# A Model-Driven Approach for Conducting Simulation Experiments
## 1 Introduction  
Modeling and simulation have become a key tool in many sciences and engineering disciplines [1]. A simulation study is usually characterized by iterative model refinements, intertwined with problem analysis, conceptual modeling, and various experimentation activities, as illustrated in modeling and simulation life cycles [2, 3, 4] (see Figure 1). Due to their complexity, there is a high demand for approaches that allow simulation studies to be conducted in a more effective and systematic manner. Especially, support for conductingsimulation experiments is of increasing interest, as they drive important activities, such as the calibration, validation, and analysis of models, and are essential for the reproducibility of simulation results. With new computational power, the new availability of data, and the thereby strengthened role of simulation experiments in advancing science within the different application domains, the requirements referring to simulation studies and experiments are becoming more rigorous. In particular, there are increasing requirements regarding the correct usage of sensitivity and uncertainty analysis [5], but also for calibration and validation to build realistic models [6], as well as increasing demand for open, replicable, and transparent methodologies [7]. In light of these new requirements, modelers that wish to create reproducible simulation experiments in an efficient and effective manner face new challenges. Especially, modelers working on interdisciplinary projects, where a variety of diverse simulation approaches and methods need to be brought together, would benefit from an easy-to-use tool that supports them in specifying, executing, and managing simulation experiments. However, also engineers working on modeling and simulation software have to account for these new challenges and would gain from a more structured and systematic approach for dealing with the increasing variety and complexity of the available specification languages (e.g., \"Simulation Experiment Specification via a Scala Layer\" [8] and \"Simulation Experiment Description Markup Language\" [9]), reporting guidelines (e.g., for finite element analysis studies in biomechanics [10]), frameworks (e.g., the Simulation Automation Framework for Experiments [11]), types of analyses and methods (e.g., experiment designs [12]), and the myriad of applications, which require (partly) domain-specific solutions. Model-driven-engineering (MDE)-based approaches can provide additional support in the specification, execution, and management of simulation experiments and resolve issues of interoperability, reproducibility, reusability, etc. In an MDE approach, meta models require certain experiment inputs, guide users through the experiment specification, and finally, generate executable code [13]. Current MDE approaches for simulation experiments realize and demonstrate this for the generation of experiment designs for parameter scans [13] and hypothesis testing [14]. However, they concentrate on specific fields of simulation, support only a specific experiment type, or are tightly coupled with specific tools. As a consequence, their applicability and, thus, their impact on the way simulation experiments are conducted are limited. Furthermore, some typical benefits of MDE [15], such as improved knowledge sharing and further automation, are not yet available or not fully exploited for simulation experiments. Thus, a more general approach is desirable that can support simulation experiments more broadly, and thus, assist in conducting entire simulation studies more effectively and systematically. Figure 1: Simplified life cycle of a modeling and simulation study with its main artifacts and activities. In this paper, we present a novel MDE framework that enhances the state-of-the-art of conducting simulation experiments in the following ways:  
* _Improved knowledge sharing across domains_: Experiment Meta models make knowledge about the structure and ingredients of simulation experiments explicit.