Incremental and Commutative Composition of State-Machine Models of Features

Abstract

In this paper, we present a technique for incre- mental and commutative composition of state-machine models of features, using the FeatureHouse framework. The inputs to FeatureHouse are feature state-machines (or state-machine fragments) modelled in a feature-oriented requirement modelling language called FORML and the outputs are two state-machine models: (1) a model of the whole product line with optional features guarded by presence conditions; this model is suitable for family-based analysis of the product line; and (2) an intermediate model of composition that facilitates incremental composition of future features. We discuss the challenges and benefits of our approach and our implementation in the FeatureHouse.