Proceedings of the 2014 Winter Simulation Conference A. Tolk, S. Y. Diallo, I. O. Ryzhov, L. Yilmaz, S. Buckley, and J. A. Miller, eds. PERSPECTIVES ON LANGUAGES FOR SPECIFYING SIMULATION EXPERIMENTS Johannes Sch¨ utzel Danhua Peng Adelinde M. Uhrmacher Institute of Computer Science University of Rostock Albert-Einstein-Str. 22 18059 Rostock, GERMANY L. Felipe Perrone Department of Computer Science Bucknell University 1 Dent Drive Lewisburg, PA 17837, USA ABSTRACT Domain specific languages have been used in modeling and simulation as tools for model description. In recent years, the efforts toward enabling simulation reproducibility have motivated the use of domain specific languages also as the means with which to express experiment specifications. In simulation areas ranging from computational biology to computer networks, the emerging trend is to treat the experimentation process as a first class object. Domain specific languages serve to specify individual sub-tasks in this process, such as configuration, observation, analysis, and evaluation of experimental results. Additionally, they can be used in a broader scope, for instance, to describe formally the experiment’s goals. The research and development of domain specific languages for experiment specification explores all of these and additional possible applications. In this paper, we discuss various existing approaches for defining this type of domain specific languages and present a critical analysis of our findings. 1 INTRODUCTION The fidelity of computational models for simulation and the accuracy of the simulators that execute them are determining factors for the quality of a simulation study. This motivates the rigorous practitioner to make strong efforts to create models at the appropriate level of abstraction and faithfulness, to validate them carefully, and to create corresponding verified computational implementations. Designing and executing the simulation experiment, however, are tasks in the workflow of the simulation process that are just as important and just as complex. More often than not, the resulting product of the design of experiment (DOE) stage is a substantial amount of descriptive data that is used to drive the execution of the simulation study. These data include DOE entities such as factors (model parameters that are varied in the experiment design) and levels (specific values assigned to factors in the execution of the experiment), the DOE methodology used, and possibly resource identifiers for the computers involved in running the experiment, among other pieces of information. The complete description of the experiment, however, comprises much more information than just the DOE set up data. In general, it includes also a description of which data generated by the experiment are to be recorded and possibly additional records on control actions related to execution. Keeping an accurate and complete record of all the conditions that define the experiment is of utmost importance to the reproducibility of the experiment. As observed by Pawlikowski, Jeong, and Lee (2002) and Kurkowski, Camp, and Colagrosso (2005) in the scope of network simulation, and by Merali (2010) and Joppa et al. (2013) in general scientific computing, having complete records of the experiment increases the scientific rigor of the experiments and, consequently, also their credibility. In the Minimum Information About a Simulation Experiment (MIASE) standard proposed by K¨ ohn and Nov` ere (2008), the authors 2836 978-1-4799-7486-3/14/$31.00 ©2014 IEEE