# A Model-Driven Approach for Conducting Simulation Experiments
## 2 Related Work  
Model-driven approaches for modeling and simulation so far have mostly focused on the generation of (executable) simulation models [20; 21; 22; 23], simulation on distributed architectures [24], or multi-formalism modeling [25].  
With regard to simulation experiments, MDE has been applied in the context of experiment design and hypothesis testing. [PERSON] et al. [13] support the specification and execution of simple simulation experiments based on factorial designs, e.g., parameter sweeps. The approach by [PERSON] et al. [26] is based on one's own domain-specific language for experiment design and semi-automatic transformations to multiple execution platforms. [PERSON] et al. [14] propose the generation of experiment designs from hypotheses and outline an overarching goal-hypothesis-experiment framework.  
Simulation experiments are also generated without explicit meta models or MDE frameworks. These usually target specific problems within the modeling and simulation life cycle. [PERSON] [27], for instance, generates efficient experiment designs for hypothesis testing based on formally specified hypotheses. [PERSON] et al. [28] support the successive composition of models by generating simulation experiments for composed models. More specifically, statistical model checking experiments of individual models are reused and adapted for the composed model based on explicit experiment specifications and explicit behavioral properties. Similarly, properties of extended or refined models can be checked [29]. [PERSON] et al. [30] focus on supporting the comparison of electrophysiology models using typical experiment types of that domain, i.e., time course analyses and steady-state analysis. Concrete experiment specifications (protocols) conducted with previous models are stored in an online database and can be repeated with new models. [PERSON] et al. [18] present a pipeline for generating a number of different experiment types including local sensitivity analysis, optimization, and statistical model checking. This pipeline was expanded by experiment schemas in [PERSON] et al. [31] to support multiple simulation tools and approaches.  
In this paper, we further generalize the latter experiment generation approach using the MDE paradigm. The main novelties are (a) the separation into two types of meta models (domain meta models and experiment type meta models) and a corresponding composition mechanism, (b) a meta model repository, (c) bi-directional transformations and execution bindings for a variety of tools, and (d) a command-line interface (CLI) for easy integration with other frameworks. As a result, our framework flexibly supports the conducting of diverse, complex experiment types, and it enables the sharing and reusing of knowledge even across different modeling domains and tools. Furthermore, our framework provides the foundation for automatically generating and reusing simulation experiments during the various phases of a simulation study, and thus for conducting entire simulation studies in a more systematic and effective manner.  
The development of meta models, as part of an MDE framework, is closely related to the development of ontologies as both represent knowledge in a structured manner. Ontologies for modeling and simulation have been developed to facilitate the data exchange, interoperability, and annotation of simulation resources [32]. Most ontologies define the components and concepts of simulation models instead of simulation experiments, e.g., the Discrete Event Simulation Component Ontology (DESC) [33] or the Discrete-event Modeling Ontology (DeMO) [34]. However, some ontologies overlap with meta models for simulation experiments, e.g., the ontology for capturing physics-based models by [PERSON] [35] and our meta model for FEA simulation studies (see Section 5) both include the boundary condition and material properties.