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Engineering Trustworthy Self-Adaptive Software with Dynamic Assurance Cases
University of York, UK.ORCID-id: 0000-0002-2678-9260
Katholieke Universiteit Leuven. (AdaptWise)ORCID-id: 0000-0002-1162-0817
University of York, UK.ORCID-id: 0000-0002-2706-5272
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). (AdaptWise)ORCID-id: 0000-0002-1343-5834
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2018 (engelsk)Inngår i: IEEE Transactions on Software Engineering, ISSN 0098-5589, E-ISSN 1939-3520, Vol. 44, nr 11, s. 1039-1069Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Building on concepts drawn from control theory, self-adaptive software handles environmental and internal uncertainties by dynamically adjusting its architecture and parameters in response to events such as workload changes and component failures. Self-adaptive software is increasingly expected to meet strict functional and non-functional requirements in applications from areas as diverse as manufacturing, healthcare and finance. To address this need, we introduce a methodology for the systematic ENgineering of TRUstworthy Self-adaptive sofTware (ENTRUST). ENTRUST uses a combination of (1) design-time and runtime modelling and verification, and (2) industry-adopted assurance processes to develop trustworthy self-adaptive software and assurance cases arguing the suitability of the software for its intended application. To evaluate the effectiveness of our methodology, we present a tool-supported instance of ENTRUST and its use to develop proof-of-concept self-adaptive software for embedded and service-based systems from the oceanic monitoring and e-finance domains, respectively. The experimental results show that ENTRUST can be used to engineer self-adaptive software systems in different application domains and to generate dynamic assurance cases for these systems.

sted, utgiver, år, opplag, sider
IEEE, 2018. Vol. 44, nr 11, s. 1039-1069
Emneord [en]
Self-adaptive software systems, software engineering methodology, assurance evidence, assurance cases
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
URN: urn:nbn:se:lnu:diva-67353DOI: 10.1109/TSE.2017.2738640ISI: 000449964600002Scopus ID: 2-s2.0-85029184307OAI: oai:DiVA.org:lnu-67353DiVA, id: diva2:1135272
Tilgjengelig fra: 2017-08-22 Laget: 2017-08-22 Sist oppdatert: 2019-08-29bibliografisk kontrollert

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Weyns, DannyIftikhar, Muhammad Usman

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Calinescu, RaduWeyns, DannyGerasimou, SimosIftikhar, Muhammad Usman
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