Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/87559
Type: Artigo de periódico
Title: N-steps Ahead Software Reliability Prediction Using The Kalman Filter
Author: Ursini E.L.
Martins P.S.
Moraes R.L.
Timoteo V.S.
Abstract: This paper presents KSL, a new software reliability growth model (SRGM) based on the Kalman filter with a sub filter and the Laplace trend test. We applied the model to the Linux operating system kernel as a case study to predict the absolute and relative (per lines of code) number of faults n-steps ahead. The Laplace trend test is applied to detect when the series no longer follows a homogeneous Poisson process, improving the confidence level. An example is provided with a prediction of 13 months ahead on the number of faults with 8% error. The results (i.e. predictive capability) indicated that the proposed approach outperforms the S-shaped prediction model, Weibull, and Exponentiated Weibull distributions, as well as typical and OS-ELM Neural networks when the series has a short number of observations. © 2014 Elsevier Inc. All rights reserved.
Editor: Elsevier Inc.
Rights: fechado
Identifier DOI: 10.1016/j.amc.2014.07.018
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84906093805&partnerID=40&md5=a0e0ae2204b128d953264f15044118ef
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

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