**5. Conclusion**

We described a complexity metric for concurrent software controlled systems or concurrent realizations of behaviour. The novel approach of creating a complexity metric for context switching involves the analysis of the switching frequency spectrum. We take a Fourier transform of the temporally distributed context switching events (*c(t)*) and treat that as a probability density function in frequency space (i.e. a normalized power spectrum). Then the entropy (*S*) of *p(f)* will generate a simple complexity measure.

The context switching metric can be used during system development as an analysis of alternative utility function. If several design options or algorithms are available, the contextswitching metric can be used as selection criteria to minimize inherent algorithmic complexity. It is comparable or equivalent to the Shannon information metric, which essentially measures entropy of a system.

As an alternative approach we compared this against a multi-scale entropy measure. Although more involved in construction, the multi-scale entropy can be used as an orthogonal metric, perhaps more useful for measuring temporal behaviours of a wide dynamic range or as a more detailed diagnostic tool. This will reveal finer structures in complexity than the single-scale metric can.
