A Temporal Model for Interactive Diagnosis of Adaptive Systems

The evolving complexity of adaptive systems impairs our ability to deliver anomaly-free solutions. Fixing these systems require a deep understanding on the reasons behind decisions which led to faulty or suboptimal system states. Developers thus need diagnosis support that trace system states to the previous circumstances-targeted requirements, input context-that had resulted in these decisions. However, the lack of efficient temporal representation limits the tracing ability of current approaches. To tackle this problem, we describe a novel temporal data model to represent, store and query decisions as well as their relationship with the knowledge (context, requirements, and actions). We validate our approach through a use case based on the smart grid at Luxembourg.