发现并修复智能物理系统中隐藏的技术债务
Studying, Identifying, and Fixing Hidden Technical Debt in AI-Intensive Cyber-Physical Systems
- 通过分析生态与开发访谈，梳理出AI-CPS特有的技术债务特征
- 提出识别与缓解方法，未来将开发自动化工具支持治理
- 适合关注AI系统长期维护与可靠性的开发者和研究者
人工智能组件在多个软件系统中日益普及，尤其在智能物理系统（AI-CPS）中广泛应用，涵盖自动驾驶、工业控制、家庭自动化、机器人及医疗等领域。由于由硬件、AI模块和传统组件构成，AI-CPS可能产生独特且更难处理的技术债务（TD）。本论文旨在刻画AI-CPS中的技术债务，并提出识别与修复方法。第一阶段通过分析AI生态系统、AI-CPS代码库及开发者访谈，构建债务特征模型；第二阶段基于此知识，设计识别与缓解策略；最后计划开发并验证一个自动化工具，支持代理型AI方案对AI-CPS技术债务进行监测、治理与偿还。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) components are increasingly pervasive in several software systems, including Cyber-Physical Systems (CPSs). AI-CPS are used in several domains, including autonomous vehicles, industry, home automation, robotics, and healthcare. Being composed of hardware, AI components, and conventional modules, AI-CPS can exhibit technical debt (TD) that is peculiar and potentially more challenging than that of conventional systems. This thesis aims to characterize AI-CPS TD and propose approaches for its identification and repair. In a first phase, we characterize AI-CPS TD by analyzing AI ecosystems and AI-CPS repositories, as well as interviewing developers. Based on the acquired knowledge, we define approaches to identify and mitigate such TD. Finally, we plan to develop and validate an automated tool that supports agentic AI solutions to monitor, govern, and repay AI-CPS TD.
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