  A common concern in experimental research is the auditability and
reproducibility of experiments. Experiments are usually designed, provisioned,
managed, and analyzed by diverse teams of specialists (e.g., researchers,
technicians and engineers) and may require many resources (e.g. cloud
infrastructure, specialized equipment). Even though researchers strive to
document experiments accurately, this process is often lacking, making it hard
to reproduce them. Moreover, when it is necessary to create a similar
experiment, very often we end up "reinventing the wheel" as it is easier to
start from scratch than trying to reuse existing work, thus losing valuable
embedded best practices and previous experiences. In behavioral studies this
has contributed to the reproducibility crisis. To tackle this challenge, we
propose the "Experiments as Code" paradigm, where the whole experiment is not
only documented but additionally the automation code to provision, deploy,
manage, and analyze it is provided. To this end we define the Experiments as
Code concept, provide a taxonomy for the components of a practical
implementation, and provide a proof of concept with a simple desktop VR
experiment that showcases the benefits of its "as code" representation, i.e.,
reproducibility, auditability, debuggability, reusability, and scalability.
