An Empirical Analysis of the Mutation Operator for Run-Time Adaptive Testing in Self-Adaptive Systems

A self-adaptive system (SAS) can reconfigure at run time in response to uncertainty and/or adversity to continually deliver an acceptable level of service. An SAS can experience uncertainty during execution in terms of environmental conditions for which it was not explicitly designed as well as unanticipated combinations of system parameters that result from a self-reconfiguration or misunderstood requirements. Run-time testing provides assurance that an SAS continually behaves as it was designed even as the system reconfigures and the environment changes. Moreover, introducing adaptive capabilities via lightweight evolutionary algorithms into a run-time testing framework can enable an SAS to effectively update its test cases in response to uncertainty alongside the SAS's adaptation engine while still maintaining assurance that requirements are being satisfied. However, the impact of the evolutionary parameters that configure the search process for run-time testing may have a significant impact on test results. Therefore, this paper provides an empirical study that focuses on the mutation parameter that guides online evolution as applied to a run-time testing framework, in the context of an SAS.