An Approach for Isolated Testing of Self-Organization Algorithms

TL;DR

This paper introduces a systematic, automated testing framework for self-organization algorithms that addresses their complex, non-deterministic behavior and dynamic environments, ensuring reliable and application-specific validation.

Contribution

It presents a novel model-based testing approach with probabilistic environment profiles for isolated testing of self-organization algorithms, applicable to real-world scenarios.

Findings

Effective automation of testing process

Successful application to smart-grid algorithms

Achieved representative test results

Abstract

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.