Reasoning Non-Functional Requirements Trade-off in Self-Adaptive Systems Using Multi-Entity Bayesian Network Modeling

Author(s)
AHMED ABDO ALI
Advisor
Seok-Won Lee
Department
일반대학원 컴퓨터공학과
Publisher
The Graduate School, Ajou University
Publication Year
2019-02
Language
eng
Alternative 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 (SAS) systems 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 Non-Functional Requirements (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. (Approach and Objective) We investigated the trade-offs between multiple Non-Functional Requirements in Self-Adaptive Systems. We fragmentized the Non-Functional 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.
URI
https://dspace.ajou.ac.kr/handle/2018.oak/14939
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Graduate School of Ajou University > Department of Computer Engineering > 3. Theses(Master)
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