To prevent ill-formed configurations, highly configurable software often allows defining constraints over the available options. As these constraints can be complex, fixing a configuration that violates one or more constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix or a very long fix list, difficult for the user to identify the desirable fix; and (2) they do not fully support non-Boolean constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the ranges of values for these options. We also design an algorithm that automatically generates range fixes for a violated constraint. We have evaluated our approach with three different strategies for handling constraint interactions, on data from nine open source projects over two configuration platforms. The evaluation shows that our notion of range fix leads to mostly simple yet complete sets of fixes, and our algorithm is able to generate fixes within one second for configuration systems with a few thousands options and constraints.

Variability models represent the common and variable features of products in a product line. Since the introduction of FODA in 1990, several variability modeling languages have been proposed in academia and industry, followed by hundreds of research papers on variability models and modeling. However, little is known about the practical use of such languages. We study the constructs, semantics, usage, and associated tools of two variability modeling languages, Kconfig and CDL, which are independently developed outside academia and used in large and significant software projects. We analyze 128 variability models found in 12 open--source projects using these languages. Our study 1) supports variability modeling research with empirical data on the real-world use of its flagship concepts. However, we 2) also provide requirements for concepts and mechanisms that are not commonly considered in academic techniques, and 3) challenge assumptions about size and complexity of variability models made in academic papers. These results are of interest to researchers working on variability modeling and analysis techniques and to designers of tools, such as feature dependency checkers and interactive product configurators.

To prevent ill-formed configurations, highly configurable software often allows defining constraints over the available options. As these constraints can be complex, fixing a configuration that violates one or more constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix, failing to cover the solution that the user wants; and (2) they do not fully support non-Boolean constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the ranges of values for these options. We also design an algorithm that automatically generates range fixes for a violated constraint. We have evaluated our approach with three different strategies for handling constraint interactions, on data from five open source projects. Our evaluation shows that, even with the most complex strategy, our approach generates complete fix lists that are mostly short and concise, in a fraction of a second.

Variability modeling, and in particular feature modeling, is a central element of model-driven software product line architectures. Such architectures often emerge from legacy code, but, unfortunately creating feature models from large, legacy systems is a long and arduous task.

Feature models describe the common and variable characteristics of a product line. Their advantages are well recognized in product line methods. Unfortunately, creating a feature model for an existing project is time-consuming and requires substantial effort from a modeler.

Variability models represent the common and variable features of products in a product line. Several variability modeling languages have been proposed in academia and industry; however, little is known about the practical use of such languages. We study and compare the constructs, semantics, usage and tools of two variability modeling languages, Kconfig and CDL. We provide empirical evidence for the real-world use of the concepts known from variability modeling research. Since variability models provide basis for automated tools (feature dependency checkers and product configurators), we believe that our findings will be of interest to variability modeling language and tool designers.

We present probabilistic feature models (PFMs) and illustrate their use by discussing modeling, mining and interactive configuration. PFMs are formalized as a set of formulas in a certain probabilistic logic. Such formulas can express both hard and soft constraints and have a well defined semantics by denoting a set of joint probability distributions over features. We show how PFMs can be mined from a given set of feature configurations using data mining techniques. Finally, we demonstrate how PFMs can be used in configuration in order to provide automated support for choice propagation based on both hard and soft constraints. We believe that these results constitute solid foundations for the construction of reverse engineering tools for software product lines and configurators using soft constraints.