Abstract. The transition from classical B [2] to the Event-B language and method [3] has seen the removal of some forms of model structuring and composition, with the intention of reinventing them in future. This work contributes to that reinvention. Inspired by a proposed method for state-based decomposition and refinement [5] of an Event-B model, we propose a familiar parallel event composition (over disjoint state variable lists), and the less familiar event fusion (over intersecting state variable lists). A brief motivation is provided for these and other forms of composition of models, in terms of feature-based modelling. We show that model consistency is preserved under such compositions. More significantly we show that model composition preserves refinement.
Event composition and event fusion, introduced in REF , are similar to composition of model programs, where event composition assumes disjoint state variables, whereas event fusion allows shared state variables.