Quality-Aware Analysis in Product Line Engineering with the Orthogonal Variability Model

Software product line engineering (SPLE) is about producing a set of similar products in a certain domain. A variability model documents the variability amongst products in a product line. The specification of variability can be extended with quality information, such as measurable quality attributes (e.g., CPU and memory consumption) and constraints on these attributes (e.g., memory consumption should be in a range of values). However, the wrong use of constraints may cause anomalies in the specification which must be detected (e.g., the model could represent no products). Furthermore, based on such quality information it is possible to carry out quality-aware analyses, i.e., the product line engineer may want to verify whether it is possible to build a product that satisfies a desired quality. The challenge for quality-aware specification and analysis is three-fold. First, there should be a way to specify quality information in variability models. Second, it should be possible to detect anomalies in the variability specification associated with quality information. Third, there should be mechanisms to verify the variability model to extract useful information, such as the possibility to build a product that fulfils certain quality conditions (e.g., is there any product that requires less than 512MB of memory?). In this article, we present an approach for quality-aware analysis in software product lines using the orthogonal variability model (OVM) to represent variability. We propose to map variability represented in the OVM associated with quality information to a constraint satisfaction problem and to use an off-the-shelf constraint programming solver to automatically perform the verification task. To illustrate our approach, we use a product line in the automotive domain which is an example that was created in a national project by a leading car company. We have developed a prototype tool named FaMa-OVM, which works as a proof of concepts. We were able to identify void models, dead and false optional elements, and check whether the product line example satisfies quality conditions

Automated Analysis of Diverse Variability Models with Tool Support

Over the past twenty years, there have been many contributions in the area of automated analysis of variability models. However, the majority of these researches are focused on feature models. We propose that the knowledge obtained during recent years on the analysis of feature models can be applied to automatically analyse different variability models. In this paper we present FaMa OVM and FaMa DEB, which are prototypical implementations for the automated analysis of two distinct variability models, namely Orthogonal Variability Models and Debian Variablity Models, respectively. In order to minimise efforts and benefit from the feature model know–how, we use FaMa Framework which allows the development of analysis tools for diverse variability modelling languages. This framework provides a well tested system that guides the tool development. Due to the structure provided by the framework, FaMa OVM and FaMa DEB tools are easy to extend and integrate with other tools. We report on the main points of both tools, such as the analysis operations provided and the logical solvers used for the analysis.Comisión Interministerial de Ciencia y Tecnología (CICYT) TIN2012-32273Junta de Andalucía TIC-5906Junta de Andalucía P12-TIC-186

Accessibility Variability Model: The UTPL MOOC Case Study

Several approaches to define Variability Models (VM) of non-functional requirements or quality attributes have been proposed. However, these approaches have focused on specific quality attributes rather than more general non-functional aspects established by standards such as ISO/IEC 25010 for software evaluation and quality. Thus, developing specific software products by selecting features and at the same time measuring the level of compliance with a standard/ guideline is a challenge. In this work, we present the definition of an accessibility VM based on the web content accessibility guides (WCAG) 2.1 W3C recommendation, to obtain a quantitative measure to improve or construct specific SPL products that require to be accessibility-aware. This paper is specially focused on illustrating the experience of measuring the accessibility in a software product line (SPL) in order to check if it is viable measuring products and recommending improvements in terms of features before addressing the construction of accessibility-aware products. The adoption of the VM accessibility has been putted into practice through a pilot case study, the MOOC (Massive Open Online Course) initiative of the Universidad Técnica Particular de Loja. The conduction of this pilot case study has allowed us to illustrate how it is possible to model and measure the accessibility in SPL using accessibility VM, as well as to recommend accessibility configuration improvements for the construction of new or updated MOOC platforms.Ministerio de Economía, Industria y Competitividad TIN2016- 79726-C2-1-RMinisterio de Ciencia, Innovación y Universidades RTI2018-101204-B-C22Agencia Estatal de Investigación TIN2017-90644-RED

Defining and validating a multimodel approach for product architecture derivation and improvement

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-41533-3_24Software architectures are the key to achieving the non-functional requirements (NFRs) in any software project. In software product line (SPL) development, it is crucial to identify whether the NFRs for a specific product can be attained with the built-in architectural variation mechanisms of the product line architecture, or whether additional architectural transformations are required. This paper presents a multimodel approach for quality-driven product architecture derivation and improvement (QuaDAI). A controlled experiment is also presented with the objective of comparing the effectiveness, efficiency, perceived ease of use, intention to use and perceived usefulness with regard to participants using QuaDAI as opposed to the Architecture Tradeoff Analysis Method (ATAM). The results show that QuaDAI is more efficient and perceived as easier to use than ATAM, from the perspective of novice software architecture evaluators. However, the other variables were not found to be statistically significant. Further replications are needed to obtain more conclusive results.This research is supported by the MULTIPLE project (MICINN TIN2009-13838) and the Vali+D fellowship program (ACIF/2011/235).González Huerta, J.; Insfrán Pelozo, CE.; Abrahao Gonzales, SM. (2013). Defining and validating a multimodel approach for product architecture derivation and improvement. En Model-Driven Engineering Languages and Systems. Springer. 388-404. https://doi.org/10.1007/978-3-642-41533-3_24S388404Ali-Babar, M., Lago, P., Van Deursen, A.: Empirical research in software architecture: opportunities, challenges, and approaches. 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Conf. on Automated Software Engineering, New York, USA, pp. 469–472 (2007)Buschmann, F., Meunier, R., Rohnert, H., Sommerlad, P., Stal, M.: Pattern-Oriented software architecture, vol. 1: A System of Patterns. Wiley (1996)Cabello, M.E., Ramos, I., Gómez, A., Limón, R.: Baseline-Oriented Modeling: An MDA Approach Based on Software Product Lines for the Expert Systems Development. In: 1st Asia Conference on Intelligent Information and Database Systems, Vietnam (2009)Carifio, J., Perla, R.J.: Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes. Journal of Social Sciences 3(3), 106–116 (2007)Clements, P., Northrop, L.: Software Product Lines: Practices and Patterns. Addison-Wesley, Boston (2007)Czarnecki, K., Kim, C.H.: Cardinality-based feature modeling and constraints: A progress report. In: Int. Workshop on Software Factories, San Diego-CA (2005)Datorro, J.: Convex Optimization & Euclidean Distance Geometry. Meboo Publishing (2005)Davis, F.D.: Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly 13(3), 319–340 (1989)Douglass, B.P.: Real-Time Design Patterns: Robust Scalable Architecture for Real-Time Systems. Addison-Wesley, Boston (2002)Feiler, P.H., Gluch, D.P., Hudak, J.: The Architecture Analysis & Design Language (AADL): An Introduction. Tech. Report CMU/SEI-2006-TN-011. SEI, Carnegie Mellon University (2006)Gómez, A., Ramos, I.: Cardinality-based feature modeling and model-driven engineering: Fitting them together. In: 4th Int. Workshop on Variability Modeling of Software Intensive Systems, Linz, Austria (2010)Gonzalez-Huerta, J., Insfran, E., Abrahao, S.: A Multimodel for Integrating Quality Assessment in Model-Driven Engineering. In: 8th International Conference on the Quality of Information and Communications Technology (QUATIC 2012), Lisbon, Portugal, September 3-6 (2012)Gonzalez-Huerta, J., Insfran, E., Abrahao, S., McGregor, J.D.: Non-functional Requirements in Model-Driven Software Product Line Engineering. In: 4th Int. Workshop on Non-functional System Properties in Domain Specific Modeling Languages, Insbruck, Austria (2012)Guana, V., Correal, V.: Variability quality evaluation on component-based software product lines. In: 15th Int. Software Product Line Conference, Munich, Germany, vol. 2, pp. 19.1–19.8 (2011)Insfrán, E., Abrahão, S., González-Huerta, J., McGregor, J.D., Ramos, I.: A Multimodeling Approach for Quality-Driven Architecture Derivation. In: 21st Int. Conf. on Information Systems Development (ISD 2012), Prato, Italy (2012)ISO/IEC 25000:2005, Software Engineering. Software product Quality Requirements and Evaluation SQuaRE (2005)Kazman, R., Klein, M., Clements, P.: ATAM: Method for Architecture Evaluation (CMU/SEI-2000-TR-004, ADA382629). Software Engineering Institute, Carnegie Mellon University, Pittsburgh (2000), http://www.sei.cmu.edu/publications/documents/00.reports/00tr004.htmlKim, T., Ko, I., Kang, S., Lee, D.: Extending ATAM to assess product line architecture. In: 8th IEEE Int. Conference on Computer and Information Technology, Sydney, Australia, pp. 790–797 (2008)Kitchenham, B.A., Pfleeger, S.L., Hoaglin, D.C., Rosenber, J.: Preliminary Guidelines for Empirical Research in Software Engineering. IEEE Transactions on Software Engineering 28(8) (2002)Kruchten, P.B.: The Rational Unified Process: An Introduction. Addison-Wesley (1999)Martensson, F.: Software Architecture Quality Evaluation. Approaches in an Industrial Context. Ph. D. thesis, Blekinge Institute of Technology, Karlskrona, Sweden (2006)Maxwell, K.: Applied Statistics for Software Managers. Software Quality Institute Series. Prentice-Hall (2002)Olumofin, F.G., Mišic, V.B.: A holistic architecture assessment method for software product lines. Information and Software Technology 49, 309–323 (2007)Perovich, D., Rossel, P.O., Bastarrica, M.C.: Feature model to product architectures: Applying MDE to Software Product Lines. In: IEEE/IFIP & European Conference on Software Architecture, Helsinki, Findland, pp. 201–210 (2009)Robertson, S., Robertson, J.: Mastering the requirements process. ACM Press, New York (1999)Roos-Frantz, F., Benavides, D., Ruiz-Cortés, A., Heuer, A., Lauenroth, K.: Quality-aware analysis in product line engineering with the orthogonal variability model. Software Quality Journal (2011), doi:10.1007/s11219-011-9156-5Saaty, T.L.: The Analytical Hierarchical Process. 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Interactive Preference-based Approach to Optimal Feature Configuration of Multi-dimensional Service Platforms

A multi-dimensional service delivery platform (MDSP) supports development, deployment and management of services in multiple business domains, serves multiple consumers with different functional and non-functional requirements and integrates services from diverse external collaborators to actualize the platform’s business objective. Consumers of product line services have variant needs that are based on the specific requirements of their business objectives, which demands optimal configuration of the MDSP. Optimal configuration of the MDSP connotes the existence of the most appropriate set of features on the MDSP that best approximates the consumer’s requirements, in the face of multiple conflicting objectives. So far, solutions proposed in the literature have mainly used either a priori or a-posterior methods. In prior methods, the requirements and preference information is provided before the configuration process begins; while a set of possible configurations is first generated and preferred selection is made from the set in a-posterior methods. These methods lack the kind of flexibility afforded by interactive methods in an attempt to generate satisfactory results. The aim of this research is to develop an approach that engenders the derivation of optimal configurations from a multi-dimensional service platform (MDSP), in a manner that is interactive and meets the needs of the consumer

Validating a model-driven software architecture evaluation and improvement method: A family of experiments

Context: Software architectures should be evaluated during the early stages of software development in order to verify whether the non-functional requirements (NFRs) of the product can be fulfilled. This activity is even more crucial in software product line (SPL) development, since it is also necessary to identify whether the NFRs of a particular product can be achieved by exercising the variation mechanisms provided by the product line architecture or whether additional transformations are required. These issues have motivated us to propose QuaDAI, a method for the derivation, evaluation and improvement of software architectures in model-driven SPL development. Objective: We present in this paper the results of a family of four experiments carried out to empirically validate the evaluation and improvement strategy of QuaDAI. Method: The family of experiments was carried out by 92 participants: Computer Science Master s and undergraduate students from Spain and Italy. The goal was to compare the effectiveness, efficiency, perceived ease of use, perceived usefulness and intention to use with regard to participants using the evaluation and improvement strategy of QuaDAI as opposed to the Architecture Tradeoff Analysis Method (ATAM). Results: The main result was that the participants produced their best results when applying QuaDAI, signifying that the participants obtained architectures with better values for the NFRs faster, and that they found the method easier to use, more useful and more likely to be used. The results of the meta-analysis carried out to aggregate the results obtained in the individual experiments also confirmed these results. Conclusions: The results support the hypothesis that QuaDAI would achieve better results than ATAM in the experiments and that QuaDAI can be considered as a promising approach with which to perform architectural evaluations that occur after the product architecture derivation in model-driven SPL development processes when carried out by novice software evaluators.The authors would like to thank all the participants in the experiments for their selfless involvement in this research. This research is supported by the MULTIPLE Project (MICINN TIN2009-13838) and the ValI+D Program (ACIF/2011/235).González Huerta, J.; Insfrán Pelozo, CE.; Abrahao Gonzales, SM.; Scanniello, G. (2015). Validating a model-driven software architecture evaluation and improvement method: A family of experiments. Information and Software Technology. 57:405-429. https://doi.org/10.1016/j.infsof.2014.05.018S4054295

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

Derivación, Evaluación y Mejora de la Calidad de Arquitecturas Software en el Desarrollo de Líneas de Producto Software Dirigido por Modelos

En los últimos años se han propuesto diferentes aproximaciones para el desarrollo de sistemas altamente complejos. Algunos esfuerzos intentan aplicar la aproximación de Líneas de Producto Software tratando de sacar partido de la reutilización masiva para producir sistemas software que comparten un conjunto común de características. Una Línea de Producto Software (LPS) es un conjunto de sistemas software que comparten un conjunto de características comunes que satisfacen las necesidades específicas de un segmento de mercado particular y que son desarrollados a partir de un conjunto de activos software comunes de un modo preestablecido [6]. El desarrollo de una LPS consta de dos procesos básicos: la Ingeniería del Dominio, donde se establece cuáles son las partes comunes y las variables y se construye un conjunto de activos (product¿s line core assets) como partes de los sistemas software a desarrollar, y la Ingeniería de la Aplicación, donde los core assets son reutilizados sistemáticamente para derivar productos específicos. De este modo se reducen costes y tiempo de desarrollo. En el desarrollo de líneas de producto se presentan dos arquitecturas software que juegan dos roles diferenciados; i) la arquitectura de la línea de producto que da soporte a todas los posibles productos que pueden ser obtenidos a partir de la línea de producto y que cuenta con los mecanismos de variabilidad necesarios para cubrir toda la gama de productos y ii) la arquitectura de producto, que es creada a partir de la arquitectura de la línea de producto ejerciendo los mecanismos de variabilidad, para que esta se adapte a los requisitos del producto en desarrollo. En general, el aseguramiento de la calidad del producto es una actividad crucial para el éxito de la industria del software, pero es, si cabe, más importante cuando se trata del desarrollo de líneas de producto software, dado que la reutilización masiva de core assets hace que los atributos de calidad (propiedades físicas o abstractas de un artefacto software) de los core assets impacten en la calidad de todos los productos de una línea de producto. Este hecho es de especial relevancia cuando tratamos con la arquitectura software, que es el core asset mas critico en el desarrollo de líneas de producto. La arquitectura software es la vía para conseguir el cumplimiento de los requisitos no funcionales de nuestro producto, por lo que asegurar que estos requisitos se cumplen durante el proceso de derivación de la arquitectura es una actividad crítica en el proceso de desarrollo. El desarrollo de líneas de producto va, en la mayoría de los casos, ligada a la aplicación del paradigma de desarrollo dirigido por modelos. El Desarrollo de Software Dirigido por Modelos (DSDM) que promueve el uso de modelos durante a lo largo de todo el proceso de desarrollo de software, permitiendo que estos modelos puedan ser transformados sucesivamente hasta la obtención del producto final. En la literatura no se encuentran propuestas que, de forma completa, sistemática y automatizada, permitan obtener arquitecturas de producto software que cumplan una serie de requisitos de calidad. El presente trabajo de investigación pretende la mejora del contexto anterior proponiendo el método QuaDAI (Quality Driven Architecture Derivation and Improvement), un método de derivación, evaluación y mejora de la calidad de arquitecturas software en el Desarrollo de Líneas de Producto Dirigido por Modelos mediante la definición de un artefacto (el multimodelo) y de un proceso dirigido por transformaciones que permite automatizar un proceso (el de derivación, evaluación y mejora) de por si altamente complejo. Este método va dirigido a empresas de desarrollo de software que utilice el paradigma de LPS y que pretendan introducir técnicas automatizadas de aseguramiento de calidad y para investigadores interesados en el campo de las arquitecturas software, líneas de producto y desarrollo dirigido por modelos.González Huerta, J. (2014). Derivación, Evaluación y Mejora de la Calidad de Arquitecturas Software en el Desarrollo de Líneas de Producto Software Dirigido por Modelos [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/36448TESI