n-Steps ahead software reliability prediction using the Kalman filter
Edson L. Ursini, Paulo S. Martins, Regina L. Moraes, Varese S. Timóteo
ARTIGO
Inglês
Agradecimentos: The authors would like to thank FAPESP 2011/17339-5, FAPESP 2013/17823-0 and PAPDIC #1198/12 grants. We would like to thank the editor and referees for their valuable comments and suggestions that improved the paper
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...
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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
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FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP
2011/17339-5; 2013/17823-0
Fechado
n-Steps ahead software reliability prediction using the Kalman filter
Edson L. Ursini, Paulo S. Martins, Regina L. Moraes, Varese S. Timóteo
n-Steps ahead software reliability prediction using the Kalman filter
Edson L. Ursini, Paulo S. Martins, Regina L. Moraes, Varese S. Timóteo
Fontes
Applied mathematics and computation (Fonte avulsa) |