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As there are many modeling methods of systems, there is always when designing a new system the problem which modeling method to use. The reason is that every modeling method designs the system from a specific viewpoint. However, all of these viewpoints are usually needed together in order to better design a system. It is also time costing to repeat integrating same information in every modeling method in order to be capable of analyzing the system from its viewpoint. In recent years, approaches to combine different modeling viewpoints together in a new modeling method or transforming one modeling method to another has been increasing. Especially there is a great focus on the relation between the data flow modeling and the control flow modeling as both of them are essential to design a good analyzed system. While the data-oriented descriptions are important for the architects to know about the flow of the data in the system and the data dependencies between the different components, they don't allow them to widely analyze the performance of the system as the control-flow-oriented ones do. On the other hand for some properties of the system, the analysis does not require a fine-grained description of the internal detailed behavior of system components which is offered by the control flow modeling. Our goal in this thesis is to deal with these both different available modeling methods of systems. We assume that we are already using data flow modeling to describe our desired system and that we want to further analyze the performance of the system and its control flow. We go through transforming the available data flow model (DFM) to a control flow model (CFM) stub containing all of the available information in the DFM, which can be then simulated with as few modifications. We define some conditions, which we consider that they must exist in the transformation process between DFMs and CFMs in order to be meaningful and valid. Based on these conditions we create mapping rules which maps the available elements in the DFM to their suitable patterns in the Palladio Component Model (PCM), which is a control-flow oriented description language, maintaining all information which is introduced in the DFM. We evaluate our work by going through the elements of the PCM, classifying them and calculating the ratio of the covered elements by the mapping rules to the total elements, which should be existing in order to get a meaningful stub. As a result we prove that we cover about 70 percent of the elements by the mapping rules while we show that the other 30 percent can't be extracted from the information available in the DFM.
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