Input-profile-based software failure probability quantification for safety signal generation systems

Kang, Hyun Gook; Lim, Ho Gon; Lee, Ho Jung; Kim, Man Cheol; Jang, Seung-Cheol

doi:10.1016/j.ress.2009.02.018

Cited by 16 publications

(16 citation statements)

References 5 publications

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“…Bayesian model has been used to infer the reliability at the next phase, given the reliability at the previous phase and using knowledge of required resources and expert's judgment. The reliability computed using Bayes' theorem is 0.972, as given in equation (9). This reliability of the DFWCS is estimated using operational profile data given in Table 5 using Brown and Lipow input domain model 25 and is called the likelihood (L).…”

Section: Results and Validationmentioning

confidence: 99%

“…However, this model is based on the observable states of the system and there is a possibility to miss out the undesirable scenario that may happen in the future. Hence, devising a model that can embed the development characteristics of the software will give more accurate results.Kang et al 9 proposed an input profile-based software testing method for the quantification of the failure probability using Binomial distribution and Bayesian approach. The proposed method is capable to consider operational profile that can be produced based on process parameters.…”

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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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Section: Results and Validationmentioning

confidence: 99%

mentioning

confidence: 99%

Software reliability analysis for safety‐critical and control systems

Kumar

Singh

Kumar

2019

Quality & Reliability Eng

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“…Sohn and Seong [12] insist that there are no unique criteria to prove whether enough testing has been performed even though various testing methods exist. However, if we assume the randomness of test cases and the even distribution of the usage profile, the calculation method for the required number of tests can be derived by using conventional statistics [13].…”

Section: Input-domain-based Test (Idbt) For Sfp Quantificationmentioning

confidence: 99%

“…More detailed explanations for the above calculations are available in reference [13]. Software testing is classified as either structural testing or functional testing [12].…”

Section: Input-domain-based Test (Idbt) For Sfp Quantificationmentioning

confidence: 99%

“…One more important characteristic is that we do not have to repeat the test for the same input value since the software response is deterministic for each specific digital input. In consideration of these characteristics of software, Kang et al [13] proposed an input-profile-based SFP quantification method.…”

Section: Input-domain-based Test (Idbt) For Sfp Quantificationmentioning

confidence: 99%

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Software Failure Probability Quantification for System Risk Assessment

Kang

Eom

Son

2009

Scholarly Research Exchange

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Risk caused by safety-critical I&C systems considerably affects overall plant risk. Software failures in digitalized I&C systems must be considered as the cause of risk. As digitalization of safety-critical systems progresses, the need for software failure probability quantification increases. For the software of safety-critical systems, very high reliability is required. This article aims at providing an overview of promising software failure probability quantification models for this kind of safety-critical system: The software reliability growth model (SRGM), the input-domain-based test model (IDBT), and the validation/verification quality model (VVQM). In order to accommodate the characteristics of safety-critical systems, a more effective framework of practical risk assessment applications is necessary. In this article, we propose the combined use of SRGM&VVQM for a more systematic and traceable method of the failure probability quantification of safety-critical software.

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TEF Dependent Software FDP and FCP Models

Peng

Liu

2018

Software Fault Detection and Correction: Modeling and Applications

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Input-profile-based software failure probability quantification for safety signal generation systems

Cited by 16 publications

References 5 publications

Software reliability analysis for safety‐critical and control systems

Software reliability analysis for safety‐critical and control systems

Software Failure Probability Quantification for System Risk Assessment

TEF Dependent Software FDP and FCP Models

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