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Incremental and Commutative Composition of State-Machine Models of Features

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MiSE15c.pdf (190.6 KB)

Date

2015-05

Authors

Beidu, Sandy
Atlee, Joanne M.
Shaker, Pourya

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IEEE

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.

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URI

https://doi.org/10.1109/MiSE.2015.10
 http://hdl.handle.net/10012/15377

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Waterloo Research
Computer Science