In this paper, we present a technique for incremental and commutative composition of state-machine models of features, using the Feature House framework. The inputs to Feature House 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 Feature House.

Product line (PL) engineering promotes the development of families of related products, where individual products are differentiated by which optional features they include. Modelling and analyzing requirements models of PLs allows for early detection and correction of requirements errors -- including unintended feature interactions, which are a serious problem in feature-rich systems. A key challenge in analyzing PL requirements is the efficient verification of the product family, given that the number of products is too large to be verified one at a time. Recently, it has been shown how the high-level design of an entire PL, that includes all possible products, can be compactly represented as a single model in the SMV language, and model checked using the NuSMV tool. The implementation in NuSMV uses BDDs, a method that has been outperformed by SAT-based algorithms. In this paper we develop PL model checking using two leading SAT-based symbolic model checking algorithms: IMC and IC3. We describe the algorithms, prove their correctness, and report on our implementation. Evaluating our methods on three PL models from the literature, we demonstrate an improvement of up to 3 orders of magnitude over the existing BDD-based method.