Model Based Development of Quality-Aware Software Services

Modelling languages and development frameworks give support for functional and structural description of software architectures. But quality-aware applications require languages which allow expressing QoS as a first-class concept during architecture design and service composition, and to extend existing tools and infrastructures adding support for modelling, evaluating, managing and monitoring QoS aspects. In addition to its functional behaviour and internal structure, the developer of each service must consider the fulfilment of its quality requirements. If the service is flexible, the output quality depends both on input quality and available resources (e.g., amounts of CPU execution time and memory). From the software engineering point of view, modelling of quality-aware requirements and architectures require modelling support for the description of quality concepts, support for the analysis of quality properties (e.g. model checking and consistencies of quality constraints, assembly of quality), tool support for the transition from quality requirements to quality-aware architectures, and from quality-aware architecture to service run-time infrastructures. Quality management in run-time service infrastructures must give support for handling quality concepts dynamically. QoS-aware modeling frameworks and QoS-aware runtime management infrastructures require a common evolution to get their integration

Quality-aware model-driven service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box character of services

Can Component/Service-Based Systems Be Proved Correct?

Component-oriented and service-oriented approaches have gained a strong enthusiasm in industries and academia with a particular interest for service-oriented approaches. A component is a software entity with given functionalities, made available by a provider, and used to build other application within which it is integrated. The service concept and its use in web-based application development have a huge impact on reuse practices. Accordingly a considerable part of software architectures is influenced; these architectures are moving towards service-oriented architectures. Therefore applications (re)use services that are available elsewhere and many applications interact, without knowing each other, using services available via service servers and their published interfaces and functionalities. Industries propose, through various consortium, languages, technologies and standards. More academic works are also undertaken concerning semantics and formalisation of components and service-based systems. We consider here both streams of works in order to raise research concerns that will help in building quality software. Are there new challenging problems with respect to service-based software construction? Besides, what are the links and the advances compared to distributed systems?Comment: 16 page

Model-driven performance evaluation for service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Software quality aspects such as performance are of central importance for the integration of heterogeneous, distributed service-based systems. Empirical performance evaluation is a process of measuring and calculating performance metrics of the implemented software. We present an approach for the empirical, model-based performance evaluation of services and service compositions in the context of model-driven service engineering. Temporal databases theory is utilised for the empirical performance evaluation of model-driven developed service systems

Semantic model-driven development of service-centric software architectures

Service-oriented architecture (SOA) is a recent architectural paradigm that has received much attention. The prevalent focus on platforms such as Web services, however, needs to be complemented by appropriate software engineering methods. We propose the model-driven development of service-centric software systems. We present in particular an investigation into the role of enriched semantic modelling for a modeldriven development framework for service-centric software systems. Ontologies as the foundations of semantic modelling and its enhancement through architectural pattern modelling are at the core of the proposed approach. We introduce foundations and discuss the benefits and also the challenges in this context

Resource Oriented Modelling: Describing Restful Web Services Using Collaboration Diagrams

The popularity of Resource Oriented and RESTful Web Services is increasing rapidly. In these, resources are key actors in the interfaces, in contrast to other approaches where services, messages or objects are. This distinctive feature necessitates a new approach for modelling RESTful interfaces providing a more intuitive mapping from model to implementation than could be achieved with non-resource methods. With this objective we propose an approach to describe Resource Oriented and RESTful Web Services based on UML collaboration diagrams. Then use it to model scenarios from several problem domains, arguing that Resource Oriented and RESTful Web Services can be used in systems which go beyond ad-hoc integration. Using the scenarios we demonstrate how the approach is useful for: eliciting domain ontologies; identifying recurring patterns; and capturing static and dynamic aspects of the interface