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Abstract. This paper presents FeatureC++, a novel language extension to C++ that supports Feature-Oriented Programming (FOP) and Aspect-Oriented Programming (AOP). Besides well-known concepts of FOP languages, FeatureC++ contributes several novel FOP language features, in particular multiple inheritance and templates for generic programming. Furthermore, FeatureC++ solves several problems regarding incremental software development by adopting AOP concepts. Starting our considerations on solving these problems, we give a summary of drawbacks and weaknesses of current FOP languages in expressing incremental refinements. Specifically, we outline five key problems and present three approaches to solve them: Multi Mixins, Aspectual Mixin Layers, and Aspectual Mixins that adopt AOP concepts in different ways. We use FeatureC++ as a representative FOP language to explain these three approaches. Finally, we present a case study to clarify the benefits of FeatureC++ and its AOP extensions.

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Abstract-A software product line is a family of related software products, typically, generated from a set of common assets. Users can select features to derive a product that fulfills their needs. Often, users expect a product to have specific nonfunctional properties, such as a small footprint or a minimum response time. Because a product line can contain millions of products, it is usually not feasible to generate and measure nonfunctional properties for each possible product of a product line. Hence, we propose an approach to predict a product's nonfunctional properties, based on the product's feature selection. To this end, we generate and measure a small set of products, and by comparing the measurements, we approximate each feature's non-functional properties. By aggregating the approximations of selected features, we predict the product's properties. Our technique is independent of the implementation approach and language. We show how already little domain knowledge can improve predictions and discuss trade-offs regarding accuracy and the required number of measurements. Although our approach is in general applicable for quantifiable non-functional properties, we evaluate it for the non-functional property footprint. With nine case studies, we demonstrate that our approach usually predicts the footprint with an accuracy of 98 % and an accuracy of over 99 % if feature interactions are known.