New technologies typically involve innovative aspects that are not addressed by the existing normative standards and hence are not assessable through common certification procedures. To ensure that new technologies can be implemented in a safe and reliable manner, a specific kind of assessment is performed, which in many industries, e.g., the energy sector, is known as Technology Qualification (TQ). TQ aims at demonstrating with an acceptable level of confidence that a new technology will function within specified limits. Expert opinion plays an important role in TQ, both to identify the safety and reliability evidence that needs to be developed, and to interpret the evidence provided. Hence, it is crucial to apply a systematic process for eliciting expert opinions, and to use the opinions for measuring the satisfaction of a technology's safety and reliability objectives. In this paper, drawing on the concept of assurance cases, we propose a goal-based approach for TQ. The approach, which is supported by a software tool, enables analysts to quantitatively reason about the satisfaction of a technology's overall goals and further to identify the aspects that must be improved to increase goal satisfaction. The three main components enabling quantitative assessment are goal models, expert elicitation, and probabilistic simulation. We report on an industrial pilot study where we apply our approach for assessing a new offshore technology. © 2011 IEEE.

Sabetzadeh, M., Falessi, D., Briand, L., Di Alesio, S., Mcgeorge, D., Ahjem, V., et al. (2011). Combining goal models, expert elicitation, and probabilistic simulation for qualification of new technology. In Proceedings of IEEE International Symposium on High Assurance Systems Engineering (pp.63-72) [10.1109/HASE.2011.22].

Combining goal models, expert elicitation, and probabilistic simulation for qualification of new technology

Falessi D.;
2011-01-01

Abstract

New technologies typically involve innovative aspects that are not addressed by the existing normative standards and hence are not assessable through common certification procedures. To ensure that new technologies can be implemented in a safe and reliable manner, a specific kind of assessment is performed, which in many industries, e.g., the energy sector, is known as Technology Qualification (TQ). TQ aims at demonstrating with an acceptable level of confidence that a new technology will function within specified limits. Expert opinion plays an important role in TQ, both to identify the safety and reliability evidence that needs to be developed, and to interpret the evidence provided. Hence, it is crucial to apply a systematic process for eliciting expert opinions, and to use the opinions for measuring the satisfaction of a technology's safety and reliability objectives. In this paper, drawing on the concept of assurance cases, we propose a goal-based approach for TQ. The approach, which is supported by a software tool, enables analysts to quantitatively reason about the satisfaction of a technology's overall goals and further to identify the aspects that must be improved to increase goal satisfaction. The three main components enabling quantitative assessment are goal models, expert elicitation, and probabilistic simulation. We report on an industrial pilot study where we apply our approach for assessing a new offshore technology. © 2011 IEEE.
13th IEEE International Symposium on High Assurance Systems Engineering, HASE 2011
Boca Raton, FL, usa
2011
IEEE
Rilevanza internazionale
contributo
2011
Settore ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
English
Assurance cases
Expert elicitation
Goal modeling
Monte Carlo simulation
Technology Qualification
Intervento a convegno
Sabetzadeh, M., Falessi, D., Briand, L., Di Alesio, S., Mcgeorge, D., Ahjem, V., et al. (2011). Combining goal models, expert elicitation, and probabilistic simulation for qualification of new technology. In Proceedings of IEEE International Symposium on High Assurance Systems Engineering (pp.63-72) [10.1109/HASE.2011.22].
Sabetzadeh, M; Falessi, D; Briand, L; Di Alesio, S; Mcgeorge, D; Ahjem, V; Borg, J
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/273347
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