Software Failure Time Data Analysis via Wavelet-Based Approach

Xiao XIAO; Tadashi DOHI

doi:10.1587/transfun.E95.A.1490

Software Failure Time Data Analysis via Wavelet-Based Approach

Xiao XIAO, Tadashi DOHI

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The non-homogeneous Poisson process (NHPP) has been applied successfully to model nonstationary counting phenomena for a large class of problems. In software reliability engineering, the NHPP-based software reliability models (SRMs) are of a very important class. Since NHPP is characterized by its rate (intensity) function, which is known as the software failure rate of NHPP-based SRM, it is of great interest to estimate accurately the rate function from observed software failure data. In the existing work the same authors introduced a Haar-wavelet-based technique for this problem and found that the Haar wavelet transform provided a very powerful performance in estimating software failure rate. In this paper, we consider the application potentiality of a Daubechies wavelet estimator in the estimation of software failure rate, given the software failure time data. We give practical solutions by overcoming technical difficulties in applying the Daubechies wavelet estimator to the real software failure time data.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E95-A No.9 pp.1490-1497

Publication Date: 2012/09/01

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Online ISSN: 1745-1337

DOI: 10.1587/transfun.E95.A.1490

Type of Manuscript: Special Section PAPER (Special Section on Software Reliability Engineering)

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Xiao XIAO, Tadashi DOHI, "Software Failure Time Data Analysis via Wavelet-Based Approach" in IEICE TRANSACTIONS on Fundamentals, vol. E95-A, no. 9, pp. 1490-1497, September 2012, doi: 10.1587/transfun.E95.A.1490.
Abstract: The non-homogeneous Poisson process (NHPP) has been applied successfully to model nonstationary counting phenomena for a large class of problems. In software reliability engineering, the NHPP-based software reliability models (SRMs) are of a very important class. Since NHPP is characterized by its rate (intensity) function, which is known as the software failure rate of NHPP-based SRM, it is of great interest to estimate accurately the rate function from observed software failure data. In the existing work the same authors introduced a Haar-wavelet-based technique for this problem and found that the Haar wavelet transform provided a very powerful performance in estimating software failure rate. In this paper, we consider the application potentiality of a Daubechies wavelet estimator in the estimation of software failure rate, given the software failure time data. We give practical solutions by overcoming technical difficulties in applying the Daubechies wavelet estimator to the real software failure time data.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E95.A.1490/_p

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@ARTICLE{e95-a_9_1490,
author={Xiao XIAO, Tadashi DOHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Software Failure Time Data Analysis via Wavelet-Based Approach},
year={2012},
volume={E95-A},
number={9},
pages={1490-1497},
abstract={The non-homogeneous Poisson process (NHPP) has been applied successfully to model nonstationary counting phenomena for a large class of problems. In software reliability engineering, the NHPP-based software reliability models (SRMs) are of a very important class. Since NHPP is characterized by its rate (intensity) function, which is known as the software failure rate of NHPP-based SRM, it is of great interest to estimate accurately the rate function from observed software failure data. In the existing work the same authors introduced a Haar-wavelet-based technique for this problem and found that the Haar wavelet transform provided a very powerful performance in estimating software failure rate. In this paper, we consider the application potentiality of a Daubechies wavelet estimator in the estimation of software failure rate, given the software failure time data. We give practical solutions by overcoming technical difficulties in applying the Daubechies wavelet estimator to the real software failure time data.},
keywords={},
doi={10.1587/transfun.E95.A.1490},
ISSN={1745-1337},
month={September},}

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TY - JOUR
TI - Software Failure Time Data Analysis via Wavelet-Based Approach
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1490
EP - 1497
AU - Xiao XIAO
AU - Tadashi DOHI
PY - 2012
DO - 10.1587/transfun.E95.A.1490
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E95-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2012
AB - The non-homogeneous Poisson process (NHPP) has been applied successfully to model nonstationary counting phenomena for a large class of problems. In software reliability engineering, the NHPP-based software reliability models (SRMs) are of a very important class. Since NHPP is characterized by its rate (intensity) function, which is known as the software failure rate of NHPP-based SRM, it is of great interest to estimate accurately the rate function from observed software failure data. In the existing work the same authors introduced a Haar-wavelet-based technique for this problem and found that the Haar wavelet transform provided a very powerful performance in estimating software failure rate. In this paper, we consider the application potentiality of a Daubechies wavelet estimator in the estimation of software failure rate, given the software failure time data. We give practical solutions by overcoming technical difficulties in applying the Daubechies wavelet estimator to the real software failure time data.
ER -