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
Note: [] Corresponding author. Irena Loutchkina, University of South Australia, Mawson Lakes, SA, Australia. E-mail: iloutchk@ieee.org
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.
Keywords: Systems integration risks, systems integration risks modeling, Bayesian networks, risk assessment
