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Early Prediction of Software Reliability: A Case Study with a Nuclear Power Plant System
| dc.contributor.author | Singh L.K.; Vinod G.; Tripathi A.K. | |
| dc.date.accessioned | 2025-05-24T09:27:21Z | |
| dc.description.abstract | 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. © 2016 IEEE. | |
| dc.identifier.doi | https://doi.org/10.1109/MC.2016.15 | |
| dc.identifier.uri | http://172.23.0.11:4000/handle/123456789/16129 | |
| dc.relation.ispartofseries | Computer | |
| dc.title | Early Prediction of Software Reliability: A Case Study with a Nuclear Power Plant System |