V&amp;V-based remaining fault estimation model for safety–critical software of a nuclear power plant

Eom, Heung-Seop; Park, Geeyong; Jang, Seung-Cheol; Son, Han Seong; Kang, Hyun Gook

doi:10.1016/j.anucene.2012.06.030

Cited by 25 publications

(8 citation statements)

References 13 publications

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“…BBN is a directed acyclic graphical network representing a set of variables and their conditional dependencies. BBN-based software reliability assessment method was discussed in [4][5][6]. BBN was employed to estimate the number of residual faults in reactor protection system (RPS) software by evaluating software development life cycle (SDLC) activities [6].…”

Section: Introductionmentioning

confidence: 99%

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Reactor protection system software test-case selection based on input-profile considering concurrent events and uncertainties

Khalaquzzaman

Lee

Cho

et al. 2015

Journal of Nuclear Science and Technology

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Recently, the input-profile-based testing for safety critical software has been proposed for determining the number of test cases and quantifying the failure probability of the software. Input-profile of a reactor protection system (RPS) software is the input which causes activation of the system for emergency shutdown of a reactor. This paper presents a method to determine the input-profile of a RPS software which considers concurrent events/transients. A deviation of a process parameter value begins through an event and increases owing to the concurrent multi-events depending on the correlation of process parameters and severity of incidents. A case of reactor trip caused by feedwater loss and main steam line break is simulated and analyzed to determine the RPS software input-profile and estimate the number of test cases. The different sizes of the main steam line breaks (e.g., small, medium, large break) with total loss of feedwater supply are considered in constructing the input-profile. The uncertainties of the simulation related to the input-profile-based software testing are also included. Our study is expected to provide an option to determine test cases and quantification of RPS software failure probability.

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Section: Introductionmentioning

confidence: 99%

“…BBN-based software reliability assessment method was discussed in [4][5][6]. BBN was employed to estimate the number of residual faults in reactor protection system (RPS) software by evaluating software development life cycle (SDLC) activities [6]. However, the method of converting the number of software faults into failure is unresolved.…”

Section: Introductionmentioning

confidence: 99%

Reactor protection system software test-case selection based on input-profile considering concurrent events and uncertainties

Khalaquzzaman

Lee

Cho

et al. 2015

Journal of Nuclear Science and Technology

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“…Since the 1970s, researchers are continuing to propose software reliability models for reliability estimation of software. However, the method neither considers the SDLC process nor a mechanism to deal with insufficiency of data.Eom et al 10 proposed a verification and validation-based fault estimation method using Bayesian network to estimate the remaining faults for SCS after the SDLC is completed. Due to insufficient failure data, these models fail to predict the reliability of safety critical systems.…”

mentioning

confidence: 99%

“…Eom et al 10 proposed a verification and validation-based fault estimation method using Bayesian network to estimate the remaining faults for SCS after the SDLC is completed. Moreover, estimating remaining fault is only a reliability metric.…”

mentioning

confidence: 99%

“…The proposed method is capable to consider operational profile that can be produced based on process parameters. However, the method neither considers the SDLC process nor a mechanism to deal with insufficiency of data.Eom et al 10 proposed a verification and validation-based fault estimation method using Bayesian network to estimate the remaining faults for SCS after the SDLC is completed. Moreover, estimating remaining fault is only a reliability metric.…”

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confidence: 99%

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Software reliability analysis for safety‐critical and control systems

Kumar

Singh

Kumar

2019

Quality & Reliability Eng

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The transition from analog to digital safety-critical instrumentation and control (I&C) systems has introduced new challenges for software experts to deliver increased software reliability. Since the 1970s, researchers are continuing to propose software reliability models for reliability estimation of software. However, these approaches rely on the failure history for the assessment of reliability. Due to insufficient failure data, these models fail to predict the reliability of safety critical systems. This paper utilizes the Bayesian update methodology and proposes a framework for the reliability assessment of the safety-critical systems (SCSs). The proposed methodology is validated using experiments performed on real data of 12 safety-critical control systems of nuclear power plants.We believe that SRM for SCS must have some special desirable characteristics:1. There should be acceptable documentation of the method and its applications that can be understood and evaluated. 2. There should be a strong justification of the assumptions made. 3. It should be possible to consider the specific operating conditions of the software. 4. It should be able to address all the uncertainties for better accuracy. 5. There should be sufficiency of model parameters. 6. The models should have proper verification and validation ways.Several models do not have description of intended uses of the method and all assumptions. 6,7 In general, the existing SRM assumes unrealistic assumptions and use failure history to estimate the parameters of the models in order to calculate the reliability. As of our knowledge, we have found very few SRM which consider the specific operating conditions of the software. 8 Software reliability growth model (SRGM) estimates the software reliability with high accuracy, if there is sufficiency of failure data and therefore are not suitable to SCS.Software errors are generally due to unstated, ambiguous, and unrealistic requirements or because of design errors. From this, we believe that if it is possible to formulate any model that can accommodate errors in each phase of software development life cycle (SDLC) and also can propagate them in next phase, prior to the completion of software development, it will give more accurate reliability prediction results. A perfect designed software requirement and specification (SRS) not only depends on the requirements and analysis but also on the expert's opinion, the domain knowledge, and the experience of the SRS designing team, the resources available, and many other constraints. Taking these as the inputs for the requirements phase, a perfect SRS is designed. These inputs may be considered as "safety cases" or "safety arguments" as these are the collection of evidence responsible for the proper development of final product. Evidence for safety cases or safety arguments for each phase exists. Using these evidences, we can create BBN model for each phase of life cycle model and calculate the inferred probability using the safety argument values. A proper conditio...

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Systems Important for NPP Safety: Software Verification and Cybersecurity

Jharko

2021

Lecture Notes in Electrical Engineering

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V&V-based remaining fault estimation model for safety–critical software of a nuclear power plant

Cited by 25 publications

References 13 publications

Reactor protection system software test-case selection based on input-profile considering concurrent events and uncertainties

Reactor protection system software test-case selection based on input-profile considering concurrent events and uncertainties

Software reliability analysis for safety‐critical and control systems

Systems Important for NPP Safety: Software Verification and Cybersecurity

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