Sensitivity-Analysis and Estimating Number-of-Faults in Removal Debugging

Paul S.F. Yip; Liquni Xi Zhejiang; Daniel Y.T. Fong; Y. Hayakawa

doi:10.1109/24.799902

Sensitivity-Analysis and Estimating Number-of-Faults in Removal Debugging

Paul S.F. Yip^*, Liquni Xi Zhejiang, Daniel Y.T. Fong, Y. Hayakawa

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

Summary & Conclusions -Estimating the number of faults in a computer program is important in software debugging. A martingale equation is used to estimate the number of faults in removal-debugging by assuming a known proportionality constant between the failure rate of a 'newly detected fault' and a 'seeded fault'. The sensitivity of the assumption is examined, and the results are generalized to allow an unknown proportionality. The information of the proportionality is shown to be crucial in the precision & availability of the estimates. It is advisable to obtain the information about the proportionality constant from external sources in order to improve the efficiency of the method in this paper.

Original language	English
Pages (from-to)	300-305
Number of pages	6
Journal	IEEE Transactions on Reliability
Volume	48
Issue number	3
DOIs	https://doi.org/10.1109/24.799902
Publication status	Published - 1999 Sept
Externally published	Yes

Keywords

Martingale estimating equation
Removal debugging
Seeded fault
Sensitivity analysis

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality
Electrical and Electronic Engineering

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10.1109/24.799902

Cite this

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title = "Sensitivity-Analysis and Estimating Number-of-Faults in Removal Debugging",

abstract = "Summary & Conclusions -Estimating the number of faults in a computer program is important in software debugging. A martingale equation is used to estimate the number of faults in removal-debugging by assuming a known proportionality constant between the failure rate of a 'newly detected fault' and a 'seeded fault'. The sensitivity of the assumption is examined, and the results are generalized to allow an unknown proportionality. The information of the proportionality is shown to be crucial in the precision & availability of the estimates. It is advisable to obtain the information about the proportionality constant from external sources in order to improve the efficiency of the method in this paper.",

keywords = "Martingale estimating equation, Removal debugging, Seeded fault, Sensitivity analysis",

author = "Yip, {Paul S.F.} and Zhejiang, {Liquni Xi} and Fong, {Daniel Y.T.} and Y. Hayakawa",

note = "Funding Information: We gratefully acknowledge the useful comments from the three referees. The work was supported by a RGC grant.",

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T1 - Sensitivity-Analysis and Estimating Number-of-Faults in Removal Debugging

AU - Yip, Paul S.F.

AU - Zhejiang, Liquni Xi

AU - Fong, Daniel Y.T.

AU - Hayakawa, Y.

N1 - Funding Information: We gratefully acknowledge the useful comments from the three referees. The work was supported by a RGC grant.

PY - 1999/9

Y1 - 1999/9

N2 - Summary & Conclusions -Estimating the number of faults in a computer program is important in software debugging. A martingale equation is used to estimate the number of faults in removal-debugging by assuming a known proportionality constant between the failure rate of a 'newly detected fault' and a 'seeded fault'. The sensitivity of the assumption is examined, and the results are generalized to allow an unknown proportionality. The information of the proportionality is shown to be crucial in the precision & availability of the estimates. It is advisable to obtain the information about the proportionality constant from external sources in order to improve the efficiency of the method in this paper.

AB - Summary & Conclusions -Estimating the number of faults in a computer program is important in software debugging. A martingale equation is used to estimate the number of faults in removal-debugging by assuming a known proportionality constant between the failure rate of a 'newly detected fault' and a 'seeded fault'. The sensitivity of the assumption is examined, and the results are generalized to allow an unknown proportionality. The information of the proportionality is shown to be crucial in the precision & availability of the estimates. It is advisable to obtain the information about the proportionality constant from external sources in order to improve the efficiency of the method in this paper.

KW - Martingale estimating equation

KW - Removal debugging

KW - Seeded fault

KW - Sensitivity analysis

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Sensitivity-Analysis and Estimating Number-of-Faults in Removal Debugging

Abstract

Keywords

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