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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...

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Publicado en:Computer (00189162) Vol. 49; no. 1; pp. 52 - 59
Autores principales: Singh, Lalit Kumar, Vinod, Gopika, Tripathi, A.K.
Formato: Case Study
Publicado: IEEE Jan2016
Materias:
Nuclear power plants
Software reliability
Computer software development
Markov processes
Probability theory
Computer software
Acceso en línea:Ver este registro en EBSCOhost
Sumario: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.