한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제24권3호
  • /
  • Pages.65-75
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  • 2019
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  • 1598-849X(pISSN)
  • /
  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Reasoning Non-Functional Requirements Trade-off in Self-Adaptive Systems Using Multi-Entity Bayesian Network Modeling

  • 투고 : 2018.12.11
  • 심사 : 2019.02.19
  • 발행 : 2019.03.29
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초록

Non-Functional Requirements (NFR) play a crucial role during the software development process. Currently, NFRs are considered more important than Functional Requirements and can determine the success of a software system. NFRs can be very complicated to understand due to their subjective manner and especially their conflicting nature. Self-adaptive systems (SAS) are operating in dynamically changing environment. Furthermore, the configuration of the SAS systems is dynamically changing according to the current systems context. This means that the configuration that manages the trade-off between NFRs in this context may not be suitable in another. This is because the NFRs satisfaction is based on a per-context basis. Therefore, one context configuration to satisfy one NFR may produce a conflict with another NFR. Furthermore, current approaches managing Non-Functional Requirements trade-off stops managing them during the system runtime which of concern. To solve this, we propose fragmentizing the NFRs and their alternative solutions in form of Multi-entity Bayesian network fragments. Consequently, 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 NFRs satisfaction. Moreover, it dynamically decides which alternative solution is suitable for the current situation.

키워드

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Fig. 1.Proposed Approach

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Fig. 2.Network of Main Components to Construct MEBN Fragments

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Fig. 3.MEBN Construction

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Fig. 4.Robot Vacuum Cleaner Goal Model [26]

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Fig. 5. RDM Goal Model [27]

Table 1. Study Questions

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Table 2. Study Propositions

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Table 3.Unit of Analysis

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Table 4.Linking Proposition with Unit of Analysis

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Table 5. Methodology On the Robot Vacuum Cleaner

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Table 6. Vacuum Cleaner Different Situation Result

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Table 7. Methodology on the RDM

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Table 8. Vacuum Cleaner Different Situation Result

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