Automatically RELAXing a Goal Model to Cope with Uncertainty

Abstract

AbstractDynamically adaptive systems (DAS) must cope with changing system and environmental conditions that may not have been fully understood or anticipated during development time. RELAX is a fuzzy logic-based specification language for making DAS requirements more tolerable to unanticipated environmental conditions. This paper presents AutoRELAX, an approach that generates RELAXed goal models that address environmental uncertainty by identifying which goals to RELAX, which RELAX operators to apply, and the shape of the fuzzy logic function that defines the goal satisfaction criteria. AutoRELAX searches for RELAXed goal models that enable a DAS to satisfy its functional requirements while balancing tradeoffs between minimizing the number of RELAXed goals and minimizing the number of adaptations triggered by minor and adverse environmental conditions. We apply AutoRELAX to an industry-provided network application that self-reconfigures in response to adverse environmental conditions, such as link failures.KeywordsUtility FunctionGoal ModelRequirement EngineerAdverse Environmental ConditionEnvironmental UncertaintyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.