Next-Generation Model-based Variability Management: Languages and Tools

International audienceVariability modelling and management is a key activity in a growing number of software engineering contexts, from software product lines to dynamic adaptive systems. Feature models are the defacto standard to formally represent and reason about commonality and variability of a software system. This tutorial aims at presenting next generation of feature modelling languages and tools, directly applicable to a wide range of model-based variability problems and application domains. Participants (being practitioners or academics, beginners or advanced) will learn the principles and foundations of languages and tool-supported techniques dedicated to the model-based management of variability

Separation of Concerns in Feature Modeling: Support and Applications

International audienceFeature models (FMs) are a popular formalism for describing the commonality and variability of software product lines (SPLs) in terms of features. SPL development increasingly involves manipulating many large FMs, and thus scalable modular techniques that support compositional development of complex SPLs are required. In this paper, we describe how a set of complementary operators (aggregate, merge, slice) provides practical support for separation of concerns in feature modeling. We show how the combination of these operators can assist in tedious and error prone tasks such as automated correction of FM anomalies, update and extraction of FM views, reconciliation of FMs and reasoning about properties of FMs. For each task, we report on practical applications in different domains. We also present a technique that can efficiently decompose FMs with thousands of features and report our experimental results

Évaluation de l'apport des aspects, des sujets et des vues pour la composition et la réutilisation des modèles

National audienceThe reuse, the evolution or the fast adaptation of the code of an application are among the strongest concerns of companies. Model engineering tries to bring a solution putting the model in the center of the software development and by capturing the business know-how initially described in the code of the application. This approach has the advantage to make the description independant of software platforms. Our objective in this paper is to present three different approaches for the composition of models, to evaluate them using the criteria proposed by the AOSD-EUROPE network of excellence in order to extract relevant information. From this evaluation, this paper provides new proposals. The evaluation aims at showing the capacity of each approach to support the composition of both functional and extra-functional concerns

Composing Feature Models

International audienceFeature modeling is a widely used technique in Software Product Line development. Feature models allow stakeholders to describe domain concepts in terms of commonalities and differences within a family of software systems. Developing a complex monolithic feature model can require significant effort and restrict the reusability of a set of features already modeled. We advocate using modeling techniques that support separating and composing concerns to better manage the complexity of developing large feature models. In this paper, we propose a set of composition operators dedicated to feature models. These composition operators enable the development of large feature models by composing smaller feature models which address well-defined concerns. The operators are notably distinguished by their documented capabilities to preserve some significant properties

Imaging Services on the Grid as a Product Line: Requirements and Architecture

International audienceSOA is now the reference architecture for medical imaging processing on the grid. Imaging services must be composed in workfows to implement the processing chains, but the need to handle end-to-end qualities of service hampered both the provision of services and their composition. This paper analyses the variability of functional and non functional aspects of this domain and proposes a first architecture in which services are organized within a product line architecture and metamodels help in structuring necessary information

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

Tackling High Variability in Video Surveillance Systems through a Model Transformation Approach

taux acceptation 44%International audienceThis work explores how model-driven engineering techniques can support the configuration of systems in domains presenting multiple variability factors. Video surveillance is a good candidate for which we have an extensive experience. Ultimately, we wish to automatically generate a software component assembly from an application specification, using model to model transformations. The challenge is to cope with variability both at the specification and at the implementation levels. Our approach advocates a clear separation of concerns. More precisely, we propose two feature models, one for task specification and the other for software components. The first model can be transformed into one or several valid component configurations through step-wise specialization. This paper outlines our approach, focusing on the two feature models and their relations. We particularly insist on variability and constraint modeling in order to achieve the mapping from domain variability to software variability through model transformations

Composing your Compositions of Variability Models

International audienceModeling and managing variability is a key activity in a growing number of software engineering contexts. Support for composing variability models is arising in many engineering scenarios, for instance, when several subsystems or modeling artifacts, each coming with their own variability and possibly developed by different stakeholders, should be combined together. In this paper, we consider the problem of composing feature models (FMs), a widely used formalism for representing and reasoning about a set of variability choices. We show that several composition operators can actually be defined, depending on both matching/merging strategies and semantic properties expected in the composed FM. We present four alternative forms and their implementations. We discuss their relative trade-offs w.r.t. reasoning, customizability, traceability, composability and quality of the resulting feature diagram. We summarize these findings in a reading grid which is validated by revisiting some relevant existing works. Our contribution should assist developers in choosing and implementing the right composition operators

Modeling Context and Dynamic Adaptations with Feature Models

International audienceSelf-adaptive and dynamic systems adapt their behavior according to the context of execution. The contextual information exhibits multiple variability factors which induce many possible configurations of the software system at runtime. The challenge is to specify the adaptation rules that can link the dynamic variability of the context with the possible variants of the system. Our work investigates the systematic use of feature models for modeling the context and the software variants, together with their inter relations, as a way to configure the adaptive system with respect to a particular context. A case study in the domain of video surveillance systems is used to illustrate the approach

Tackling High Variability in Video Surveillance Systems through a Model Transformation Approach

taux acceptation 44%International audienceThis work explores how model-driven engineering techniques can support the configuration of systems in domains presenting multiple variability factors. Video surveillance is a good candidate for which we have an extensive experience. Ultimately, we wish to automatically generate a software component assembly from an application specification, using model to model transformations. The challenge is to cope with variability both at the specification and at the implementation levels. Our approach advocates a clear separation of concerns. More precisely, we propose two feature models, one for task specification and the other for software components. The first model can be transformed into one or several valid component configurations through step-wise specialization. This paper outlines our approach, focusing on the two feature models and their relations. We particularly insist on variability and constraint modeling in order to achieve the mapping from domain variability to software variability through model transformations