A Bayesian Network Approach to Estimating Software Reliability of RSG-GAS Reactor Protection System

Abstract

Reliability represents one of the most important attributes of software quality. Assessing the reliability of software embedded in the safety of highly critical systems is essential. Unfortunately, there are many factors influencing software reliability that cannot be measured directly. Furthermore, the existing models and approaches for assessing software reliability have assumptions and limitations which are not directly acceptable for all systems, such as reactor protection systems. This paper presents the result of a study which aims to conduct quantitative assessment of the software reliability at the reactor protection system (RPS) of RSG-GAS based on software development life cycle. A Bayesian network (BN) is applied in this research and used to predict the software defect in the operation which represents the software reliability. The availability of operation failure data, characteristics of the RPS components and their operation features, prior knowledge on the software development and system reliability, as well as relevant finding from references were considered in the assessment and the construction of nodes on causal network model. The structure of causal model consists of eight nodes including design quality, problem complexity, and defect inserted in the software. The calculation result using Agenarisk software revealed that software defect in the operation of RPS follows binomial statistic distribution with the mean of 1.393. This number indicated the high software maturity level and high capability of the organization. The improvement of software defect concentration range on the posterior distribution compared with the prior's is also identified. The result achieved is valuable for further reliability estimation by introducing new evidence and experience data, and by setting up an appropriate plan in order to enhance software reliability in the RPS.

Dynamic Modeling of Reactor Protection System in Nuclear Power Plant for Reliability Evaluation Based on State Transition Diagram

“…In [14], Bayesian Network (BN) is utilized to predict the software fault in the software reliability analysis at the RPS of RSG-GAS based on Software Development Life Cycle (SDLC). The model structure consists of eight nodes.…”

Reactor Operational Experience Review and Analysis Based on Un-Intended Reactor Trip Data

“…Most operational evaluation and analysis conducted were related to the neutronics and thermalhydraulics aspects [14,15]. Other analyses on the operational experience data were emphasized on the challenge due to components reliability and ageing phenomena [16,17]. In many aspects, human factors tend to be specific and influenced by local population characteristics [18,19].…”