An Approach for Isolated Testing of Self-Organization Algorithms
This work provides a domain-specific solution for researchers and engineers dealing with self-organization algorithms, but it is incremental as it builds on existing model-based testing approaches.
The paper tackles the problem of testing self-organization algorithms by developing a systematic, automated framework that addresses challenges like non-determinism and complex state spaces, and demonstrates its application to partitioning-based algorithms in a smart-grid context.
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.