Reliability analysis of Digital Instrumentation and Control software system

Zou, Bowen; Yang, Ming; Benjamin, Emi-Reynolds; Yoshikawa, Hidekazu

doi:10.1016/j.pnucene.2017.03.006

Cited by 13 publications

(10 citation statements)

References 12 publications

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“…In mapping methods, the structures of the BNs are translated from other software reliability evaluation models or from the logic of the software. For example, Zou et al [73] integrated a flow network model, BNs, and the proposed contribution method to evaluate the reliability of a digital instrumentation and control software system; they analyzed the sensitive edges by mapping the flow network model into the BNs automatically. Roshandel et al [74] applied a DBNbased software reliability prediction approach at the architectural level, constructed the structure of the BNs from the corresponding global behavioral model, and then extended the model to the DBNs; however, they did not perform a strict model transformation.…”

Section: Software Reliability Evaluation With Bnsmentioning

confidence: 99%

Application of Bayesian Networks in Reliability Evaluation

Cai

Kong

et al. 2019

IEEE Trans. Ind. Inf.

192

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The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.

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Section: Software Reliability Evaluation With Bnsmentioning

confidence: 99%

Application of Bayesian Networks in Reliability Evaluation

Cai

Kong

et al. 2019

IEEE Trans. Ind. Inf.

192

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“…The authors performed the sensitivity analysis in order to determine the algorithm's capability for detecting the outliers. Zou et al performed the sensitivity analysis of an integrated software reliability analysis platform in order to determine the location of the most sensitive code blocks in the system [18]. The authors in [10] performed the sensitivity analysis of different quality indicators in the software module clustering process.…”

Section: Related Workmentioning

confidence: 99%

Sensitivity Analysis of Evolutionary Algorithm for Software Reusability

Rathee

Chhabra

2019

mendel

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Fast and competitive software industry demands rapid development using Component Based Software Development (CBSD). CBSD is dependent on the availability of the high-quality reusable component libraries. Recently, evolutionary multi-objective optimization algorithms have been used to identify sets of reusable software components from the source-code of Object Oriented (OO) software, using different quality indicators (e.g. cohesion, coupling, etc.). Sometimes, these used quality indicators are quite sensitive towards the small variations in their values, although they should not be. Therefore, this paper analyzes the sensitivity of the evolutionary technique for three quality indicators used during the identification: Frequent Usage Pattern (FUP), Semantic and evolutionary coupling. The sensitivity analysis is performed on three widely used open-source OO software. The experimentation is performed by mutating the system to different degrees. Results of the empirical analysis indicate that the semantic parameter is most sensitive and important. Ignoring this feature highly degrades the quality; FUP relation is uniformly sensitive and evolutionary relations's sensitivity is non-uniform.

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“…The model does not consider the expert's knowledge in the earlier stages of the life cycle model. Zou et al 23 developed a software platform known as Software Reliability Auto-Modeling and Analysis Platform. The proposed technique is based on flow network model, which is hard to create for lengthy programs.…”

Section: Introductionmentioning

confidence: 99%

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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Reliability analysis of Digital Instrumentation and Control software system

Cited by 13 publications

References 12 publications

Application of Bayesian Networks in Reliability Evaluation

Application of Bayesian Networks in Reliability Evaluation

Sensitivity Analysis of Evolutionary Algorithm for Software Reusability

Software reliability analysis for safety‐critical and control systems

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