Over the last years, Web services have become increasingly popular. It is because they allow businesses to share data and business process (BP) logic through a programmatic interface across networks. In order to reach the full potential of Web services, they can be combined to achieve specifi c functionalities. Web services run in complex contexts where arising events may compromise the quality of the system (e.g. a sudden security attack). As a result, it is desirable to count on mechanisms to adapt Web service compositions (or simply called service compositions) according to problematic events in the context. Since critical systems may require prompt responses, manual adaptations are unfeasible in large and intricate service compositions. Thus, it is suitable to have autonomic mechanisms to guide their self-adaptation. One way to achieve this is by implementing variability constructs at the language level. However, this approach may become tedious, difficult to manage, and error-prone as the number of con figurations for the service composition grows. The goal of this thesis is to provide a model-driven framework to guide autonomic adjustments of context-aware service compositions. This framework spans over design time and runtime to face arising known and unknown context events (i.e., foreseen and unforeseen at design time) in the close and open worlds respectively. At design time, we propose a methodology for creating the models that guide autonomic changes. Since Service-Oriented Architecture (SOA) lacks support for systematic reuse of service operations, we represent service operations as Software Product Line (SPL) features in a variability model. As a result, our approach can support the construction of service composition families in mass production-environments. In order to reach optimum adaptations, the variability model and its possible con figurations are verifi ed at design time using Constraint Programming (CP). At runtime, when problematic events arise in the context, the variability model is leveraged for guiding autonomic changes of the service composition. The activation and deactivation of features in the variability model result in changes in a composition model that abstracts the underlying service composition. Changes in the variability model are refl ected into the service composition by adding or removing fragments of Business Process Execution Language (WS-BPEL) code, which are deployed at runtime. Model-driven strategies guide the safe migration of running service composition instances. Under the closed-world assumption, the possible context events are fully known at design time. These events will eventually trigger the dynamic adaptation of the service composition. Nevertheless, it is diffi cult to foresee all the possible situations arising in uncertain contexts where service compositions run. Therefore, we extend our framework to cover the dynamic evolution of service compositions to deal with unexpected events in the open world. If model adaptations cannot solve uncertainty, the supporting models self-evolve according to abstract tactics that preserve expected requirements. / Alférez Salinas, GH. (2013). Achieving Autonomic Web Service Compositions with Models at Runtime [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34672

A recent trend in software industry is to provide enterprise applications in the cloud that are accessible everywhere and on any device. As the market is highly competitive, customer orientation plays an important role. Companies therefore start providing applications as a service, which are directly configurable by customers in an online self-service portal. However, customer configurations are usually deployed in separated application instances. Thus, each instance is provisioned manually and must be maintained separately. Due to the induced redundancy in software and hardware components, resources are not optimally utilized. A multi-tenant aware application architecture eliminates redundancy, as a single application instance serves multiple customers renting the application. The combination of a configuration self-service portal with a multi-tenant aware application architecture allows serving customers just-in-time by automating the deployment process. Furthermore, self-service portals improve application scalability in terms of functionality, as customers can adapt application configurations on themselves according to their changing demands. However, the configurability of current multi-tenant aware applications is rather limited. Solutions implementing variability are mainly developed for a single business case and cannot be directly transferred to other application scenarios. The goal of this thesis is to provide a generic framework for handling application variability, automating configuration and reconfiguration processes essential for self-service portals, while exploiting the advantages of multi-tenancy. A promising solution to achieve this goal is the application of software product line methods. In software product line research, feature models are in wide use to express variability of software intense systems on an abstract level, as features are a common notion in software engineering and prominent in matching customer requirements against product functionality. This thesis introduces a framework for feature-based configuration management of reconfigurable cloud applications. The contribution is three-fold. First, a development strategy for flexible multi-tenant aware applications is proposed, capable of integrating customer configurations at application runtime. Second, a generic method for defining concern-specific configuration perspectives is contributed. Perspectives can be tailored for certain application scopes and facilitate the handling of numerous configuration options. Third, a novel method is proposed to model and automate structured configuration processes that adapt to varying stakeholders and reduce configuration redundancies. Therefore, configuration processes are modeled as workflows and adapted by applying rewrite rules triggered by stakeholder events. The applicability of the proposed concepts is evaluated in different case studies in the industrial and academic context. Summarizing, the introduced framework for feature-based configuration management is a foundation for automating configuration and reconfiguration processes of multi-tenant aware cloud applications, while enabling application scalability in terms of functionality.

Une Ligne de Produits Logiciels (LPL) supporte la gestion d’une famille de logiciels. Cette approche se caractérise par une réutilisation systématique des artefacts communs qui réduit le coût et le temps de mise sur le marché et augmente la qualité des logiciels. Cependant, une LPL exige un investissement initial coûteux. Certaines organisations qui ne peuvent pas faire face à un tel investissement, utilisent le « Clone-and-own » C&O pour construire et faire évoluer des familles de logiciels. Cependant, l'efficacité de cette pratique se dégrade proportionnellement à la croissance de la famille de produits, qui devient difficile à maintenir. Dans cette thèse, nous proposons une approche hybride qui utilise à la fois une LPL et l'approche C&O pour faire évoluer une famille de produits logiciels. Un mécanisme automatique d’identification des correspondances entre les « features » caractérisant les produits et les artéfacts logiciels, permet la migration des variantes de produits développées en C&O dans une LPL. L’originalité de ce travail est alors d’aider à la dérivation de nouveaux produits en proposant différents scenarii d’opérations C&O à effectuer pour dériver un nouveau produit à partir des features requis. Le développeur peut alors réduire ces possibilités en exprimant ses préférences (e.g. produits, artefacts) et en utilisant les estimations de coûts sur les opérations que nous proposons. Les nouveaux produits ainsi construits sont alors facilement intégrés dans la LPL. Nous avons étayé cette thèse en développant le framework SUCCEED (SUpporting Clone-and-own with Cost-EstimatEd Derivation) et l’avons appliqué à une étude de cas sur des familles de portails web. / A Software Product Line (SPL) manages commonalities and variability of a related software products family. This approach is characterized by a systematic reuse that reduces development cost and time to market and increases software quality. However, building an SPL requires an initial expensive investment. Therefore, organizations that are not able to deal with such an up-front investment, tend to develop a family of software products using simple and intuitive practices. Clone-and-own (C&O) is an approach adopted widely by software developers to construct new product variants from existing ones. However, the efficiency of this practice degrades proportionally to the growth of the family of products in concern, that becomes difficult to manage. In this dissertation, we propose a hybrid approach that utilizes both SPL and C&O to develop and evolve a family of software products. An automatic mechanism of identification of the correspondences between the features of the products and the software artifacts, allows the migration of the product variants developed in C&O in an SPL The originality of this work is then to help the derivation of new products by proposing different scenarios of C&O operations to be performed to derive a new product from the required features. The developer can then reduce these possibilities by expressing her preferences (e.g. products, artifacts) and using the proposed cost estimations on the operations. We realized our approach by developing SUCCEED, a framework for SUpporting Clone-and-own with Cost-EstimatEd Derivation. We validate our works on a case study of families of web portals.

A recent trend in software industry is to provide enterprise applications in the cloud that are accessible everywhere and on any device. As the market is highly competitive, customer orientation plays an important role. Companies therefore start providing applications as a service, which are directly configurable by customers in an online self-service portal. However, customer configurations are usually deployed in separated application instances. Thus, each instance is provisioned manually and must be maintained separately. Due to the induced redundancy in software and hardware components, resources are not optimally utilized. A multi-tenant aware application architecture eliminates redundancy, as a single application instance serves multiple customers renting the application. The combination of a configuration self-service portal with a multi-tenant aware application architecture allows serving customers just-in-time by automating the deployment process. Furthermore, self-service portals improve application scalability in terms of functionality, as customers can adapt application configurations on themselves according to their changing demands. However, the configurability of current multi-tenant aware applications is rather limited. Solutions implementing variability are mainly developed for a single business case and cannot be directly transferred to other application scenarios. The goal of this thesis is to provide a generic framework for handling application variability, automating configuration and reconfiguration processes essential for self-service portals, while exploiting the advantages of multi-tenancy. A promising solution to achieve this goal is the application of software product line methods. In software product line research, feature models are in wide use to express variability of software intense systems on an abstract level, as features are a common notion in software engineering and prominent in matching customer requirements against product functionality. This thesis introduces a framework for feature-based configuration management of reconfigurable cloud applications. The contribution is three-fold. First, a development strategy for flexible multi-tenant aware applications is proposed, capable of integrating customer configurations at application runtime. Second, a generic method for defining concern-specific configuration perspectives is contributed. Perspectives can be tailored for certain application scopes and facilitate the handling of numerous configuration options. Third, a novel method is proposed to model and automate structured configuration processes that adapt to varying stakeholders and reduce configuration redundancies. Therefore, configuration processes are modeled as workflows and adapted by applying rewrite rules triggered by stakeholder events. The applicability of the proposed concepts is evaluated in different case studies in the industrial and academic context. Summarizing, the introduced framework for feature-based configuration management is a foundation for automating configuration and reconfiguration processes of multi-tenant aware cloud applications, while enabling application scalability in terms of functionality.