A Framework for Evaluating Model-Driven Self-adaptive Software Systems

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition, self-adaptive application, context oriented software developmen

Domain-Specific Modeling and Code Generation for Cross-Platform Multi-Device Mobile Apps

Nowadays, mobile devices constitute the most common computing device. This new computing model has brought intense competition among hardware and software providers who are continuously introducing increasingly powerful mobile devices and innovative OSs into the market. In consequence, cross-platform and multi-device development has become a priority for software companies that want to reach the widest possible audience. However, developing an application for several platforms implies high costs and technical complexity. Currently, there are several frameworks that allow cross-platform application development. However, these approaches still require manual programming. My research proposes to face the challenge of the mobile revolution by exploiting abstraction, modeling and code generation, in the spirit of the modern paradigm of Model Driven Engineering

Model-driven development for pervasive information systems

This chapter focus on design methodologies for pervasive information systems (PIS). It aims to contribute for the efficiency and effectiveness on software development of ubiquitous services/applications supported on pervasive information systems. Pervasive information systems are composed of conveniently orchestrated embedded or mobile computing devices that offer innovative ways to support existing and new business models. Those systems are characterized as having a potential large number of interactive heterogeneous embedded/mobile computing devices that collect, process, and communicate information. Additionally, they are target of high rates of technological innovations. Therefore, changes on requirements or in technology demands for frequent modifications on software at device and system levels. Software design and evolution for those requires suitable approaches that cope with such demands and characteristics of pervasive information systems. Model-driven development approaches (which essentially centre the focus of development on models, and involves concepts such as Platform-Independent Models, Platform-Specific Models, model transformations, and use of established standards) currently in research at academic and industrial arenas to design of large systems, offer potential benefits that can be applied to design and evolution of these pervasive information systems. In this chapter, we raise issues and propose strategies related to the software development of PIS using a model-driven development perspective

Model-driven methodologies for pervasive information systems development

This paper intends to introduce the concept of pervasive information systems (PIS) and the issues that arise from the software development for pervasive information systems. The model driven approach is generally described and its benefits to the software design are identified. Finally, some future directions for the usage of model driven methodologies within the development of PIS are highlighted, presenting some specific problems that nowadays that kind of methodologies have not yet been able to overcome

A Framework for Evaluating Model-Driven Self-adaptive Software Systems

In the last few years, Model Driven Development (MDD), Component-based Software Development (CBSD), and context-oriented software have become interesting alternatives for the design and construction of self-adaptive software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the modularity, flexibility, adaptability, and reliability of software systems. An analysis of these technologies shows them all to include the principle of the separation of concerns, and their further integration is a key factor to obtaining high-quality and self-adaptable software systems. Each technology identifies different concerns and deals with them separately in order to specify the design of the self-adaptive applications, and, at the same time, support software with adaptability and context-awareness. This research studies the development methodologies that employ the principles of model-driven development in building self-adaptive software systems. To this aim, this article proposes an evaluation framework for analysing and evaluating the features of model-driven approaches and their ability to support software with self-adaptability and dependability in highly dynamic contextual environment. Such evaluation framework can facilitate the software developers on selecting a development methodology that suits their software requirements and reduces the development effort of building self-adaptive software systems. This study highlights the major drawbacks of the propped model-driven approaches in the related works, and emphasise on considering the volatile aspects of self-adaptive software in the analysis, design and implementation phases of the development methodologies. In addition, we argue that the development methodologies should leave the selection of modelling languages and modelling tools to the software developers

Liability in the medical sector : the ‘breast-taking’ consequences of the poly implant prothese case

The article deals with the liability of third-party certifiers in the medical sector and especially focuses on the role of TuV Rheinland in the recent Poly Implant Prothese (PIP) breast implant case. The aim of the contribution is twofold. Firstly, it provides an overview of the different challenges that courts face when having to decide on the liability of certifiers of medical devices towards third parties. These, for instance, relate to the strict conditions under which certifiers can incur third-party liability under national law. Whether product certifiers can be held liable depends on the jurisdiction where the claims have been filed. Therefore, the PIP breast implant case is also interesting from a private international law perspective. Third-party certifiers can be sued before the courts of their domicile. Whether they can be brought before courts in other Member States depends inter alia on the interpretation of the place of the damaging event and the place of the damage. The difficulty to pinpoint these locations not only emerges in the field of jurisdiction but also manifests itself within the search for the applicable law as identical connecting factors are employed in that area of private international law. Secondly, the article examines the decisions that have been issued by national courts in the PIP breast implant case. Rulings in France and Germany denied compensation for patients who purchased the defective breast implants. The PIP case is currently pending before the European Court of Justice (ECJ). It thus remains to be seen what stance the ECJ will take and especially what the consequences might be for certifiers in the medical sector. Based on the analysis of these decisions, the contribution puts forth a number of reasons why the threat of liability seems the most effective way to guarantee that third-party certifiers issue accurate and reliable certificates. This in turn ensures that only safe medical devices are placed on the European market and safeguards the health of consumers. Future scandals with medical devices might in this way be prevented

Group-In: Group Inference from Wireless Traces of Mobile Devices

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 202

MLContext: A Context-Modeling Language for Context-Aware Systems

Context awareness refers to systems that can both sense and react based on their environment. The complexity of these systems makes necessary to apply software engineering techniques in their development, such as Model-Driven Software development (MDD). One of the main difficulties that developers of context-aware systems must tackle is how to manage the needed context information. In this paper, we present MLContext, a textual Domain Specific Language (DSL) which is specially tailored for modeling context information and automatically generating software artefacts from context models. It has been designed to provide a high-level abstraction, to be an easy to learn, and to promote reuse of context models. We have built a toolkit including an editor and a parser to convert MLContext textual specifications into models. As a proof of concept, we have automatically generated ontologies and Java code for the OCP middleware. MLContext models can be reused in applications with the same context because they do not include details related to the platforms or the implementation. These context models can be specified by non-developers users because MLContext provides high-level abstractions of the domain