Software Product Line (SPL) Engineering has proved to be an effective method for software production. However, in the SPL community it is well recognized that variability in SPLs is increasing by the thousands. Hence, an automatic support is needed to deal with variability in SPL. Most of the current proposals for automatic reasoning on SPL are not devised to cope with extrafunctional features. In this paper we introduce a proposal to model and reason on an SPL using constraint programming. We take into account functional and extra-functional features, improve current proposals and present a running, yet feasible implementation.

Software product-lines (SPLs) are software architectures that can be readily reconfigured for different project requirements. A key part of an SPL is a model that captures the rules for reconfiguring the software. SPLs commonly use feature models to capture SPL configuration rules. Each SPL configuration is represented as a selection of features from the feature model. Invalid SPL configurations can be created due to feature conflicts introduced via staged or parallel configuration or changes to the constraints in a feature model. When invalid configurations are created, a method is needed to automate the diagnosis of the errors and repair the feature selections.This paper provides two contributions to research on automated configuration of SPLs. First, it shows how configurations and feature models can be transformed into constraint satisfaction problems to automatically diagnose errors and repair invalid feature selections. Second, it presents empirical results from diagnosing configuration errors in feature models ranging in size from 100 to 5,000 features. The results of our experiments show that our CSP-based diagnostic technique can scale up to models with thousands of features.

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a b s t r a c tOver the last two decades, software product lines have been used successfully in industry for building families of systems of related products, maximizing reuse, and exploiting their variable and configurable options. In a changing world, modern software demands more and more adaptive features, many of them performed dynamically, and the requirements on the software architecture to support adaptation capabilities of systems are increasing in importance. Today, many embedded system families and application domains such as ecosystems, service-based applications, and self-adaptive systems demand runtime capabilities for flexible adaptation, reconfiguration, and post-deployment activities. However, as traditional software product line architectures fail to provide mechanisms for runtime adaptation and behavior of products, there is a shift toward designing more dynamic software architectures and building more adaptable software able to handle autonomous decision-making, according to varying conditions. Recent development approaches such as Dynamic Software Product Lines (DSPLs) attempt to face the challenges of the dynamic conditions of such systems but the state of these solution architectures is still immature. In order to provide a more comprehensive treatment of DSPL models and their solution architectures, in this research work we provide an overview of the state of the art and current techniques that, partially, attempt to face the many challenges of runtime variability mechanisms in the context of Dynamic Software Product Lines. We also provide an integrated view of the challenges and solutions that are necessary to support runtime variability mechanisms in DSPL models and software architectures.

Feature models are widely used to model software product-line (SPL)