Probabilistic Model Checking for Energy Analysis in Software Product Lines

In a software product line (SPL), a collection of software products is defined by their commonalities in terms of features rather than explicitly specifying all products one-by-one. Several verification techniques were adapted to establish temporal properties of SPLs. Symbolic and family-based model checking have been proven to be successful for tackling the combinatorial blow-up arising when reasoning about several feature combinations. However, most formal verification approaches for SPLs presented in the literature focus on the static SPLs, where the features of a product are fixed and cannot be changed during runtime. This is in contrast to dynamic SPLs, allowing to adapt feature combinations of a product dynamically after deployment. The main contribution of the paper is a compositional modeling framework for dynamic SPLs, which supports probabilistic and nondeterministic choices and allows for quantitative analysis. We specify the feature changes during runtime within an automata-based coordination component, enabling to reason over strategies how to trigger dynamic feature changes for optimizing various quantitative objectives, e.g., energy or monetary costs and reliability. For our framework there is a natural and conceptually simple translation into the input language of the prominent probabilistic model checker PRISM. This facilitates the application of PRISM's powerful symbolic engine to the operational behavior of dynamic SPLs and their family-based analysis against various quantitative queries. We demonstrate feasibility of our approach by a case study issuing an energy-aware bonding network device.Comment: 14 pages, 11 figure

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

WeaFQAs: A Software Product Line Approach for Customizing and Weaving Efficient Functional Quality Attributes

Fecha de Lectura de Tesis: 10 de julio de 2018Los atributos de calidad funcionales (FQA) son aquellos que tienen una clara implicación en la funcionalidad del sistema, es decir, existen unos componentes específicos que deben ser incorporados a la arquitectura software del sistema para satisfacer sus requisitos de atributos de calidad. Ejemplos de FQAs son seguridad, usabilidad, o persistencia. Modelar estos atributos es una tarea compleja. Por un lado, se componen de muchas características relacionadas, por ejemplo seguridad está compuesto, entre otros, por autenticación, confidencialidad y encriptación. Tienen dependencias entre ellos, por ejemplo, seguridad puede ser requerido por usabilidad o persistencia. Por otro lado, tienen muchos puntos de variabilidad: una aplicación concreta puede requerir autenticación y control de acceso mientras que otra puede necesitar sólo encriptación. Además, su funcionalidad suele estar dispersa afectando a varios componentes del sistema en desarrollo. El objetivo de esta tesis es definir una línea de productos software orientada a aspectos que permita: (1) modelar las similitudes y la variabilidad de los FQAs desde las primeras etapas del proceso de desarrollo, (2) gestionar las dependencias existentes entre los FQAs, (3) independizar el modelado de los FQAs de la arquitectura de la aplicación afectada, (4) configurar los FQAs en base a los requisitos de cada aplicación teniendo además en cuenta propiedades no funcionales como el rendimiento y el consumo energético de cada solución, (5) incorporar las configuraciones a la arquitectura de la aplicación de manera automática; y (6) gestionar la evolución de los FQAs cuando los requisitos cambien en el futuro. Como resultado se ha definido WeaFQAs, un proceso software para gestionar los FQAs que cubre todos los puntos mencionados. Se han realizado y comparado dos instanciaciones de WeaFQAs usando diferentes lenguajes de variabilidad y de modelado, además de proporcionar soporte con una herramienta basada en el lenguaje CVL

Composing Multiple Variability Artifacts to Assemble Coherent Workflows

International audienceThe development of scientific workflows is evolving towards the system- atic use of service oriented architectures, enabling the composition of dedicated and highly parameterized software services into processing pipelines. Building consistent workflows then becomes a cumbersome and error-prone activity as users cannot man- age such large scale variability. This paper presents a rigorous and tooled approach in which techniques from Software Product Line (SPL) engineering are reused and ex- tended to manage variability in service and workflow descriptions. Composition can be facilitated while ensuring consistency. Services are organized in a rich catalog which is organized as a SPL and structured according to the common and variable concerns captured for all services. By relying on sound merging techniques on the feature mod- els that make up the catalog, reasoning about the compatibility between connected services is made possible. Moreover, an entire workflow is then seen as a multiple SPL (i.e., a composition of several SPLs). When services are configured within, the prop- agation of variability choices is then automated with appropriate techniques and the user is assisted in obtaining a consistent workflow. The approach proposed is com- pletely supported by a combination of dedicated tools and languages. Illustrations and experimental validations are provided using medical imaging pipelines, which are rep- resentative of current scientific workflows in many domains

Domain Specific Languages for Managing Feature Models: Advances and Challenges

International audienceManaging multiple and complex feature models is a tedious and error-prone activity in software product line engineering. Despite many advances in formal methods and analysis techniques, the supporting tools and APIs are not easily usable together, nor unified. In this paper, we report on the development and evolution of the Familiar Domain-Specific Language (DSL). Its toolset is dedicated to the large scale management of feature models through a good support for separating concerns, composing feature models and scripting manipulations. We overview various applications of Familiar and discuss both advantages and identified drawbacks. We then devise salient challenges to improve such DSL support in the near future

Empirical Assessment of Generating Adversarial Configurations for Software Product Lines

International audienceSoftware product line (SPL) engineering allows the derivation of products tailored to stakeholders' needs through the setting of a large number of configuration options. Unfortunately, options and their interactions create a huge configuration space which is either intractable or too costly to explore exhaustively. Instead of covering all products, machine learning (ML) approximates the set of acceptable products (e.g., successful builds, passing tests) out of a training set (a sample of configurations). However, ML techniques can make prediction errors yielding non-acceptable products wasting time, energy and other resources. We apply adversarial machine learning techniques to the world of SPLs and craft new configurations faking to be acceptable configurations but that are not and vice-versa. It allows to diagnose prediction errors and take appropriate actions. We develop two adversarial configuration generators on top of state-of-the-art attack algorithms and capable of synthesizing configurations that are both adversarial and conform to logical constraints. We empirically assess our generators within two case studies: an industrial video synthesizer (MOTIV) and an industry-strength, open-source Web-appconfigurator (JHipster). For the two cases, our attacks yield (up to) a 100% misclassification rate without sacrificing the logical validity of adversarial configurations. This work lays the foundations of a quality assurance framework for ML-based SPLs

SPLEMMA: A Generic Framework for Controlled-Evolution of Software Product Lines

International audienceManaging in a generic way the evolution process of feature- oriented Software Product Lines (SPLs) is complex due to the number of elements that are impacted and the heterogeneity of the SPLs regarding artifacts used to define them. Existing work presents specific approaches to manage the evolution of SPLs in terms of such artifacts, i.e., assets, feature models and relation definitions. Moreover stakeholders do not necessarily master all the knowledge of the SPL making its evolution difficult and error-prone without a proper tool support. In order to deal with these issues, we introduce SPLEmma, a generic framework that follows a Model Driven Engineering approach to capture the evolution of a SPL independently of the kind of assets, technologies or feature models used for the product derivation. Authorized changes are described by the SPL maintainer and captured in a model used to generate tools that guide the evolution process and preserve the consistency of the whole SPL. We report on the application of our approach on two SPLs: YourCast for digital signage systems, and SALOON, which enables generation of configurations for cloud providers