A Software Reliability Estimation Method to Nuclear Safety Software

Park, Gee-Yong; Jang, Seung-Cheol

doi:10.5516/net.04.2012.067

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…Extensive research has been carried out to develop a method for estimating the average probability of failure on demand of a safety instrument system, including the digital system platform [4,5]. Referring to IEC (International Electrotechnical Commission) standard, the probability of a failure on demand of a safety critical system should be on the order of 10 −5 .…”

Section: Introduction mentioning

confidence: 99%

“…45 No. 1 (2019) 43 -49 Atom Indonesia Journal homepage: http://aij.batan.go.id hardware faults occur due to aging or wearing out, but software faults causing a digital system failure are design fault introduced during the software development or maintenance phase [5]. A root cause analysis of the software defects based on the orthogonal defects classification in software development has been proposed [6].…”

Section: Introduction mentioning

confidence: 99%

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

2019

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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.

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Section: Introduction mentioning

confidence: 99%

Section: Introduction mentioning

confidence: 99%

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

2019

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“…With the advancement of digital systems and software being used in the SCS, study on reliability of the software is becoming crucial to avoid the catastrophic disasters. Failure of hardware is due to the aging or wearing out, but software neither ages nor wears out, it fails due to the design faults introduced at the time of development phase or the maintenance phase . The extent to which any system meets its operational availability is determined by the ability to deliver the software of the system.…”

Section: Introductionmentioning

confidence: 99%

“…Failure of hardware is due to the aging or wearing out, but software neither ages nor wears out, it fails due to the design faults introduced at the time of development phase or the maintenance phase. 2 The extent to which any system meets its operational availability is determined by the ability to deliver the software of the system. Even today, very less effort is applied for the reliability measurement in the software industry, and even the organizations which measure the reliability determine it from the historical perspective.…”

Section: Introductionmentioning

confidence: 99%

Suitability analysis of software reliability models for its applicability on NPP systems

Kumar

Singh

Kumar

2018

Quality & Reliability Eng

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The advent of software components in the safety critical systems of nuclear power plant has introduced new challenges for software professionals to provide increased software reliability. Several proposals have been proposed for ensuring software reliability in different phases of software development life cycle. The present article is a novel attempt in providing an exhaustive survey of software reliability models for their applicability on safety critical systems of nuclear power plants. Our systematic review shows that none of the proposals for ensuring software reliability are applicable on such systems. The issues and challenges are discussed, and a software reliability model for safety critical system of nuclear power plants is proposed.

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“…For example in [45], author uses the NHPP to estimate software reliability for nuclear safety software. The Bayesian statistical inference (BSI) method was used to estimate the model parameters.…”

Section: B Power Model (Powm)mentioning

confidence: 99%

Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm

Sheta¹,

Abdel-Raouf²

2016

ijacsa

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Abstract-In this age of technology, building quality software is essential to competing in the business market. One of the major principles required for any quality and business software product for value fulfillment is reliability. Estimating software reliability early during the software development life cycle saves time and money as it prevents spending larger sums fixing a defective software product after deployment. The Software Reliability Growth Model (SRGM) can be used to predict the number of failures that may be encountered during the software testing process. In this paper we explore the advantages of the Grey Wolf Optimization (GWO) algorithm in estimating the SRGM's parameters with the objective of minimizing the difference between the estimated and the actual number of failures of the software system. We evaluated three different software reliability growth models: the Exponential Model (EXPM), the Power Model (POWM) and the Delayed S-Shaped Model (DSSM). In addition, we used three different datasets to conduct an experimental study in order to show the effectiveness of our approach.

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A Software Reliability Estimation Method to Nuclear Safety Software

Cited by 11 publications

References 12 publications

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

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

Suitability analysis of software reliability models for its applicability on NPP systems

Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm

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