Tele Assistance: A Self-Adaptive Service-Based System Exemplar

    Research on adaptive and self-managing systems is hindered by a lack of prototypical applications that researchers could use to evaluate and compare new methods, techniques and tools. To address this limitation, we introduce a reference implementation of a Tele Assistance System (TAS) for research on self-adaptation in the domain of service-based systems. Our TAS exemplar of service-based systems comes with pre-defined scenarios for comparing the effectiveness of different self-adaptation solutions. Other researchers can easily exploit the underlying service platform, reusable components and development method we devised for TAS to speed up the engineering of additional research exemplars. This webpage describes the TAS and provides links with resources that can be downloaded for experimentation with TAS. The description is structured based on the template for documenting exemplars proposed at the community website for self-adaptive systems. The definition of the TAS exemplar is a joint effort of AdaptWise, at the Computer Science Department at Linnaeus University and the Computer Science Department at York University, UK.


    The Tele Assistance System (TAS) is based on technology services for elderly and chronic sick people that provide care and reassurance needed to allow them to remain living in their own homes. The potential of technology-based services to improve the quality of live of the people with needs and reduce the cost for care has widely been accepted amongst academics and practitioners. There is a huge market potential for these technologies, however, the adoption is hampered by several societal and technological barriers. Key technical factors are reliability, safety, and interoperability, and this is where research on self-adaptation can contribute.

    TAS is an exemplar of a service-based system (SBS). SBSs are widely used in e-commerce, online banking, e-health and many other applications. In these systems, services offered by third-party providers are dynamically composed into workflows delivering complex functionality. SBSs increasingly rely on self-adaptation to cope with the uncertainties associated with third-party services, as the loose coupling of services makes online reconfiguration feasible.

    TAS was originally introduced in [1], and has the advantage that it has already been used in the evaluation of several self-adaptation solutions [2-5], albeit based on ad-hoc implementations, scenarios and evaluation metrics that make the comparison of these solutions and its use to evaluate other solutions very difficult. To address these limitations, we propose the TAS exemplar.

    The TAS provides health support to chronic condition sufferers within the comfort of their home. TAS uses a combination of sensors embedded in a wearable device and remote services from healthcare, pharmacy and emergency service providers. As shown in the figure, the TAS workflow takes periodical measurements of the vital parameters of a patient and employs a third-party medical service for their analysis.


    The analysis result may trigger the invocation of a pharmacy service to deliver new medication to the patient or to change his/her dose of medication, or the invocation of an alarm service leading, e.g., to an ambulance being dispatched to the patient. The same alarm service can be invoked directly by the patient, by using a panic button on the wearable device.

    In practice, the TAS will be subject to a variety of uncertainties, such as services may fail, service response times may vary, or new services may become available. Users may even require new features or new services need to be integrated to the system, which were not anticipated upfront (e.g., in case of an alarm, relatives may need to be informed).

    Different types of adaptations can be applied to deal with these uncertainties, such as switch to equivalent service, simultaneous invocation of several services for idempotent operations, or change the workflow architecture. Adaptations should minimize both the time to adapt the system and the cost for using services.

    Relevance: Reliability of Service-Based Systems

    Assistance systems such as TAS provide services to people that may be critical. Consequently, reliability is a primary concern of the stakeholders. A recent survey [6] collected empirical evidence about the adoption intention of assistive technologies by elderly at home and identified reliability as a main concern. In other domains of service-based systems, such as e-commerce and online banking, reliability is equally well a primary concern. A systematic review [7] identified reliability, i.e., the ability to deliver reliable and trustworthy services, as a key design principle for e-commerce applications.

    The problems with reliability of SBS is grounded in uncertainty of the environment, the system itself and its goals. Inspired by [8], we identified the following types of uncertainties for SBS:

    • Unpredictable environment: service failure; variation of service response time
    • Incomplete information: new service
    • Changing requirements: new goal
    • Inadequate design: wrong operation sequence

    Each of these uncertainties may jeopardise the reliability of a SBS. In practice, it is important that adaptations to deal with these uncertainties are performed autonomically, that is, with minimum human intervention. Our aim is to demonstrate such self-adaptation with the TAS exemplar.

    Adaptation Challenges

    TAS has to deal with several types of requirements. The following table describes generic adaptation scenarios for the TAS.


    For each type of uncertainty, the type of adaptations are listed and the type of requirement that the adaptations are meant to meet handles. Within these scenarios, the exemplar allows the evaluation and comparison of different self-adaptation solutions based on quality attributes and metrics described the following table (inspired on [9]).


    TAS Download and Setup

    We have implemented TAS using a new Research Service Platform (ReSeP), and we propose predefined scenarios for its immediate use in the evaluation of self-adaptation solutions. In addition, other researchers can take advantage of ReSeP and its reusable components to speed up the engineering of new service-based system exemplars. We have applied ActivFORMS [10] to test two strategies for one concrete TAS scenario. All the experimentation material with instructions to setup TAS and use it for experimentation can be downloaded via the following links:


    [1] L. Baresi, D. Bianculli, C. Ghezzi, S. Guinea, and P. Spoletini, Validation of web service compositions, Software, IET, 1(6):219-232, December 2007 (DOI)

    [2] R. Calinescu, Lars Grunske, M. Kwiatkowska, R. Mirandola, and G. Tamburrelli, Dynamic QoS management and optimization in service-based systems, IEEE Transactions on Software Engineering, 37(3):387-409, May 2011 (DOI)

    [3] Radu Calinescu, Carlo Ghezzi, Marta Kwiatkowska, and Raffaela Mirandola, Self-adaptive software needs quantitative verification at runtime, Communications of the ACM, 55(9):69-77, September 2012 (DOI)

    [4] I. Epifani, C. Ghezzi, R. Mirandola, and G. Tamburrelli, Model evolution by run-time parameter adaptation, International Conference on Software Engineering, ICSE 2009. (DOI)

    [5] Antonio Filieri, Carlo Ghezzi, Raffaela Mirandola, and Giordano Tamburrelli, Conquering complexity via seamless integration of design-time and run-time verification, Conquering Complexity, pages 253-275. Springer London, 2012. (Springer)

    [6] Zhao Huang, Morad Benyoucef, From e-commerce to social commerce: A close look at design features, Electronic Commerce Research and Applications, Volume 12, Issue 4, July-August 2013, Pages 246-259, ISSN 1567-4223, (DOI)

    [7] Sanna Sintonen, Mika Immonen, Telecare services for aging people: Assessment of critical factors influencing the adoption intention, Computers in Human Behavior, Volume 29, Issue 4, July 2013, Pages 1307-1317, ISSN 0747-5632, (DOI)

    [8] A.J. Ramirez, A.C. Jensen, and B.H.C. Cheng, A taxonomy of uncertainty for dynamically adaptive systems, International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2012 (DOI)

    [9] Norha M. Villegas, Hausi A. Muller, Gabriel Tamura, Laurence Duchien, and Rubby Casallas, A framework for evaluating quality-driven self- adaptive software systems, International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2011 (DOI)

    [10] M. Usman Iftikhar, Danny Weyns: ActivFORMS: active formal models for self-adaptation, International Symposium in Software Engineering of Adaptive and Self-Managing Systems, SEAMS 2014 (pdf)