AI智能体质量保证实践与差距的大规模实证研究

中文摘要 AI 辅助

基于大型语言模型（LLM）的智能体正越来越多地应用于软件工程、网页自动化、研究和生产力应用中。它们对规划、记忆、工具使用、代码执行和外部交互的整合，实现了更高的自主性，但也引入了新的可靠性、安全性和安全性风险。我们对157个至少拥有100个GitHub星标的开源基于LLM的智能体项目进行了大规模的质量保证（QA）实践实证研究。我们分析了文档、源代码、配置和测试，以表征跨执行面、安全措施、测试工件、风险场景和反复出现的差距的QA实践。我们发现，当前的QA主要侧重于基本功能和高风险操作，而覆盖率仍然分散。安全措施在等效执行路径上的应用不一致，测试很少检查边界、对抗性或多步骤工具使用失败，且已识别的风险很少转化为端到端的QA检查。这些发现强调了需要超越功能级测试，转向系统性的端到端验证，以确保智能体工作流在与不可信输入、工具、持久状态和外部API交互时保持在预期边界内。

英文摘要

Large language model (LLM)-based agents are increasingly used across software engineering, web automation, research, and productivity applications. Their integration of planning, memory, tool use, code execution, and external interactions enables greater autonomy but also introduces new reliability, safety, and security risks. We present a large-scale empirical study of quality assurance (QA) practices in 157 open-source LLM-based agent projects with at least 100 GitHub stars. We analyze documentation, source code, configurations, and tests to characterize QA practices across execution surfaces, safeguards, testing artifacts, risk scenarios, and recurring gaps. We find that current QA primarily focuses on basic functionality and high-risk actions, while coverage remains fragmented. Safeguards are inconsistently applied across equivalent execution routes, tests rarely examine boundary, adversarial, or multi-step tool-use failures, and identified risks are seldom translated into end-to-end QA checks. These findings highlight the need to move beyond feature-level testing toward systematic end-to-end validation that ensures agent workflows remain within intended boundaries when interacting with untrusted inputs, tools, persistent state, and external APIs.

发表机构

• York University（约克大学）
• Polytechnique Montréal（蒙特利尔高等工程技术学院）

机构由 AI 辅助整理，请以论文原文为准。