called *scenario*.[^4]

Semantically, scenarios represent the experience gained about the system’s behavior during development, including both successful ($S \vDash \gamma$) and unsuccessful ($S \nvDash \gamma$) test runs. As stated above, since we expect to operate in test spaces we cannot cover exhaustively, the knowledge about the areas we did cover is an important asset and likewise result of the systems engineering process.

Effectively, as we construct and evolve a system $S$ we want to construct and augment a set of scenarios $C = \{c_1, ..., c_n\}$ alongside with it. $C$ is also called a *scenario suite* and can be seen as a toolbox to test $S$’s adaptation abilities with respect to a fixed adaptation domain $\mathcal{A}$.

While formally abiding to Definition \[def:scenario\], scenarios can be encoded in various ways in practical software development, such as:

#### Sets of data points of expected or observed behavior.

Given a system $S' = X' \leadsto Y'$ whose behavior is desirable (for example a trained predecessor of our system or a watchdog component), we can create scenarios $(X', Y', g', f')$ with $g' = \top \iff S' \otimes E_i \models \gamma_i$ and $f' = \phi_i(S' \otimes E_i)$ for an arbitrary amount of elements $(E_i, 