Test Suite Reduction for Self-Organizing Systems: A Mutation-Based Approach

Abstract

We study regression testing and test suite reduction for self-organizing (SO) systems. The complex environments of SO systems typically require large test suites. The physical distribution of their components and their history-dependent behavior, however, make test execution very expensive. Consequently, an efficient test suite reduction mechanism is needed. The fundamental characteristic of SO systems is their ability to reconfigure themselves. We thus in- vestigate a mutation-based approach concentrating on reconfigura- tions, more specifically the communication between the distributed components in reconfigurations. Due to distribution, we argue for an explicit consideration of higher-order mutants and find a short- cut that makes the number of test cases to execute before reduction feasible. For the reduction task, we evaluate the applicability of two existing clustering techniques, Affinity Propagation and Dissimilar- ity-based Sparse Subset Selection. It turns out that these techniques are able to drastically reduce the original test suite while retaining a good mutation score. We discuss the approach by means of a test suite for a self-organizing production cell as a running example.