Most of the frameworks or assistance systems for experiment specification do not provide a process explicitly based on formally specified hypotheses. This deficiency leads to inaccurate or insufficient record of an experiment, decreasing the trustworthiness and reproducibility of the experiment. Moreover, the wide variety of models, metamodels, tools, and data for experimentation requires Global Model Management (GMM) that is utilizing Model-Driven Engineering techniques, facilitates

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... on, sharing, reusability, and replicability of simulation experiments. In this study, we strive to illustrate how to support simulation experimentation with hypotheses as a scientific workflow through GMM with an extension to the Simulation Experiment Description Mark-up Language (SED-ML). In particular, we present a megamodel built to serve as a repository to manage the artefacts employed in a simulation experiment. Based on the SED-ML, and enriched with hypothesis handling, our megamodel attempts to address all the phases of a simulation experiment, including specification, input data generation, execution, and output data analysis.