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Real-world feature models are typically too large and complex to be configured manually. In practice, configuration tasks are, therefore, accomplished by employing interactive configurators. After each explicit feature selection or deselection by the ...

The compilation of feature models into binary decision diagrams (BDDs) is a major challenge in the area of configurable systems analysis. Many large-scale feature models have been reported to exceed state-of-the-art compilation capabilities, e.g., for ...

Feature models are the de-facto standard in product line engineering to capture the commonalities and variability of systems. However, feature models provide little user guidance during configuration and are unable to represent configuration sequences. A ...

Network Function Virtualization (NFV) aims to replace traditional network functions running in proprietary hardware with software instances (i.e., Virtual Network Functions, VNFs) embedded in general-purpose virtualization solutions. Aware that ...

ML systems have become an essential tool for experts of many domains, data scientists and researchers, allowing them to find answers to many complex business questions starting from raw datasets. Nevertheless, the development of ML systems able to ...

Product lines are ubiquitous for managing variable systems. The variability of a product line is typically described in terms of a feature model. Analyzing a feature model gives insight into various aspects, such as the validity of a configuration of ...

Feature modeling is the "de facto" standard to describe the common and variant parts of software product lines. Different tools, approaches, and operations for the automated analysis of feature models (AAFM) have been proposed in the last 20 years. The ...

The variability of the common features in an Software Product Line (SPL) can be managed by an feature model, an artifact that consist of a tree-shaped diagram, that describe the features identified in the products and the possible relationships between ...

Software product line (SPL) engineering methodology assist to create a range of software products within less time and cost but with high quality by the reuse of core software assets, which has been tested. Thus, testing is crucial for ...

In the context of Software Product Lines (SPLs), evaluating the quality of a feature model is essential to ensure that errors in the early stages do not spread throughout the SPL. One way to evaluate a feature model is to use measures. However, measures ...

The paper describes a demonstration of pure::variants [2], a commercial tool for variant and variability management for product lines. The demonstration shows how flexible product line (PL) architectures can be built, tested and maintained by using the ...

In this paper, we give a brief overview on the tutorial "Using Feature Models to Manage Variability and Requirements Reuse".

The development of variable software, in general, and feature models, in particular, is an error-prone and time-consuming task. It gets increasingly more challenging with industrial-size models containing hundreds or thousands of features and ...

In recent years, the complexity of production plants and therefore of the underlying automation systems has grown significantly. This makes the manual design of automation systems increasingly difficult. As a result, errors are found only during ...

Goal models represent requirements and intentions of a software system. They play an important role in the development life cycle of software product lines (SPLs). In the domain engineering phase, goal models guide the development of variability in SPLs ...

Linux is a highly configurable operating-system kernel which has been widely studied in the context of software product lines over the past years. Understanding the challenges and perils of evolving and maintaining feature models of the size of Linux is ...

Software Product Lines (SPLs) are a mechanism for large-scale reuse where families of related software systems are represented in terms of commonalities and variabilities, e.g., using Feature Models (FMs). While FMs define all possible configurations of ...

Automated testing and debugging of knowledge bases (such as configuration knowledge bases and feature models) is an important contribution to manage knowledge evolution efficiently. However, existing approaches rely on the assumption of consistent test ...

Feature modeling has been widely used in domain engineering for the development and configuration of software products. A feature model represents the set of possible configurations to apply in a given context. Recently, this formalism was applied to ...

Feature models are a common way to represent variability requirements of software product lines by expressing the set of feature combinations that software products can have. Assuring quality of feature models is thus of paramount importance for ...