We have conducted preliminary experiments to tests and compare the performance of our strategy with existing abstraction-refinement technique available in VIS. As our abstraction representation requires fairness constraints, we have chosen the \emph{incremental\_ctl\_verification} abstraction refinement technique as it supports CTL formulas and fairness constraints \cite{PardoHachtel98incremCTLMC} \cite{PardoHachtel97autoAbsMC}. We have executed and compared the execution time and the number of refinement iterations for two examples: VCI-PI platform consisting of Virtual Component Interface (VCI), a PI-Bus and VCI-PI protocol converter and a simplified version of a CAN bus platform consisting of 3 nodes and a CAN bus. The results have been obtained on a PC with an AMD Athlon dual-core processor 4450e and 1.8GB of RAM memory. \medskip \begin{table} [h] %\hspace*{-8mm} \begin{tabular}{cccc} \toprule \textbf{VCI} & \emph{Verification} & \emph{Verification} & \emph{Refinement} \\ \textbf{-PI}& \emph{Technique} & \emph{Time (s)} & \emph{Iteration} \\ \midrule \midrule & Prop. Selection & 2.2 & 1 \\ $\phi_1$ & Incremental & 18.1 & 0 \\ & Standard MC & 14.9 & - \\ \midrule & Prop\_Selection & 1.0 & 0 \\ $\phi_2$ & Incremental & 168.0 & 467 \\ & Standard MC & 14.9 & - \\ \bottomrule \end{tabular} \caption{\label{TabVCI_PI} Results on VCI-PI platform } \end{table} %\medskip \begin{table} [h] %\hspace*{-8mm} \begin{tabular}{cccc} \toprule \textbf{CAN} & \emph{Verification} & \emph{Verification} & \emph{Refinement} \\ \textbf{Bus} & \emph{Technique} & \emph{Time (s)} & \emph{Iteration} \\ \midrule \midrule & Prop. Selection & & \\ $\phi_1$ & Incremental & >1 day & 0 \\ & Standard MC & 51.9 & - \\ \midrule & Prop\_Selection & & \\ $\phi_2$ & Incremental & 57.3 & 0 \\ & Standard MC & 2.2 & - \\ \bottomrule \end{tabular} \caption{\label{TabCANBus} Results on CAN Bus platform } \end{table} \medskip Table 1 shows the... \TODO{Comments about Table 1, Table 2 and conclusion of Experimental Results}