Runtime quality analysis such as resilience and load testing assists software engineers in identifying deviations of quality requirements in business-critical applications. Thus, different stakeholders need to reason about the business’ domain from a common point of view to refine the model to their needs. Domain-driven-Design empowers stakeholders to elaborate domain models in a concise manner using a Ubiquitous Language for communication. Modeling languages such as UML and MARTE are often too technical for this purpose. In contrast, Domain Storytelling naturally fits into this methodology by using pictographic storytelling and the Ubiquitous Language to collaboratively shape the domain in Domain Stories. However, three fundamental problems arise. (P1) Domain Storytelling does not offer to enrich domain models by quality properties like performance or reliability. (P2) Next, there is no solution available that transforms the annotated model into runtime quality analysis tests. (P3) In association with P1, there has not been a solution to report the analysis results on the domain level in the context of a Domain Stories. In the scope of this work, we have created an annotation-based modeling concept that extends Domain Storytelling by performance and reliability properties and report the analysis results to domain experts. Our modeling approach relates to annotation-based concepts from UML and MARTE as well as ATAM for finding a uniform template to describe different runtime quality analysis tests. We implemented this modeling concept into an existing Domain Story Modeler to create a prototype as a solution for P1 and P3. For this purpose, we have conducted four interviews with domain experts from two distinct business domains (taxing and insurance) to elicit requirements and find issues with current approaches towards solving P1, P2, and P3. We have gathered evidence that current solutions are similar to waterfall models in terms of communication and that domain experts lack the ability to efficiently express their needs on a domain level to software engineers. We summarized our findings and prepared twelve user stories which build up our prototype. After implementation, we have used one sample Domain Story from literature and field data from expert interviews to demonstrate that the transformation of the annotated domain models work using ChaosToolkit for resilience testing and JMeter for load testing. In the end, we evaluated our prototype in a qualitative study to figure out if our prototype is a solution to P1 and P3. Based on our findings, we concluded that our modeling concept greatly assists domain experts in their processes to enrich domain models with quality properties that are critical to their domain and make the analysis results available in an understandable manner. We have demonstrated that our modeling concept is capable of solving P2 in the future, thus, effectively allowing domain experts and software engineers to make their processes more efficient.