Abstract
Reliability Growth is a modeling process for product quality characterization over the lifespan for both hardware and software products and has been explained by multiple models like Duane, Crow-AMSAA, Lloyd Lipow etc. Our research proposes a framework for case-based/scenario based model estimation and prediction, by supervised learning of historical data. In this proposed framework, the case base is generated from historical data and Crow Model is applied in a novel sense to extract information from the historically labeled occurrences. With our framework, we draw in a comparative advantage over the traditional predictive modeling using a Crow’s Growth Model.
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References
Crow, L. H. (1983). “AMSAA discrete reliability growth model”, US army material system analysis activity (AMSAA). Technical report no. 357, Aberdeen proving ground
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© 2013 Springer Science+Business Media Singapore
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Bhat, V., Mishra, R., Sundarakrishna, S., Chakraborty, A. (2013). Classification Based Reliability Growth Prediction on Data Generated by Multiple Independent Processes. In: Mandal, P. (eds) Proceedings of the International Conference on Managing the Asian Century. Springer, Singapore. https://doi.org/10.1007/978-981-4560-61-0_48
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DOI: https://doi.org/10.1007/978-981-4560-61-0_48
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Publisher Name: Springer, Singapore
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Online ISBN: 978-981-4560-61-0
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