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Non-functional requirements trade-off in self-adaptive systems
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Publication Year
2018-10-19
Journal
Proceedings - 2018 4th International Workshop on Requirements Engineering for Self-Adaptive, Collaborative, and Cyber Physical Systems, RESACS 2018
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - 2018 4th International Workshop on Requirements Engineering for Self-Adaptive, Collaborative, and Cyber Physical Systems, RESACS 2018, pp.9-15
Keyword
Goal modelMulti-entity Bayesian networkNon functional requirements trade-offSelf-adaptive systemsUncertainty
Mesh Keyword
Goal modelingMulti-Entity Bayesian NetworksSelf-adaptive systemTrade offUncertainty
All Science Classification Codes (ASJC)
Artificial IntelligenceSoftwareSafety, Risk, Reliability and Quality
Abstract
(Context and Motivation) Non-Functional Requirements (NFR) play a crucial role during the software development process. Currently, Non-Functional Requirements considered to be more important than Functional Requirements and can determine the success of the software system. Non-Functional Requirements can be very complicated to understand due to their subjective manner and especially their conflicting nature. Many approaches and techniques have been introduced to manage the conflicts between multiple Non-functional Requirements and to analyze the trade-off in costs and benefits between the alternative solutions that satisfy them. (Problem) Self-Adaptive Systems tends to change its behavior and configurations due to the changes in its environment. Current solutions might not be suitable for the current situations, because current approaches managing Non-Functional Requirements trade-off stops managing them during the system runtime. (Approach and Objective) In this paper, we investigated the trade-offs between multiple Non-Functional Requirements in Self-Adaptive Systems. We fragmentized the NonFunctional Requirements and its alternative solutions in form of Multi-entity Bayesian network fragments. As a result, when changes occur, our system creates a situation specific Bayesian network to measure the impact of the system's conditions and environmental changes on the Non-Functional Requirements satisfaction. Furthermore, it dynamically decides which alternative solution is suitable for the current situation.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36314
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85061372956&origin=inward
DOI
https://doi.org/10.1109/resacs.2018.00007
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8500727
Type
Conference
Funding
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03034279).
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Lee, Seok-Won Image
Lee, Seok-Won이석원
Department of Software and Computer Engineering
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