# Software Engineering for AI-driven Building Operation

arXiv: https://arxiv.org/abs/2608.16237 · HTML: https://mlsanity.com/paper/2608.16237/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 85 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 24 of 24)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges](https://mlsanity.com/paper/2010.09337/) (2020)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [Adoption and Effects of Software Engineering Best Practices in Machine Learning](https://mlsanity.com/paper/2007.14130/) (2020)
- [A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challenges](https://mlsanity.com/paper/2012.07919/) (2020)
- [Studying Software Engineering Patterns for Designing Machine Learning Systems](https://mlsanity.com/paper/1910.04736/) (2019)
- [Requirements Engineering Challenges in Building AI-Based Complex Systems](https://mlsanity.com/paper/1908.11791/) (2019)
- [Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation](https://mlsanity.com/paper/1910.05313/) (2019)
- [Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2212.10693/) (2022)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [Adapting Software Architectures to Machine Learning Challenges](https://mlsanity.com/paper/2105.12422/) (2021)
- [Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systems](https://mlsanity.com/paper/2003.05434/) (2020)
- [Engineering Reliable Deep Learning Systems](https://mlsanity.com/paper/1910.12582/) (2019)
- [On Misbehaviour and Fault Tolerance in Machine Learning Systems](https://mlsanity.com/paper/2109.07989/) (2021)
- [Multi-zone HVAC Control with Model-Based Deep Reinforcement Learning](https://mlsanity.com/paper/2302.00725/) (2023)
- [A Large-Scale Study of Model Integration in ML-Enabled Software Systems](https://mlsanity.com/paper/2408.06226/) (2024)
- [A Survey of AI Reliance](https://mlsanity.com/paper/2408.03948/) (2024)
- [Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository](https://mlsanity.com/paper/2303.13173/) (2023)
- [Ventilation and Temperature Control for Energy-efficient and Healthy Buildings: A Differentiable PDE Approach](https://mlsanity.com/paper/2403.08996/) (2024)
- [Data Management for Building Information Modelling in a Real-Time Adaptive City Platform](https://mlsanity.com/paper/2103.04924/) (2021)
- [Data-driven HVAC Control Using Symbolic Regression: Design and Implementation](https://mlsanity.com/paper/2304.03078/) (2023)
- [Digital Twins for Software Engineering Processes](https://mlsanity.com/paper/2510.05768/) (2025)

## Abstract (arXiv)

Building operations are energy-inefficient. Artificial Intelligence (AI)-driven control systems promise benefits through optimization and predictive control, but deploying them in real buildings reveals a significant software engineering (SE) challenge. SE for AI practices assume digital environments where failures mean poor user experience. Buildings are different. A bad control decision wastes energy irreversibly, violates occupant comfort, or accelerates equipment wear. Although actual safety-critical failures are rare, as real building automation systems are inherently fault-tolerant, the physical and lasting nature of even minor failures fundamentally changes SE4AI requirements. Rooted in two interdisciplinary research projects in civil engineering and computer science that target the AI-driven optimization of building operations, we identify the missing perspectives in SE4AI that currently stymie the successful deployment of AI-based systems for building operations. We further share lessons learned and best practices, and discuss broader implications for engineering AI-driven building operations and cyber-physical systems more generally. Our work proposes a foundation for SE4AI in systems where failure has physical consequences - one the research agenda below will need to validate.