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

Abstract: A Feature Model (FM) is a compact representation of all the products of a software product line. Automated analysis of FMs is rapidly gaining importance: new operations of analysis have been proposed, new tools have been developed to support those operations and different logical paradigms and algorithms have been proposed to perform them. Implementing operations is a complex task that easily leads to errors in analysis solutions. In this context, the lack of specific testing mechanisms is becoming a major obstacle hindering the development of tools and affecting their quality and reliability. In this article, we present FaMa Test Suite, a set of implementation–independent test cases to validate the functionality of FM analysis tools. This is an efficient and handy mechanism to assist in the development of tools, detecting faults and improving their quality. In order to show the effectiveness of our proposal, we evaluated the suite using mutation testing as well as real faults and tools. Our results are promising and directly applicable in the testing of analysis solutions. We intend this work to be a first step toward the development of a widely accepted test suite to support functional testing in the community of automated analysis of feature models.

The increasing complexity and cost of software-intensive systems has led developers to seek ways of increasing software reusability. One software reuse approach is to develop a Software Product-line (SPL), which is a reconfigurable software architecture that can be reused across projects. Creating configurations of the SPL that meets arbitrary requirements is hard. Existing research has focused on techniques that produce a configuration of the SPL in a single step. This paper provides three contributions to the study of multi-step configuration for SPLs. First, we present a formal model of multi-step SPL configuration and map this model to constraint satisfaction problems (CSPs). Second, we show how solutions to these CSP configuration problem CSPs can be derived automatically with a constraint solver. Third, we present empirical results demonstrating that our CSP-based technique can solve multi-step configuration problems involving hundreds of features in seconds. 1

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Abstract—The increasing complexity and cost of softwareintensive systems has led developers to seek ways of reusing software components across development projects. One approach to increasing software reusability is to develop a Software Product-line (SPL), which is a software architecture that can be reconfigured and reused across projects. Rather than developing software from scratch for a new project, a new configuration of the SPL is produced. It is hard, however, to find a configuration of the SPL that meets an arbitrary requirement set and does not violate any configuration constraints in the SPL. Existing research has focused on techniques that produce a configuration of the SPL in a single step. Budgetary constraints or other restrictions, however, may require multi-step configuration processes. For example, an automotive manufacturer may want to produce a series of configurations of a car over a span of years without exceeding a yearly budget to add features. This paper provides three contributions to the study of multistep configuration for SPLs. First, we present a formal model of multi-step SPL configuration and map this model to constraint satisfaction problems (CSPs). Second, we show how solutions to these SPL configuration problems can be automatically derived with a constraint solver by mapping them to CSPs. Third, we present empirical results demonstrating that our CSP-based reasoning technique can scale to SPL models with hundreds of features and multiple configuration steps. I.

Abstract—Feature modeling is widely used in software product-line engineering to capture the commonalities and variabilities within an application domain. As feature models evolve, they can become very complex with respect to the number of features and the dependencies among them, which can cause the product derivation based on feature selection to become quite time consuming and error prone. We address this problem by presenting techniques to find good feature selection sequences that are based on the number of products that contain a particular feature and the impact of a selected feature on the selection of other features. Specifically, we identify a feature selection strategy, which brings up highly selective features early for selection. By prioritizing feature selection based on the selectivity of features our technique makes the feature selection process more efficient. Moreover, our approach helps with the problem of unexpected side effects of feature selection in later stages of the selection process, which is commonly considered a difficult problem. We have run our algorithm on the e-Shop and Berkeley DB feature models and also on some automatically generated feature models. The evaluation results demonstrate that our techniques can shorten the product derivation processes significantly.