Early Prediction of Software Reliability: A Case Study with a Nuclear Power Plant System.
Existing methods to predict software reliability using the Markov chain are based on assumed state-transition probabilities. A new prediction approach applied to a nuclear plant's feed-water system yielded results that were 96.9 percent accurate relative to the system's actual reliability. Across 38...
| Publicado en: | Computer (00189162) Vol. 49; no. 1; pp. 52 - 59 |
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| Autores principales: | , , |
| Formato: | Case Study |
| Publicado: |
IEEE
Jan2016
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=112441812&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 112441812 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00189162 PUT jtl: Computer (00189162) issn: 00189162 maglogo: N pubinfo: dt: Jan2016 vid: 49 iid: 1 pid: 13605 pub: IEEE artinfo: ui: 112441812 10.1109/MC.2016.15 ppf: 52 ppct: 7 formats: tig: atl: Early Prediction of Software Reliability: A Case Study with a Nuclear Power Plant System. aug: au: Singh, Lalit Kumar Vinod, Gopika Tripathi, A.K. affil: Indian Institute of Technology (Banaras Hindu University) Bhabha Atomic Research Centre su: Nuclear power plants Software reliability Computer software development Markov processes Probability theory Computer software sug: subj: Nuclear power plants Software reliability Computer software development Markov processes Probability theory Computer software keyword: Markov chain Nuclear power generation nuclear reactor control Nuclear reactors Petri nets Predictive models real-time control systems Real-time systems safety-critical systems software engineering software reliability software reliability prediction ab: Existing methods to predict software reliability using the Markov chain are based on assumed state-transition probabilities. A new prediction approach applied to a nuclear plant's feed-water system yielded results that were 96.9 percent accurate relative to the system's actual reliability. Across 38 operational datasets, the average accuracy was 99.67 percent. pubtype: Academic Journal doctype: Case Study src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2016 holdings: @attributes: islocal: N |
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