Bayesian analysis for NHPP-based software fault detection and correction processes

L. J. Wang; Q. P. Hu; M. Xie

doi:10.1109/IEEM.2015.7385808

Bayesian analysis for NHPP-based software fault detection and correction processes

L. J. Wang, Q. P. Hu, M. Xie

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

Many software reliability methods have been proposed for estimating and predicting reliability, and the method based on Bayesian framework is a popular one. However, the present Bayesian approaches are all based on the impractical assumption that the detected faults are corrected immediately with no debugging time delay. In this paper, we derive effective parameter estimation algorithms based on Bayesian framework not only for fault detection process, but also for combined detection and correction processes. To have a better understanding of the estimation performance, a simulation study and a practical example are conducted to investigate the performance of the proposed Bayesian approach.

Original language	English
Title of host publication	IEEE International Conference on Industrial Engineering and Engineering Management
Publisher	IEEE Computer Society
Pages	1046-1050
Volume	2016-January
ISBN (Print)	9781467380669
DOIs	https://doi.org/10.1109/IEEM.2015.7385808
Publication status	Published - 18 Jan 2016
Event	2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2015) - , Singapore Duration: 6 Dec 2015 → 9 Dec 2015 http://www.IEEM.org

Publication series

Name
Volume	2016-January
ISSN (Print)	2157-3611
ISSN (Electronic)	2157-362X

Conference

Conference	2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2015)
Place	Singapore
Period	6/12/15 → 9/12/15
Internet address	http://www.IEEM.org

Research Keywords

Bayesian
NHPP
software reliability

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10.1109/IEEM.2015.7385808

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title = "Bayesian analysis for NHPP-based software fault detection and correction processes",

abstract = "Many software reliability methods have been proposed for estimating and predicting reliability, and the method based on Bayesian framework is a popular one. However, the present Bayesian approaches are all based on the impractical assumption that the detected faults are corrected immediately with no debugging time delay. In this paper, we derive effective parameter estimation algorithms based on Bayesian framework not only for fault detection process, but also for combined detection and correction processes. To have a better understanding of the estimation performance, a simulation study and a practical example are conducted to investigate the performance of the proposed Bayesian approach.",

keywords = "Bayesian, NHPP, software reliability",

author = "Wang, {L. J.} and Hu, {Q. P.} and M. Xie",

year = "2016",

month = jan,

day = "18",

doi = "10.1109/IEEM.2015.7385808",

language = "English",

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volume = "2016-January",

publisher = "IEEE Computer Society",

pages = "1046--1050",

booktitle = "IEEE International Conference on Industrial Engineering and Engineering Management",

address = "United States",

note = "2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2015) ; Conference date: 06-12-2015 Through 09-12-2015",

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Wang, LJ, Hu, QP & Xie, M 2016, Bayesian analysis for NHPP-based software fault detection and correction processes. in IEEE International Conference on Industrial Engineering and Engineering Management. vol. 2016-January, 7385808, IEEE Computer Society, pp. 1046-1050, 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2015), Singapore, 6/12/15. https://doi.org/10.1109/IEEM.2015.7385808

Bayesian analysis for NHPP-based software fault detection and correction processes. / Wang, L. J.; Hu, Q. P.; Xie, M.
IEEE International Conference on Industrial Engineering and Engineering Management. Vol. 2016-January IEEE Computer Society, 2016. p. 1046-1050 7385808.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Bayesian analysis for NHPP-based software fault detection and correction processes

AU - Wang, L. J.

AU - Hu, Q. P.

AU - Xie, M.

PY - 2016/1/18

Y1 - 2016/1/18

N2 - Many software reliability methods have been proposed for estimating and predicting reliability, and the method based on Bayesian framework is a popular one. However, the present Bayesian approaches are all based on the impractical assumption that the detected faults are corrected immediately with no debugging time delay. In this paper, we derive effective parameter estimation algorithms based on Bayesian framework not only for fault detection process, but also for combined detection and correction processes. To have a better understanding of the estimation performance, a simulation study and a practical example are conducted to investigate the performance of the proposed Bayesian approach.

AB - Many software reliability methods have been proposed for estimating and predicting reliability, and the method based on Bayesian framework is a popular one. However, the present Bayesian approaches are all based on the impractical assumption that the detected faults are corrected immediately with no debugging time delay. In this paper, we derive effective parameter estimation algorithms based on Bayesian framework not only for fault detection process, but also for combined detection and correction processes. To have a better understanding of the estimation performance, a simulation study and a practical example are conducted to investigate the performance of the proposed Bayesian approach.

KW - Bayesian

KW - NHPP

KW - software reliability

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84962013241&origin=recordpage

U2 - 10.1109/IEEM.2015.7385808

DO - 10.1109/IEEM.2015.7385808

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781467380669

VL - 2016-January

SP - 1046

EP - 1050

BT - IEEE International Conference on Industrial Engineering and Engineering Management

PB - IEEE Computer Society

T2 - 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2015)

Y2 - 6 December 2015 through 9 December 2015

ER -

Bayesian analysis for NHPP-based software fault detection and correction processes

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