Customizable programs and program families provide user-selectable features to allow users to tailor a program to an application scenario. Knowing in advance which feature selection yields the best performance is difficult because a direct measurement of all possible feature combinations is infeasible. Our work aims at predicting program performance based on selected features. However, when features interact, accurate predictions are challenging. An interaction occurs when a particular feature combination has an unexpected influence on performance. We present a method that automatically detects performance-relevant feature interactions to improve prediction accuracy. To this end, we propose three heuristics to reduce the number of measurements required to detect interactions. Our evaluation consists of six real-world case studies from varying domains (e.g., databases, encoding libraries, and web servers) using different configuration techniques (e.g., configuration files and preprocessor flags). Results show an average prediction accuracy of 95%.

Software product lines (SPLs) and adaptive systems aim at variability to cope with changing requirements. Variability can be described in terms of features, which are central for development and configuration of SPLs. In traditional SPLs, features are bound statically before runtime. By contrast, adaptive systems support feature binding at runtime and are sometimes called dynamic SPLs (DSPLs). DSPLs are usually built from coarse-grained components, which reduces the number of possible application scenarios. To overcome this limitation, we closely integrate static binding of traditional SPLs and runtime adaptation of DSPLs. We achieve this integration by statically generating a tailor-made DSPL from a highly customizable SPL. The generated DSPL provides only the runtime variability required by a particular application scenario and the execution environment. The DSPL supports self-configuration based on coarse-grained modules. We provide a feature-based adaptation mechanism that reduces the effort of computing an optimal configuration at runtime. In a case study, we demonstrate the practicability of our approach and show that a seamless integration of static binding and runtime adaptation reduces the complexity of the adaptation process.

Feature orientation is an emerging paradigm of software development. It supports the automatic generation of large scale software systems from a set of features. A feature is a unit of functionality that satisfies a requirement, represents a design decision, and provides a potential configuration option. The goal of feature-oriented software development (FOSD) is to consider and trace the features of a software system during all phases of the software life cycle from analysis and design to implementation and testing. The aim of the FOSD workshop is to stimulate collaboration between researchers working on FOSD and related areas.

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 non-functional 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 non-functional properties for each possible product of a product line. Hence, we propose an approach to predict a product's non-functional 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.

A software product line (SPL) enables stakeholders to derive different software products for a domain while providing a high degree of reuse of their code units. Software products are derived in a configuration process by composing different code units. The configuration process becomes complex if SPLs contain hundreds of features. In many cases, a stakeholder is not only interested in functional but also in non-functional properties of a desired product. Because SPLs can be used in different application scenarios alternative implementations of already existing functionality are developed to meet special non-functional requirements, like restricted binary size and performance guarantees. To enable these complex configurations we discuss and present techniques to measure non-functional properties of software modules and use these values to compute SPL configurations optimized to the users needs.

Software product lines (SPLs) are used to create tailor-made software products by managing and composing reusable assets. Generating a software product from the assets of an SPL is possible statically before runtime or dynamically at load-time or runtime. Both approaches have benefits and drawbacks with respect to composition flexibility, performance, and resource consumption. Which type of composition is preferable should be decided by taking the application scenario into account. Current tools and languages, however, force a programmer to decide between static and dynamic composition during development. In this paper, we present an approach that employs code generation to support static and dynamic composition of features of a single code base. We offer an implementation on top of FeatureC++, an extension of the C++ programming language that supports software composition based on features. To simplify dynamic composition and to avoid creation of invalid products we furthermore provide means to (1) validate the correctness of a composition at runtime, (2) automatically instantiate SPLs in case of stand-alone applications, and (3) automatically apply interaction code of crosscutting concerns.