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- W54593981 abstract "A considerable number of reliability growth models has been proposed to evaluate software dependability. Each of them provides an inference rule from past failure data on the present and future behaviour; they are based on different assumptions, some of which are only realistic for particular application classes and unprovable at present. Also for this reason none of the models has ever shown to be the absolutely best: its accuracy varies from an application to another and cannot be predicted a priori. Although it may be easy to identify the worst models in a given case by means of absolute quality measures quantifying the departure of prediction from reality, the identification of the best one is particularly difficult, as the goodness of fit values may be time-variant. Thus even an experienced model user may encounter considerable difficulties in making up his mind when selecting the most promising modelization for the specific situation considered. In order to simplify the decision phase, allowing the use of evaluating tools also to appliers non-familiar with the underlying modelling theories, we suggest to automatize the model choice, by selecting deterministically the estimation value considered to be most reliable. The intention of this contribution is to study two particular strategies integrating different predictions, which have been suggested in literature, in particular with respect to their sensitivity to model-behavioural changes, identifying the most critical and most suitable situations to be treated by each of them." @default.
- W54593981 created "2016-06-24" @default.
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- W54593981 date "1992-10-01" @default.
- W54593981 modified "2023-10-18" @default.
- W54593981 title "Integration of Software Reliability Predictions to Achieve Modelling Fault Tolerance" @default.
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- W54593981 doi "https://doi.org/10.1016/s1474-6670(17)49417-1" @default.
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