Analysis of Feature Models Using Alloy: A Survey

Feature Models (FMs) are a mechanism to model variability among a family of closely related software products, i.e. a software product line (SPL). Analysis of FMs using formal methods can reveal defects in the specification such as inconsistencies that cause the product line to have no valid products. A popular framework used in research for FM analysis is Alloy, a light-weight formal modeling notation equipped with an efficient model finder. Several works in the literature have proposed different strategies to encode and analyze FMs using Alloy. However, there is little discussion on the relative merits of each proposal, making it difficult to select the most suitable encoding for a specific analysis need. In this paper, we describe and compare those strategies according to various criteria such as the expressivity of the FM notation or the efficiency of the analysis. This survey is the first comparative study of research targeted towards using Alloy for FM analysis. This review aims to identify all the best practices on the use of Alloy, as a part of a framework for the automated extraction and analysis of rich FMs from natural language requirement specifications.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857

MODEL DRIVEN SOFTWARE PRODUCT LINE ENGINEERING: SYSTEM VARIABILITY VIEW AND PROCESS IMPLICATIONS

La Ingeniería de Líneas de Productos Software -Software Product Line Engineerings (SPLEs) en inglés- es una técnica de desarrollo de software que busca aplicar los principios de la fabricación industrial para la obtención de aplicaciones informáticas: esto es, una Línea de productos Software -Software Product Line (SPL)- se emplea para producir una familia de productos con características comunes, cuyos miembros, sin embargo, pueden tener características diferenciales. Identificar a priori estas características comunes y diferenciales permite maximizar la reutilización, reduciendo el tiempo y el coste del desarrollo. Describir estas relaciones con la suficiente expresividad se vuelve un aspecto fundamental para conseguir el éxito. La Ingeniería Dirigida por Modelos -Model Driven Engineering (MDE) en inglés- se ha revelado en los últimos años como un paradigma que permite tratar con artefactos software con un alto nivel de abstracción de forma efectiva. Gracias a ello, las SPLs puede aprovecharse en granmedida de los estándares y herramientas que han surgido dentro de la comunidad de MDE. No obstante, aún no se ha conseguido una buena integración entre SPLE y MDE, y como consecuencia, los mecanismos para la gestión de la variabilidad no son suficientemente expresivos. De esta manera, no es posible integrar la variabilidad de forma eficiente en procesos complejos de desarrollo de software donde las diferentes vistas de un sistema, las transformaciones de modelos y la generación de código juegan un papel fundamental. Esta tesis presenta MULTIPLE, un marco de trabajo y una herramienta que persiguen integrar de forma precisa y eficiente los mecanismos de gestión de variabilidad propios de las SPLs dentro de los procesos de MDE. MULTIPLE proporciona lenguajes específicos de dominio para especificar diferentes vistas de los sistemas software. Entre ellas se hace especial hincapié en la vista de variabilidad ya que es determinante para la especificación de SPLs.Gómez Llana, A. (2012). MODEL DRIVEN SOFTWARE PRODUCT LINE ENGINEERING: SYSTEM VARIABILITY VIEW AND PROCESS IMPLICATIONS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15075Palanci

Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Configuration Analysis for Large Scale Feature Models: Towards Speculative-Based Solutions

Los sistemas de alta variabilidad son sistemas de software en los que la gestión de la variabilidad es una actividad central. Algunos ejemplos actuales de sistemas de alta variabilidad son el sistema web de gesión de contenidos Drupal, el núcleo de Linux, y las distribuciones Debian de Linux. La configuración en sistemas de alta variabilidad es la selección de opciones de configuración según sus restricciones de configuración y los requerimientos de usuario. Los modelos de características son un estándar “de facto” para modelar las funcionalidades comunes y variables de sistemas de alta variabilidad. No obstante, el elevado número de componentes y configuraciones que un modelo de características puede contener hacen que el análisis manual de estos modelos sea una tarea muy costosa y propensa a errores. Así nace el análisis automatizado de modelos de características con mecanismos y herramientas asistidas por computadora para extraer información de estos modelos. Las soluciones tradicionales de análisis automatizado de modelos de características siguen un enfoque de computación secuencial para utilizar una unidad central de procesamiento y memoria. Estas soluciones son adecuadas para trabajar con sistemas de baja escala. Sin embargo, dichas soluciones demandan altos costos de computación para trabajar con sistemas de gran escala y alta variabilidad. Aunque existan recusos informáticos para mejorar el rendimiento de soluciones de computación, todas las soluciones con un enfoque de computación secuencial necesitan ser adaptadas para el uso eficiente de estos recursos y optimizar su rendimiento computacional. Ejemplos de estos recursos son la tecnología de múltiples núcleos para computación paralela y la tecnología de red para computación distribuida. Esta tesis explora la adaptación y escalabilidad de soluciones para el analisis automatizado de modelos de características de gran escala. En primer lugar, nosotros presentamos el uso de programación especulativa para la paralelización de soluciones. Además, nosotros apreciamos un problema de configuración desde otra perspectiva, para su solución mediante la adaptación y aplicación de una solución no tradicional. Más tarde, nosotros validamos la escalabilidad y mejoras de rendimiento computacional de estas soluciones para el análisis automatizado de modelos de características de gran escala. Concretamente, las principales contribuciones de esta tesis son: • Programación especulativa para la detección de un conflicto mínimo y 1 2 preferente. Los algoritmos de detección de conflictos mínimos determinan el conjunto mínimo de restricciones en conflicto que son responsables de comportamiento defectuoso en el modelo en análisis. Nosotros proponemos una solución para, mediante programación especulativa, ejecutar en paralelo y reducir el tiempo de ejecución de operaciones de alto costo computacional que determinan el flujo de acción en la detección de conflicto mínimo y preferente en modelos de características de gran escala. • Programación especulativa para un diagnóstico mínimo y preferente. Los algoritmos de diagnóstico mínimo determinan un conjunto mínimo de restricciones que, por una adecuada adaptación de su estado, permiten conseguir un modelo consistente o libre de conflictos. Este trabajo presenta una solución para el diagnóstico mínimo y preferente en modelos de características de gran escala mediante la ejecución especulativa y paralela de operaciones de alto costo computacional que determinan el flujo de acción, y entonces disminuir el tiempo de ejecución de la solución. • Completar de forma mínima y preferente una configuración de modelo por diagnóstico. Las soluciones para completar una configuración parcial determinan un conjunto no necesariamente mínimo ni preferente de opciones para obtener una completa configuración. Esta tesis soluciona el completar de forma mínima y preferente una configuración de modelo mediante técnicas previamente usadas en contexto de diagnóstico de modelos de características. Esta tesis evalua que todas nuestras soluciones preservan los valores de salida esperados, y también presentan mejoras de rendimiento en el análisis automatizado de modelos de características con modelos de gran escala en las operaciones descrita

Configurable Feature Models

Feature models represent all the products that can be built under a variability-intensive system such as a software product line, but they are not fully configurable. There exist no explicit effort in defining configuration models that enable making decisions on attributes and cardinalities in feature models that use these artefacts. In this paper we present configurable feature models as an evolution from feature models that integrate configuration models within, improving the configurability of variability-intensive systems

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance

Improving Variabilty Analysis through Scenario-Based Incompatibility Detection

Software Product Line (SPL) developments include Variability Management (VA) as a core activity aiming at minimizing the inherent complexity in commonality and variability manipulation. Particularly, the (automated) analysis of variability models refers to the activities, methods and techniques involved in the definition, design, and instantiation of variabilities modeled during SPL development. Steps of this analysis are defined as a variability analysis process (VA process), which is focused on assisting variability model designers in avoiding anomalies and/or inconsistencies, and minimizing problems when products are implemented and derived. Previously, we have proposed an approach for analyzing variability models through a well-defined VA process (named SeVaTax). This process includes a comprehensive set of scenarios, which allows a designer to detect (and even correct in some cases) different incompatibilities. In this work, we extend SeVaTax by classifying the scenarios according to their dependencies, and by assessing the use of these scenarios. This assessment introduces two experiments to evaluate accuracy and coverage. The former addresses responses when variability models are analyzed, and the latter the completeness of our process with respect to other proposals. Findings show that a more extensive set of scenarios might improve the possibilities of current practices in variability analysis.Fil: Buccella, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; ArgentinaFil: Pol'la, Matias Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; ArgentinaFil: Cechich, Susana Alejandra. Universidad Nacional del Comahue. Facultad de Informatica; Argentin

Modeling Business Process Variability

This master thesis presents research findings on business process variability modeling. Its main goal is to analyze inherent problems of business process variability and solve them simply, innovatively and effectively. To achieve this goal, process variability is defined by analyzing scientific literature, its main problems identified and is illustrated using a healthcare running example: process variability is classified into process variability within the domain space and over time. These two forms of process variability respectively lead to process variability modeling and process model evolution problems. After defining the main problems inherent to process variability, the focus of this research project is defined: solving process variability modeling problems. First current business process modeling languages are evaluated to assess the effectiveness of their respective modeling concepts when modeling process variability, using a newly created set of evaluation criteria and the healthcare running example. The following business process modeling languages are evaluated: Event driven process chains (EPC), the Business Process Modeling Notation (BPMN) and Configurable EPC (C-EPC). Business process variability modeling and Software product line engineering have similar problems. Therefore the variability modeling concepts developed by software product line engineering are analyzed. Feature diagrams and software configuration management are the main variability management concepts provided by software product line engineering. To apply these variability management concepts to model process variability meant combining them with existing business modeling languages. Riebisch feature diagrams are combined with C-EPC to form Feature-EPC. Applying software configuration management, meant merging Change Oriented Versioning with basic EPC to create COV-EPC, and merging the Proteus Configuration Language with basic EPC to design PCL-EPC. Finally these newly created business process modeling languages are also evaluated using the newly designed evaluation criteria and the healthcare running example. EPC or BPMN are not suited to model business process variability within the domain space. C-EPC provide explicit means to model business process variability, however the process models tend to get big very fast. Furthermore the syntax, the contextual constraints and the semantics of the configuration requirements and guidelines used to configure the C-EPC process models are unclear. Feature-EPC improve C-EPC with domain modeling capability and clearly defined configuration rules: their syntax, contextual constraints and semantics have been clearly defined using a context free grammar in Backus-Naur form. Furthermore, consistent combinations of features and configuration rules are ensured using respectively constraints and a conflict resolution algorithm. However, Feature-EPC and C-EPC suffer from the same weakness: large configurable process models. In COV-EPC and PCL-EPC the problem of large configurable process models is solved. COV-EPC ensures consistent combinations of options and configuration rules using respectively validities and a conflict resolution algorithm. PCL-EPC guarantees consistent combinations of process fragments by means of a PCL specification