다중 개체 베이지안 네트워크 모델링을 이용한 자가 적응 시스템에서의 추론된 비기능적 요구사항의 균형
Reasoning Non-Functional Requirements Trade-off in Self-Adaptive Systems Using Multi-Entity Bayesian Network Modeling
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
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.
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.
참고문헌 (27)
- Chung, L., "Conceptual modeling: Foundations and applications", pp.363~379 (2009), Springer
- Boehm, B.W, "Characteristics of software quality" (1978), North-Holland Pub. Co
- "System and Software Requirements Engineering, IEEE Computer Society Press Tutorial" (1990), System and Software Requirements Engineering, IEEE Computer Society Press Tutorial
- Roman, G. C., "A taxonomy of current issues in requirements engineering", Computer, 4, pp.14~23 (1985)
- GOLDSBY, "Goal-based modeling of dynamically adaptive system requirements", 15th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ecbs 2008), pp.36~45 (2008), IEEE
- ZIV, Hadar, "The uncertainty principle in software engineering", submitted to Proceedings of the 19th International Conference on Software Engineering (ICSE'97) (1997)
- DE LEMOS, Rogério, "Software Engineering for Self-Adaptive Systems II", pp.1~32 (2013), Springer
- Mylopoulos, John, "Representing and using nonfunctional requirements: A process-oriented approach", IEEE Transactions on software engineering, 18(6), pp.483~497 (1992)
- Chung, Lawrence, "Non-functional requirements in software engineering, Vol. 5" (2012), Springer Science &Business Media
- Fenton, Norman, "Making decisions : using Bayesian nets and MCDA", Knowledge-Based Systems, 14(7), pp.307~325 (2001)
- S. J. Russell, "Artificial intelligence : A modern approach" (2003), Prentice Hall
- Filieri, Antonio, "A formal approach to adaptive software: continuous assurance of non-functional requirements", Formal Aspects of Computing, 24(2), pp.163~186 (2012)
- Portinale, Luigi, "Using dynamic decision networks and extended fault trees for autonomous fdir", 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, pp.480~484 (2011)
- Bencomo, Nelly, "Dynamic decision networks for decision-making in self-adaptive systems: a case study", Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp.113~122 (2013)
- Laskey, K.B., "MEBN : A language for first-order Bayesian knowledge bases", Artificial intelligence, 172(2-3), pp.140~178 (2008)
- Carvalho, R.N, "PROGNOS: predictive situational awareness with probabilistic ontologies", Information Fusion (FUSION), 2010 13th Conference, pp.1~8 (2010)
- Park, C.Y, "Predictive situation awareness reference model using multi-entity bayesian networks", Information Fusion (FUSION), 2014 17th International Conference, pp.1~8 (2014)
- Boruah, A., "An MEBN framework as a dynamic firewall's knowledge flow architecture", Signal Processing and Integrated Networks (SPIN), 2014International Conference, pp.249~254 (2014)
- Lee, H.C, "Decision supporting approach under uncertainty for feature-oriented adaptive system", Computer Software and Applications Conference (COMPSAC), 2015 IEEE 39th Annual, pp.324~329 (2015)
- Lee, S.W, "Probabilistic risk assessment for security requirements: A preliminary study", 2011 Fifth International Conference on Secure Software Integration and Reliability Improvement, pp.11~20 (2011)
- AlGhamdi, G, "Modeling insider behavior using multi-entity Bayesian networks", ~ (2006)
- Carvalho, R.N, "Uncertainty Reasoning for the Semantic Web II", pp.19~40 (2013), Springer
- Golestan, K, "Situation awareness within the context of connected cars : A comprehensive review and recent trends", Information Fusion, 29, pp.68~83 (2016)
- Lee, S.W., "Case Study Methodology Designed Research in Software Engineering Methodology Validation", SEKE, pp.117~122 (2004)
- Yin, Robert K, "Case study research: Design and methods" (2013), Sage publications
- Bencomo, N., "Supporting decision-making for self-adaptive systems: from goal models to dynamic decision networks", International Working Conference on Requirements Engineering:Foundation for Software Quality, pp.221~236 (2013), Springer
- Ramirez, A.J, "Relaxing claims: Coping with uncertainty while evaluating assumptions at run time", International Conference on Model Driven Engineering Languages and Systems, pp.53~69 (2012), Springer