  We provide a systematic approach for testing self-organization (SO)
algorithms. The main challenges for such a testing domain are the strongly
ramified state space, the possible error masking, the interleaving of
mechanisms, and the oracle problem resulting from the main characteristics of
SO algorithms: their inherent non-deterministic behavior on the one hand, and
their dynamic environment on the other. A key to success for our SO algorithm
testing framework is automation, since it is rarely possible to cope with the
ramified state space manually. The test automation is based on a model-based
testing approach where probabilistic environment profiles are used to derive
test cases that are performed and evaluated on isolated SO algorithms. Besides
isolation, we are able to achieve representative test results with respect to a
specific application. For illustration purposes, we apply the concepts of our
framework to partitioning-based SO algorithms and provide an evaluation in the
context of an existing smart-grid application.
