Asynchronous Learning for Service Composition

Abstract

Correctness of system compositions is automatically ensured by using formal behavioural models of services. However, such models are not always provided. We present a model inference technique for blackbox asynchronous services, that interleaves behavioural exploration and incremental model refinement. To save learning effort, only behaviour relevant to the desired system specification is explored. Compared to existing inference techniques that assume only controllable behaviour, our method addresses also uncontrollable events. Experimental results show that obtained models can be successfully used for a safe composition.