n-Steps ahead software reliability prediction usin... - BV FAPESP
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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

n-Steps ahead software reliability prediction using the Kalman filter

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Author(s):
Ursini, Edson L. [1] ; Martins, Paulo S. [1] ; Moraes, Regina L. [1] ; Timoteo, Varese S. [1]
Total Authors: 4
Affiliation:
[1] Univ Estadual Campinas, UNICAMP, Sch Technol, BR-13484332 Limeira, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: Applied Mathematics and Computation; v. 245, p. 116-134, OCT 15 2014.
Web of Science Citations: 4
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. (C) 2014 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 13/17823-0 - Fault classification in mobile cloud computing
Grantee:Regina Lúcia de Oliveira Moraes
Support Opportunities: Regular Research Grants
FAPESP's process: 11/17339-5 - Methodology for analytical, simulation and emulation models to dimensioning IP VPn (Internet protocol - Virtual private networks)
Grantee:Edson Luiz Ursini
Support Opportunities: Regular Research Grants