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.

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Features are commonly used to describe functional and nonfunctional aspects of software. To effectively evolve and reuse features, their location in software assets has to be known. However, locating features is often difficult given their crosscutting nature. Once implemented, the knowledge about a feature's location quickly deteriorates, requiring expensive recovering of these locations. Manually recording and maintaining traceability information is generally considered expensive and error-prone. In this paper, we argue to the contrary and hypothesize that such information can be effectively embedded into software assets, and that arising costs will be amortized by the benefits of this information later during development. We test this hypothesis in a study where we simulate the development of a product line of cloned/forked projects using a lightweight code annotation approach. We identify annotation evolution patterns and measure the cost and benefit of these annotations. Our results show that not only the cost of adding annotations, but also that of maintaining them is small compared to the actual development cost. Embedding the annotations into assets significantly reduced the maintenance cost because they naturally co-evolve with the assets. Our results also show that a majority of these annotations provides a benefit for feature-related code maintenance tasks, such as feature propagation and migrating clones into a platform.

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The decision-making process in Product Line Engineering (PLE) is often concerned with variant qualities such as cost, battery life, or security. Pareto-optimal variants, with respect to a set of objectives such as minimizing a variant's cost while maximizing battery life and security, are variants in which no single quality can be improved without sacrificing other qualities. We propose a novel method and a tool for visualization and exploration of a multi-dimensional space of optimal variants (i.e., a Pareto front). The visualization method is an integrated, interactive, and synchronized set of complementary views onto a Pareto front specifically designed to support PLE scenarios, including: understanding differences among variants and their positioning with respect to quality dimensions; solving trade-offs; selecting the most desirable variants; and understanding the impact of changes during product line evolution on a variant's qualities. We present an initial experimental evaluation showing that the visualization method is a good basis for supporting these PLE scenarios.

Cloning is widely used for creating new product variants. While it has low adoption costs, it often leads to maintenance problems. Long term reliance on cloning is discouraged in favor of systematic reuse offered by product line engineering (PLE) with a central platform integrating all reusable assets. Unfortunately, adopting an integrated platform requires a risky and costly migration. However, industrial experience shows that some benefits of an integrated platform can be achieved by properly managing a set of cloned variants.In this paper, we propose an incremental and minimally invasive PLE adoption strategy called virtual platform. Virtual platform covers a spectrum of strategies between ad-hoc clone and own and PLE with a fully-integrated platform divided into six governance levels. Transitioning to a governance level requires some effort and it provides some incremental benefits. We discuss tradeoffs among the levels and illustrate the strategy on an example implementation.