choices and explore how task difficulty, task risk, developer motivation, and trust appear to be associated with software development task delegation to LLMs. Our statistical analysis reveals that trust is a significant factor associated with task delegation to LLMs. Finally, we share actionable implications for human-AI collaboration in software research, and for software practitioners to make the most of their newly found powers of delegation.
| Comments: | Accepted for publication in the Proceedings of the 49th IEEE/ACM International Conference on Software Engineering (ICSE'27) |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2610.34317 [cs.SE] |
| (or arXiv:2610.34317v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34317 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Samuel Ferino [view email]
[v1]
Sun, 25 Oct 2026 14:28:10 UTC (2,651 KB)
来源：arXiv cs.AI · arxiv.org