Derivation of subset product lines in FeatureIDE

The development and configuration of software product lines can be challenging tasks. During development, engineers often need to focus on a particular subset of features that is relevant for them. In such cases, it would be beneficial to hide other features and their implementation. During product configuration, requirements of potentially multiple stakeholders need to be considered. Therefore, configuration often happens in stages, in which different people contribute configuration decisions for different features. Moreover, in some cases, stakeholders want to share a set of products rather than a specific one. In all these cases, the necessary operation is the same: some features from the product line are assigned a value (e.g., via a partial configuration) while other features remain configurable. In this work, we propose a subset operation that takes a product line and a partial configuration to derive a subset product line comprising only the desired subset of features and implementation artifacts. Furthermore, we present, evaluate, and publish our implementation of the proposed subset operation within the FeatureIDE framework

Automated Completion of Partial Configurations as a Diagnosis Task Using FastDiag to Improve Performance

The completion of partial configurations might represent an expensive computational task. Existing solutions, such as those which use modern constraint satisfaction solvers, perform a complete search, making them unsuitable on large-scale configurations. In this work, we propose an approach to define the completion of a partial configuration like a diagnosis task to solve it by applying the FastDiag algorithm, an efficient solution for preferred minimal diagnosis (updates) in the analyzed partial configuration. We evaluate our proposed method in the completion of partial configurations of random medium and large-size features models and the completion of partial configurations of a feature model of an adapted version of the Ubuntu Xenial OS. Our experimental analysis shows remarkable improvements in our solution regarding the use of classical CSP-based approaches for the same tasks.Ministerio de Ciencia, Innovación y Universidades RTI2018-101204-B-C22Agencia Estatal de Investigación TIN2017-90644-RED

Automated variability injection for graphical modelling languages

© ACM 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in International Conference on Generative Programming: Concepts and Experiences, https://doi.org/10.1145/3425898.3426957Model-based development approaches, such as Model-Driven Engineering (MDE), heavily rely on the use of modelling languages to achieve and automate software development tasks. To enable the definition of model variants (e.g., supporting the compact description of system families), one solution is to combine MDE with Software Product Lines. However, this is technically costly as it requires adapting many MDE artefacts associated to the modelling language -- especially the meta-models and graphical environments. To alleviate this situation, we propose a method for the automated injection of variability into graphical modelling languages. Given the meta-model and graphical environment of a particular language, our approach permits configuring the allowed model variability, and the graphical environment is automatically adapted to enable creating models with variability. Our solution is implemented atop the Eclipse Modeling Framework and Sirius, and synthesizes adapted graphical editors integrated with FeatureIDEWork funded by the R&D programme of Madrid (S2018/TCS4314), the Spanish Ministry of Science (RTI2018-095255-BI00), and the Austrian Science Fund (P 30525-N31

Consistency-Preserving Evolution Planning on Feature Models

A software product line (SPL) enables large-scale reuse in a family of related software systems through configurable features. SPLs represent a long-term investment so that their ongoing evolution becomes paramount and requires careful planning. While existing approaches enable to create an evolution plan for an SPL on feature-model (FM) level, they assume the plan to be rigid and do not support retroactive changes. In this paper, we present a method that enables to create and retroactively adapt an FM evolution plan while preventing undesired impacts on its structural and logical consistency. This method is founded in structural operational semantics and linear temporal logic. We implement our method using rewriting logic, integrate it within an FM tool suite and perform an evaluation using a collection of existing FM evolution scenarios