The Systems Integration Technical Risk assessment fusing of Bayesian Belief Networks and Parametric Models

Authors:
Irena Loutchkina;Lakhmi C. Jain;Thong Nguyen;Sergey Nesterov
Affiliations:
University of South Australia, Mawson Lakes, SA, Australia;Knowledge-Based Intelligent Engineering Systems KES Centre, School of Electrical and Information Engineering, University of South Australia, Mawson Lakes, SA, Australia;Airborne Mission Systems Branch, Air Operations Division, Defence Science and Technology Organisation DSTO, Edinburgh, Australia;University of South Australia, Mawson Lakes, SA, Australia
Venue:
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
Year:
2013

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Abstract

This paper presents an approach for modelling Systems Integration Technical Risks SITR assessment using Bayesian Belief Networks BBN. SITR represent a significant part of project risks associated with a development of large software intensive systems. We propose conceptual modelling framework to address the problem of SITR assessment at early stages of a system life cycle. This framework includes a set of BBN models, representing the risk contributing factors, and complementing Parametric Models PM, used for providing input data to the BBN models. In particular we describe SITR identification approach explaining corresponding BBN models' topologies and relevant conceptual model framework. This framework includes a set of BBN models, representing the risk contributing factors, fused with complementary PMs providing input data to the BBN models. Heuristic approaches for easing Conditional Probabilities Tables CPT generation are described. We briefly discuss preliminary results of model testing. In conclusion we summarise benefits and constraints for SITR assessment based on BBN models, and provide suggestions for further research directions for model improvement.