Automatically exploring how uncertainty impacts behavior of dynamically adaptive systems

Abstract

A dynamically adaptive system (DAS) monitors itself and its execution environment to evaluate requirements satisfaction at run time. Unanticipated environmental conditions may produce sensory inputs that alter the self-assessment capabilities of a DAS in unpredictable and undesirable ways. Moreover, it is impossible for a human to know or enumerate all possible combinations of system and environmental conditions that a DAS may encounter throughout its lifetime. This paper introduces Loki, an approach for automatically discovering combinations of environmental conditions that produce requirements violations and latent behaviors in a DAS. By anticipating adverse environmental conditions that might arise at run time, Loki facilitates the identification of goals with inadequate obstacle mitigations or insufficient constraints to prevent such unwanted behaviors. We apply Loki to an autonomous vehicle system and describe several undesirable behaviors discovered.