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Provenance-Based Trust-Aware Requirements Engineering Framework for Self-Adaptive Systemsoa mark
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Publication Year
2023-05-01
Publisher
MDPI
Citation
Sensors, Vol.23
Keyword
goal modelingprovenancerequirements engineeringself-adaptive systemtrust
Mesh Keyword
Engineering frameworksEngineering phaseEvidence modelGoal modelsProvenanceRequirement engineeringSelf-adaptive systemTrustTrust evidencesTrust-aware
All Science Classification Codes (ASJC)
Analytical ChemistryInformation SystemsAtomic and Molecular Physics, and OpticsBiochemistryInstrumentationElectrical and Electronic Engineering
Abstract
With the development of artificial intelligence technology, systems that can actively adapt to their surroundings and cooperate with other systems have become increasingly important. One of the most important factors to consider during the process of cooperation among systems is trust. Trust is a social concept that assumes that cooperation with an object will produce positive results in the direction we intend. Our objectives are to propose a method for defining trust during the requirements engineering phase in the process of developing self-adaptive systems and to define the trust evidence models required to evaluate the defined trust at runtime. To achieve this objective, we propose in this study a provenance-based trust-aware requirement engineering framework for self-adaptive systems. The framework helps system engineers derive the user’s requirements as a trust-aware goal model through analysis of the trust concept in the requirements engineering process. We also propose a provenance-based trust evidence model to evaluate trust and provide a method for defining this model for the target domain. Through the proposed framework, a system engineer can treat trust as a factor emerging from the requirements engineering phase for the self-adaptive system and understand the factors affecting trust using the standardized format.
ISSN
1424-8220
Language
eng
URI
https://dspace.ajou.ac.kr/dev/handle/2018.oak/33432
DOI
https://doi.org/10.3390/s23104622
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Type
Article
Funding
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2020R1F1A1075605). This work was supported by the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education (NRF5199991014091).
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Lee, Seok-Won Image
Lee, Seok-Won이석원
Department of Software and Computer Engineering
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