Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: Computer Science On-Line Conference; Zlin; Zlin

Год издания: 2017

Идентификатор DOI: 10.1007/978-3-319-57141-6_16

Ключевые слова: Compensation model, Dependability, Multi-attribute decision making, N-version software, Application programs, Computer software, Computer software selection and evaluation, Fault tolerance, Intelligent systems, Redundancy, Software design, Software engineering, Compensation modeling, Multi attribute decision making, N version programming, Optimal variants, Real-life information, Software development approach, Software selection, Decision making

Аннотация: Multi-attribute decision making deals with discrete finite set of alternatives. The solution to the multi-attribute decision making problem is the choice of an alternative from the set of all possible alternatives on the base of usually contradicting attributes. In this paper, a new multi-attribute decision making model is presented. The proposed model develops a linear compensatory process for the interconnected attributes. It concerns the overall ranking of the alternatives based on the attribute-wise ranking as well as the interaction and the combination of the attributes. The compensation model of multi-attribute decision making is applied to N-version software selection. N-version programming is one of the well-known software development approach which ensures high dependability and fault tolerance of software. However, the problem of extra resource involvement arises since the N-version programming stipulates program redundancy. A set of characteristics/attributes have to be considered when choosing an optimal variant of N-version software. The proposed compensation model of multi-attribute decision making provides a solution to this problem. Additionally, a case study on choosing N-version software for a real-life information and control system problem is provided to verify the correctness of our model. © Springer International Publishing AG 2017.

Журнал: Advances in Intelligent Systems and Computing

Выпуск журнала: Vol. 575

Номера страниц: 148-157

ISSN журнала: 21945357

• Gruzenkin Denis Vladimirovich (Siberian Fed Univ, Krasnoyarsk, Russia)
• Grishina Galina Viktorovna (Siberian Fed Univ, Krasnoyarsk, Russia)
• Durmus Mustafa Seckin (Pamukkale Univ, Krasnoyarsk, Turkey)
• Ustoglu Ilker (Yildiz Tech Univ, Istanbul, Turkey)
• Tsarev Roman Yurievich (Siberian Fed Univ, Krasnoyarsk, Russia)

• Scopus
• Web of Science Core Collection
• Ядро РИНЦ (eLIBRARY.RU)