The transformations achievable using the proposed framework correspond to very complex transformations on signal transition graphs. Hence, a very broad range of signal transition graphs can be synthesized. The only requirement is that the corresponding initial state graph is finite, connected, and has a consistent state assignment. Unlike previous methods, the initial STG need not be a live, safe, nor a free choice net. Performing transformations at the state graph level has the advantage that the requirements imposed on the initial STG are very weak. A constraint satisfaction framework is proposed that can guarantee necessary and sufficient conditions for a state graph assignment to result in a transformed state graph that is free of critical races. In this article, we propose a global assignment theory for encoding state graph transformations.
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