分析AI编程助手的使用条款,揭示责任归属问题并提出可问责开发框架。
Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmap
- 对比主流AI编程工具的使用条款,梳理责任分配模式。
- 发现多数条款将代码正确性与合规责任转嫁给开发者。
- 提出面向自主开发代理的可问责研究路线,适合政策与工具设计者。
AI编程助手和自主代理正日益融入软件开发流程,改变代码生成、审查与维护方式。尽管现有研究多关注其生产力影响,却较少探讨问责机制:当代理生成、修改或推荐代码时,谁应负责?实践中,问责由使用条款(ToS)等政策文件定义。本文对主流AI编程助手及代理化开发工具的使用条款进行比较分析,考察其在所有权、责任、赔偿义务和披露要求方面如何在工具提供商与开发者之间分配权责,并识别出共性与差异。结果表明,普遍存在将代码正确性、安全性与法律合规责任转移给用户的现象,且在赔偿、数据再利用和使用限制等方面存在显著差异。基于此,我们指出当前政策框架与日益自主化的开发流程严重脱节。论文进一步提出可问责代理在软件工程中的研究路线图,涵盖责任建模、治理机制设计、支持问责的工具开发以及开发者认知与实践的实证研究等关键方向。
原文摘要 · Abstract (English)
AI coding assistants and autonomous agents are becoming integral to software development workflows, reshaping how code is produced, reviewed, and maintained. While recent research has focused mainly on the capabilities and impacts of productivity of these systems, much less attention has been paid to accountability: who is responsible when agents generate, modify, or recommend code? In practice, accountability is defined through the Terms of Service (ToS) and related policy documents that govern the use of AI-powered development tools. In this vision paper, we present a comparative analysis of the Terms of Service for widely used AI coding assistants and agent-enabled development tools. We examine how these documents allocate ownership, responsibility, liability, and disclosure obligations between tool providers and software developers, and we identify common patterns and divergences between providers. Our analysis reveals a consistent tendency to shift responsibility for correctness, safety, and legal compliance onto users, as well as substantial variation in how providers address issues such as indemnification, data reuse, and acceptable use. Based on these findings, we argue that existing policy frameworks are poorly aligned with increasingly agent-mediated and autonomous software development workflows. We outline a research roadmap for accountable agents in software engineering, identifying challenges and opportunities for modeling responsibility, designing governance artifacts, developing tooling that supports accountability, and conducting empirical studies of developers' perceptions and practices.
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