ADSEng

A model-based methodology for autonomous digital service engineering

Dhaminda B. Abeywickrama, Eila Ovaska

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    1 Citation (Scopus)

    Abstract

    In digital service ecosystems (DSEs), business stakeholders provide the most important driving factors and managing them is a challenge. It requires systems and services to handle uncertainty. Uncertainty in DSEs can be attributed to several factors; for example, dynamic nature and the unknown deployment environment, and change and evolution of requirements. Therefore, there is a need for novel software engineering methods and tools to handle these uncertainties in DSEs. In this regard, valuable lessons can be learnt from the autonomic computing (AC) paradigm and systems that are characterized by self-properties. This paper proposes a novel, systematic service engineering methodology called ADSEng for ecosystem-based engineering of autonomous digital services. In the current research, the means of handling uncertainty from requirements to architecture and running systems are investigated. To do this, two interrelated research problems are studied: reflexivity that is realized using AC techniques, and evolvability of the ecosystem, supported by automated transformations. Our main contributions are: (i) a modeling methodology from uncertainty specification to runtime models and (ii) quality-driven adaptation patterns embodied by digital services. The paper also presents key lessons learnt from the research experience thus far.

    Original languageEnglish
    Title of host publication8th International Conference on Management of Digital EcoSystems, MEDES 2016
    PublisherAssociation for Computing Machinery ACM
    Pages34-42
    Number of pages9
    ISBN (Print)978-1-4503-4267-4
    DOIs
    Publication statusPublished - 1 Nov 2016
    MoE publication typeA4 Article in a conference publication
    Event8th International Conference on Management of Digital EcoSystems, MEDES 2016 - Biarritz, France
    Duration: 1 Nov 20164 Nov 2016

    Conference

    Conference8th International Conference on Management of Digital EcoSystems, MEDES 2016
    CountryFrance
    CityBiarritz
    Period1/11/164/11/16

    Fingerprint

    Ecosystems
    Software engineering
    Uncertainty
    Specifications
    Industry

    Keywords

    • reflexivity
    • evolvability
    • self- properties
    • quality attributes
    • digital ecosystem

    Cite this

    Abeywickrama, D. B., & Ovaska, E. (2016). ADSEng: A model-based methodology for autonomous digital service engineering. In 8th International Conference on Management of Digital EcoSystems, MEDES 2016 (pp. 34-42). Association for Computing Machinery ACM. https://doi.org/10.1145/3012071.3012072
    Abeywickrama, Dhaminda B. ; Ovaska, Eila. / ADSEng : A model-based methodology for autonomous digital service engineering. 8th International Conference on Management of Digital EcoSystems, MEDES 2016. Association for Computing Machinery ACM, 2016. pp. 34-42
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    Abeywickrama, DB & Ovaska, E 2016, ADSEng: A model-based methodology for autonomous digital service engineering. in 8th International Conference on Management of Digital EcoSystems, MEDES 2016. Association for Computing Machinery ACM, pp. 34-42, 8th International Conference on Management of Digital EcoSystems, MEDES 2016, Biarritz, France, 1/11/16. https://doi.org/10.1145/3012071.3012072

    ADSEng : A model-based methodology for autonomous digital service engineering. / Abeywickrama, Dhaminda B.; Ovaska, Eila.

    8th International Conference on Management of Digital EcoSystems, MEDES 2016. Association for Computing Machinery ACM, 2016. p. 34-42.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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    Abeywickrama DB, Ovaska E. ADSEng: A model-based methodology for autonomous digital service engineering. In 8th International Conference on Management of Digital EcoSystems, MEDES 2016. Association for Computing Machinery ACM. 2016. p. 34-42 https://doi.org/10.1145/3012071.3012072