Beena's arXiv study tackles hidden technical debt in AI-powered robotic systems

Beena's doctoral research, presented at ASE 2026, tackles a growing blind spot in software engineering: technical debt hidden inside AI-intensive cyber-physical systems (AI-CPS). These systems combine physical hardware, AI components, and conventional software modules, creating debt that behaves differently from traditional code-level debt. The study begins by systematically characterizing AI-CPS technical debt through analysis of AI ecosystems and real-world AI-CPS repositories, plus interviews with developers who maintain these systems.

The core finding is that existing TD identification methods miss debt unique to the AI lifecycle—things like outdated training data, model drift, brittle sensor integrations, and undocumented AI behavior constraints. To address this, Beena proposes new identification and mitigation approaches, then plans to build an automated tool that uses agentic AI to continuously monitor, govern, and pay down this debt. The tool would act as an always-on technical debt manager for fleets of autonomous systems, potentially reducing maintenance failures and safety risks in domains like autonomous vehicles, healthcare robotics, and smart home automation. The full paper is available via the DOI 10.48550/arXiv.2608.02638.