Experiments as Code and its application to VR studies in human-building interaction

• Published in: Scientific reports 2024-04, Vol.14 (1), p.9883-9883, Article 9883
• Main Authors: Aguilar, Leonel, Gath-Morad, Michal, Grübel, Jascha, Ermatinger, Jasper, Zhao, Hantao, Wehrli, Stefan, Sumner, Robert W., Zhang, Ce, Helbing, Dirk, Hölscher, Christoph
• Format: Article
• Language: English
• Subjects: 639/166/986; 704/844/1759; Automation; Experiments; Humanities and Social Sciences; multidisciplinary; Reproducibility; Science; Science (multidisciplinary); Technicians
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-60791-3

Experiments as Code (ExaC) is a concept for reproducible, auditable, debuggable, reusable, & scalable experiments. Experiments are a crucial tool to understand Human-Building Interactions (HBI) and build a coherent theory around it. However, a common concern for experiments is their auditability and reproducibility. Experiments are usually designed, provisioned, managed, and analyzed by diverse teams of specialists (e.g., researchers, technicians, engineers) and may require many resources (e.g., cloud infrastructure, specialized equipment). Although researchers strive to document experiments accurately, this process is often lacking. Consequently, it is difficult to reproduce these experiments. Moreover, when it is necessary to create a similar experiment, the “wheel is very often reinvented”. It appears easier to start from scratch than trying to reuse existing work. Thus valuable embedded best practices and previous experiences are lost. In behavioral studies, such as in HBI, this has contributed to the reproducibility crisis. To tackle these challenges, we propose the ExaC paradigm, which not only documents the whole experiment, but additionally provides the automation code to provision, deploy, manage, and analyze the experiment. To this end, we define the ExaC concept, provide a taxonomy for the components of a practical implementation, and provide a proof of concept with an HBI desktop VR experiment that demonstrates the benefits of its “as code” representation, that is, reproducibility, auditability, debuggability, reusability, & scalability.