Added a new module which provides an interface for pyCPA. pyCPA is a pragmatic Python implementation of Compositional Performance Analysis (aka the SymTA/S approach provided by Symtavision) used for research in worst-case timing analysis. The source of pyCPA is available at http://code.google.com/p/pycpa/.
This new interface allows to perform worst-case timing analysis to acquire metrics such as worst-case response times of tasks in SMFF models. This can be used within the generation process to e.g. generate models that have a certain slack w.r.t. task deadlines. Another option is to use the pyCPA analysis as reference for the evaluation of timing analysis approaches. You could also use SMFF and pyCPA to develop optimization algorithms for real-time systems without having to implement your own timing analysis.
Together SMFF and pyCPA will provide a powerful combination of testcase generation and reference timing analysis.
Streamlined the system model by removing unused fields. Furthermore, made the system model even more extendible (tasklinks now carry profiles and the generalization to abstract scheduling parameters has been completed). As a result, the XML format has changed a bit.
In addition to the changes in the system model, I fixed a minor bug in the TaskChainPriorityAssigner.
