A first prototype of a new repository for feature model exchange and knowledge sharing

Feature models are the “de facto” standard for variability modelling and are used in both academia and industry. The MODEVAR initia tive tries to establish a common textual feature modelling language that can be used by different communities and can allow informa tion sharing. Feature model related researches use different models for different purposes such as analysis, sampling, testing, debug ging, teaching, etc. Those models are shared in private repositories and there is a risk that all that knowledge is spread across different platforms which hinder collaboration and knowledge reuse. In this paper, we propose a first working version of a new feature model repository that allows to centralise the knowledge generated in the community together with advanced capabilities such as DOI generation, an API, analysis reports, among others. Our solution is a front end interface that uses the popular open science repos itory Zenodo as an end point to materialise the storage of all the information. Zenodo is enhanced with characteristics that facilitate the management of the models. The idea of our repository is to provide existing but also new features that are not present in other repositories (e.g., SPLOT). We propose to populate our repository with all the existing models of many sources including SPLOT.Ministerio de Ciencia, Innovación y Universidades RTI2018-101204-B-C22 (OPHELIA)Agencia Estatal de Investigación TIN2017-90644-RED

Towards a New Repository for Feature Model Exchange

Feature models are one of the most important contributions to the field of software product lines, feature oriented software develop ment or variability intensive systems. Since their invention in 1990, many feature model dialects appeared from less formal to more formal, from visual to textual, integrated in tool chains or just as a support for a concrete research contribution. Ten year ago, S.P.L.O.T. a feature model online tool was presented. One of its most used features has been the ability to centralise a feature model repository with its own feature model dialect. As a result of MODEVAR, we hope to have a new simple textual feature model language that can be shared by the community. Having a new repository for that language can help to share knowledge. In this paper we present some ideas about the characteristics that the future feature model repository should have in the future. The idea is to discuss those characteristics with the communityMinisterio de Economía y Competitividad RTI2018-101204-B-C22 (OPHELIA)Ministerio de Ciencia, Innovación y Universidades MCIU-AEI TIN2017-90644-REDT (TASOVA

Un analizador de modelos de variabilidad basado en el árbol de características.

Un árbol de características generalizado (GFT) es un modelo de variabilidad en el que las restricciones textuales han sido eliminadas manteniendo la semántica del modelo. La ventaja de un GFT es que se puede analizar directamente razonando sobre las relaciones jerárquicas del árbol de características, sin tener que transformar el modelo a SAT o construir un árbol de decisión binario (BDD). Las desventajas de un GFT son que puede contener características duplicadas y que su tamaño en número de características con respecto al modelo de variabilidad original es considerablemente mayor, lo que complica el análisis automático. En este artículo se propone un analizador de modelos GFT basado en las relaciones jerárquicas del árbol de características teniendo en cuenta la existencia de características duplicadas. Se definen un conjunto de operaciones de análisis sobre GFT y se compara su eficiencia con solvers SAT y BDD. El solver GFT mejora la eficiencia del análisis sobre solvers BDD para modelos de hasta diez mil características.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A modular metamodel and refactoring rules to achieve software product line interoperability.

Emergent application domains, such as cyber–physical systems, edge computing or industry 4.0. present a high variability in software and hardware infrastructures. However, no single variability modeling language supports all language extensions required by these application domains (i.e., attributes, group cardinalities, clonables, complex constraints). This limitation is an open challenge that should be tackled by the software engineering field, and specifically by the software product line (SPL) community. A possible solution could be to define a completely new language, but this has a high cost in terms of adoption time and development of new tools. A more viable alternative is the definition of refactoring and specialization rules that allow interoperability between existing variability languages. However, with this approach, these rules cannot be reused across languages because each language uses a different set of modeling concepts and a different concrete syntax. Our approach relies on a modular and extensible metamodel that defines a common abstract syntax for existing variability modeling extensions. We map existing feature modeling languages in the SPL community to our common abstract syntax. Using our abstract syntax, we define refactoring rules at the language construct level that help to achieve interoperability between variability modeling languages.Work supported by the projects MEDEA RTI2018-099213-B-I00, IRIS PID2021-122812OB-I00 (co-financed by FEDER funds), Rhea P18-FR-1081 (MCI/AEI/FEDER, UE), LEIA UMA18-FEDERIA-157, and DAEMON H2020-101017109. // Funding for open access: Universidad de Málaga / CBUA

The state of adoption and the challenges of systematic variability management in industry

Handling large-scale software variability is still a challenge for many organizations. After decades of research on variability management concepts, many industrial organizations have introduced techniques known from research, but still lament that pure textbook approaches are not applicable or efficient. For instance, software product line engineering—an approach to systematically develop portfolios of products—is difficult to adopt given the high upfront investments; and even when adopted, organizations are challenged by evolving their complex product lines. Consequently, the research community now mainly focuses on re-engineering and evolution techniques for product lines; yet, understanding the current state of adoption and the industrial challenges for organizations is necessary to conceive effective techniques. In this multiple-case study, we analyze the current adoption of variability management techniques in twelve medium- to large-scale industrial cases in domains such as automotive, aerospace or railway systems. We identify the current state of variability management, emphasizing the techniques and concepts they adopted. We elicit the needs and challenges expressed for these cases, triangulated with results from a literature review. We believe our results help to understand the current state of adoption and shed light on gaps to address in industrial practice.This work is supported by Vinnova Sweden, Fond Unique Interminist´eriel (FUI) France, and the Swedish Research Council. Open access funding provided by University of Gothenbur