Abstract. Feature models are used to specify members of a product-line. Despite years of progress, contemporary tools provide limited support for feature constraints and offer little or no support for debugging feature models. We integrate prior results to connect feature models, grammars, and propositional formulas. This connection allows arbitrary propositional constraints to be defined among features and enables off-the-shelf satisfiability solvers to debug feature models. We also show how our ideas can generalize recent results on the staged configuration of feature models.

Abstract. Although a feature model can represent commonalities and variabilities in a very concise taxonomic form, features in a feature model are merely symbols. Mapping features to other models, such as behavioral or data specifications, gives them semantics. In this paper, we propose a general template-based approach for mapping feature models to concise representations of variability in different kinds of other models. We show how the approach can be applied to UML 2.0 activity and class models and describe a prototype implementation. 1

Model transformations are touted to play a key role in Model Driven Developmente. Although well-established standards for creating metamodels such as the Meta-Object Facility exist, there is currently no mature foundation for specifying transformations among models. We propose a framework for the classification of several existing and proposed model transformation approaches. The classification framework is given as a feature model that makes explicit the different design choices for model transformations. Based on our analysis of model transformation approaches, we propose a few major categories in which most approaches fit.

Abstract Feature modeling is a key technique for capturing commonalities and variabilities in system families and product lines. In this paper, we propose a cardinality-based notation for feature modeling, which integrates a number of existing extensions of previous approaches. We then introduce and motivate the novel concept of staged configuration. Staged configuration can be achieved by the stepwise specialization of feature models or by multi-level configuration, where the configuration choices available in each stage are defined by separate feature models. Staged configuration is important because in a realistic development process, different groups and different people make product configuration choices in different stages. Finally, we also discuss how multi-level configuration avoids a breakdown between the different abstraction levels of individual features. This problem, sometimes referred to as “analysis paralysis”, easily occurs in feature modeling because features can denote entities at arbitrary levels of abstractions within a system family. Key words: Software product lines, system families, domain analysis, software configuration

Abstract. Feature modeling is an important approach to capturing commonalities and variabilities in system families and product lines. In this paper, we propose a cardinality-based notation for feature modeling, which integrates a number of existing extensions of previous approaches. We then introduce and motivate the novel concept of staged configuration. Staged configuration can be achieved by the stepwise specialization of feature models. This is important because in a realistic development process, different groups and different people eliminate product variability in different stages. We also indicate how cardinality-based feature models and their specialization can be given a precise formal semantics. 1

Software factories have been proposed as a comprehensive and integrative approach to generative software development. Feature modeling has several applications in generative software development, including domain analysis, product-line scoping, and feature-based product specification. This paper reports on our recent progress in cardinalitybased feature modeling and its support for expressing additional constraints. We show that the Object-Constraint Language (OCL) can adequately capture such constraints. Furthermore, we identify a set of facilities based on constraint satisfaction that can be provided by feature modeling and feature-based configuration tools and present a prototype implementing some of these facilities. We report on our experience with the prototype and give directions for future work.

Feature modeling is a notation and an approach for modeling commonality and variability in product families. In their basic form, feature models contain mandatory/optional features, feature groups, and implies and excludes relationships. It is known that such feature models can be translated into propositional formulas, which enables the analysis and configuration using existing logicbased tools. In this paper, we consider the opposite translation problem, that is, the extraction of feature models from propositional formulas. We give an automatic and efficient procedure for computing a feature model from a formula. As a side effect we characterize a class of logical formulas equivalent to feature models and identify logical structures corresponding to their syntactic elements. While many different feature models can be extracted from a single formula, the computed model strives to expose graphically the maximum of the original logical structure while minimizing redundancies in the representation. The presented work furthers our understanding of the semantics of feature modeling and its relation to logics, opening avenues for new applications in reverse engineering and refactoring of feature models.

Software product line modeling has received a great deal of attention for its potential in fostering reuse of software artifacts across development phases. Research on the testing phase, has focused on identifying the potential for reuse of test cases across product line instances. While this offers potential reductions in test development effort for a given product line instance, it does not focus on and leverage the fundamental abstraction that is inherent in software product lines- variability. In this paper, we illustrate how rich software product line modeling notations can be mapped onto an underlying relational model that captures variability in the feasible product line instances. This relational model serves as the semantic basis for defining a family of coverage criteria for testing of a product line. These criteria make it possible to accumulate test coverage information for the product line itself over the course of multiple product line instance development efforts. Cumulative coverage, in turn, enables targeted testing efforts for new product line instances. We describe how combinatorial interaction testing methods can be applied to define test configurations that achieve a desired level of coverage and identify challenges to scaling such methods to large, complex software product lines.

Features express the variabilities and commonalities among programs in a software product line (SPL). A feature model defines the valid combinations of features, where each combination corresponds to a program in an SPL. SPLs and their feature models evolve over time. We classify the evolution of a feature model via modifications as refactorings, specializations, generalizations, or arbitrary edits. We present an algorithm to reason about feature model edits to help designers determine how the program membership of an SPL has changed. Our algorithm takes two feature models as input (before and after edit versions), where the set of features in both models are not necessarily the same, and it automatically computes the change classification. Our algorithm is able to give examples of added or deleted products and efficiently classifies edits to even large models that have thousands of features. 1

A mechanically formalized feature modeling metamodel is presented. This theory is a generic higher-order formalization of a mathematical model synthesizing several feature modeling approaches found in the literature. This meta-model support not only a better understanding of the various approaches to feature modeling, but also supports reasoning about and within feature model approaches, feature models, and on feature trees and their configurations. 1