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

Detecting the Onset of Dementia using Context-Oriented Architecture

In the last few years, Aspect Oriented Software De- velopment (AOSD) and Context Oriented Software Development (COSD) have become interesting alternatives for the design and construction of self-adaptive software systems. An analysis of these technologies shows them all to employ the principle of the separation of concerns, Model Driven Architecture (MDA) and Component-based Software Development (CBSD) for building high quality of software systems. In general, the ultimate goal of these technologies is to be able to reduce development costs and effort, while improving the adaptability, and dependability of software systems. COSD, has emerged as a generic devel- opment paradigm towards constructing self-adaptive software by integrating MDA with context-oriented component model. The self-adaptive applications are developed using a Context- Oriented Component-based Applications Model-Driven Architec- ture (COCA-MDA), which generates an Architecture Description language (ADL) presenting the architecture as a components- based software system. COCA-MDA enables the developers to modularise the application based on their context-dependent behaviours, and separate the context-dependent functionality from the context-free functionality of the application. In this article, we wish to study the impact of the decomposition mechanism performed in MDA approaches over the software self-adaptability. We argue that a better and significant advance in software modularity based on context information can increase software adaptability and increase their performance and modi- fiability

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

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

Separating Agent-Functioning and Inter-Agent Coordination by Activated Modules: The DECOMAS Architecture

The embedding of self-organizing inter-agent processes in distributed software applications enables the decentralized coordination system elements, solely based on concerted, localized interactions. The separation and encapsulation of the activities that are conceptually related to the coordination, is a crucial concern for systematic development practices in order to prepare the reuse and systematic integration of coordination processes in software systems. Here, we discuss a programming model that is based on the externalization of processes prescriptions and their embedding in Multi-Agent Systems (MAS). One fundamental design concern for a corresponding execution middleware is the minimal-invasive augmentation of the activities that affect coordination. This design challenge is approached by the activation of agent modules. Modules are converted to software elements that reason about and modify their host agent. We discuss and formalize this extension within the context of a generic coordination architecture and exemplify the proposed programming model with the decentralized management of (web) service infrastructures

Designing a goal-oriented smart-home environment

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-016-9670-x[EN] Nowadays, systems are growing in power and in access to more resources and services. This situation makes it necessary to provide user-centered systems that act as intelligent assistants. These systems should be able to interact in a natural way with human users and the environment and also be able to take into account user goals and environment information and changes. In this paper, we present an architecture for the design and development of a goal-oriented, self-adaptive, smart-home environment. With this architecture, users are able to interact with the system by expressing their goals which are translated into a set of agent actions in a way that is transparent to the user. This is especially appropriate for environments where ambient intelligence and automatic control are integrated for the user’s welfare. In order to validate this proposal, we designed a prototype based on the proposed architecture for smart-home scenarios. We also performed a set of experiments that shows how the proposed architecture for human-agent interaction increases the number and quality of user goals achieved.This work is partially supported by the Spanish Government through the MINECO/FEDER project TIN2015-65515-C4-1-R.Palanca Cámara, J.; Del Val Noguera, E.; García-Fornes, A.; Billhard, H.; Corchado, JM.; Julian Inglada, VJ. (2016). Designing a goal-oriented smart-home environment. Information Systems Frontiers. 1-18. https://doi.org/10.1007/s10796-016-9670-xS118Alam, M. R., Reaz, M. B. I., & Ali, M. A. M. (2012). A review of smart homes: Past, present, and future. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(6), 1190–1203.Andrushevich, A., Staub, M., Kistler, R., & Klapproth, A. (2010). Towards semantic buildings: Goal-driven approach for building automation service allocation and control. In 2010 IEEE conference on emerging technologies and factory automation (ETFA) (pp. 1–6) IEEE.Ayala, I., Amor, M., & Fuentes, L. (2013). Self-configuring agents for ambient assisted living applications. Personal and Ubiquitous Computing, 17(6), 1159–1169.Cetina, C., Giner, P., Fons, J., & Pelechano, V. (2009). Autonomic computing through reuse of variability models at runtime: The case of smart homes. Computer, 42(10), 37–43.Cook, D. J. (2009). Multi-agent smart environments. Journal of Ambient Intelligence and Smart Environments, 1(1), 51–55.Dalpiaz, F., Giorgini, P., & Mylopoulos, J. (2009). An architecture for requirements-driven self-reconfiguration. In Advanced information systems engineering (pp. pp 246–260). Springer.De Silva, L. C., Morikawa, C., & Petra, I. M. (2012). State of the art of smart homes. Engineering Applications of Artificial Intelligence, 25(7), 1313–1321.Huhns, M., & et al. (2005). Research directions for service-oriented multiagent systems. IEEE Internet Computing, 9, 69–70.Iftikhar, M. U., & Weyns, D. (2014). Activforms: active formal models for self-adaptation. In SEAMS, (pp 125–134).Kucher, K., & Weyns, D. (2013). A self-adaptive software system to support elderly care. Modern Information Technology, MIT.Lieberman, H., & Espinosa, J. (2006). A goal-oriented interface to consumer electronics using planning and commonsense reasoning. In Proceedings of the 11th international conference on Intelligent user interfaces (pp. 226–233).Liu, H., & Singh, P. (2004). ConceptNet—a practical commonsense reasoning tool-kit. BT Technology Journal, 22(4), 211–226.Loseto, G., Scioscia, F., Ruta, M., & Di Sciascio, E. (2012). Semantic-based smart homes: a multi-agent approach. In 13th Workshop on objects and Agents (WOA 2012) (Vol. 892, pp. 49–55).Martin, D., Burstein, M., Hobbs, J., Lassila, O., McDermott, D., McIlraith, S., Narayanan, S., Paolucci, M., Parsia, B., Payne, T., & et al (2004). OWL-S: Semantic markup for web services. W3C Member Submission, 22, 2007–2004.Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G, & Gotts, N. M. (2007). Agent-based land-use models: a review of applications. Landscape Ecology, 22(10), 1447–1459.Molina, J. M., Corchado, J. M., & Bajo, J. (2008). Ubiquitous computing for mobile environments. In Issues in multi-agent systems (pp 33–57). Birkhäuser, Basel.Palanca, J., Navarro, M., Julian, V., & García-Fornes, A. (2012). Distributed goal-oriented computing. Journal of Systems and Software, 85(7), 1540–1557. doi: 10.1016/j.jss.2012.01.045 .Rao, A., & Georgeff, M. (1995). BDI agents: From theory to practice. In Proceedings of the first international conference on multi-agent systems (ICMAS95) (pp. 312–319).Reddy, Y. (2006). Pervasive computing: implications, opportunities and challenges for the society. In 1st International symposium on pervasive computing and applications (p. 5).de Silva, L., & Padgham, L. (2005). Planning as needed in BDI systems. International Conference on Automated Planning and Scheduling.Singh, P. (2002). The public acquisition of commonsense knowledge. In Proceedings of AAAI Spring symposium acquiring (and using) linguistic (and world) knowledge for information access

Distributed Learning System Design: A New Approach and an Agenda for Future Research

This article presents a theoretical framework designed to guide distributed learning design, with the goal of enhancing the effectiveness of distributed learning systems. The authors begin with a review of the extant research on distributed learning design, and themes embedded in this literature are extracted and discussed to identify critical gaps that should be addressed by future work in this area. A conceptual framework that integrates instructional objectives, targeted competencies, instructional design considerations, and technological features is then developed to address the most pressing gaps in current research and practice. The rationale and logic underlying this framework is explicated. The framework is designed to help guide trainers and instructional designers through critical stages of the distributed learning system design process. In addition, it is intended to help researchers identify critical issues that should serve as the focus of future research efforts. Recommendations and future research directions are presented and discussed