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

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2019, 24(3), pp.65-75
  • DOI : 10.9708/jksci.2019.24.03.065
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : December 11, 2018
  • Accepted : February 19, 2019
  • Published : March 29, 2019

Ahmed Abdo Ali Saeed 1 Seok-Won Lee 1

1아주대학교

Accredited

ABSTRACT

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.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.