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Assessing Software Designs Using Capture-Recapture Methods

Authors:

Scott A. Vander Wiel and

Lawrence G. VottaAuthors Info & Claims

IEEE Transactions on Software Engineering, Volume 19, Issue 11

Pages 1045 - 1054

https://doi.org/10.1109/32.256852

Published: 01 November 1993 Publication History

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Abstract

The number of faults not discovered by the design review can be estimated by using capture-recapture methods. Since these methods were developed for wildlife population estimation, the assumptions used to derive them do not match design review applications. The authors report on a Monte Carlo simulation to study the effects of broken assumptions on maximum likelihood estimators (MLEs) and jackknife estimators (JEs) of faults remaining. It is found that the MLE performs satisfactorily if faults are classified into a small number of homogeneous groups. Without grouping, the MLE can perform poorly, but it generally does better than the JE.

References

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{2} K. P. Burnham and W. S. Overton, "Estimation of the size of a closed population when capture probabilities vary among animals," Biometrika, vol. 65, pp. 625-633, 1978.

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[3]

{3} S. G. Eick, C. R. Loader, M. D. Long, S. A. Vander Wiel, and L. G. Votta, "Estimating software fault content before coding, in Proc. 14th Int. Conf. Software Eng., pp. 59-65, May 1992.

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{4} S. G. Eick, C. R. Loader, M. D. Long, S. A. Vander Wiel, and L. G. Votta, "Capture-recapture and other statistical methods for software inspection data," in Computing Science and Statistics: Proc. 25 Symp. Interface, 1993, to be published.

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{5} W. S. Humphery, Managing the Software Process. Reading, MA.: Addison-Wesley Publishing Co., 1989.

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{6} G. A. Miller, "The magic number seven, plus or minus two: Some limits on our capacity for processing information," Psychol. Rev., vol. 63, pp. 81-97, 1956.

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{7} D. L. Otis, K. P. Burnham, G. C. White, and D. R. Anderson, Statistical Inference from Capture Data on Closed Animal Populations (Wildlife Monographs). Bethesda, MD: The Wildlife Society, 1978.

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{8} G. C. White, D. R. Anderson, K. P. Burnham, and D. L. Otis, "Capture-recapture and removal methods for sampling closed populations," Tech. Rep., Los Alamos National Lab., Los Alamos, NM, 1982.

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Walia GCarver JRombach DElbaum SMünch J(2008)Evaluation of capture-recapture models for estimating the abundance of naturally-occurring defectsProceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement10.1145/1414004.1414031(158-167)Online publication date: 9-Oct-2008
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Walia GCarver JNagappan NSchäfer WDwyer MGruhn V(2008)The effect of the number of inspectors on the defect estimates produced by capture-recapture modelsProceedings of the 30th international conference on Software engineering10.1145/1368088.1368134(331-340)Online publication date: 15-May-2008
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cover image IEEE Transactions on Software Engineering

IEEE Transactions on Software Engineering Volume 19, Issue 11

Special issue on software reliability

November 1993

111 pages

ISSN:0098-5589

Editor:
Nancy G. Leveson
Univ. Washington, Seattle

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IEEE Press

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Published: 01 November 1993

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Leverett ÉRhode MWedgbury A(2022)Vulnerability Forecasting: Theory and PracticeDigital Threats: Research and Practice10.1145/34923283:4(1-27)Online publication date: 25-Mar-2022
https://dl.acm.org/doi/10.1145/3492328
Walia GCarver JRombach DElbaum SMünch J(2008)Evaluation of capture-recapture models for estimating the abundance of naturally-occurring defectsProceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement10.1145/1414004.1414031(158-167)Online publication date: 9-Oct-2008
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Walia GCarver JNagappan NSchäfer WDwyer MGruhn V(2008)The effect of the number of inspectors on the defect estimates produced by capture-recapture modelsProceedings of the 30th international conference on Software engineering10.1145/1368088.1368134(331-340)Online publication date: 15-May-2008
https://dl.acm.org/doi/10.1145/1368088.1368134
Elbaum SKanduri SAndrews A(2007)Trace anomalies as precursors of field failuresEmpirical Software Engineering10.1007/s10664-007-9042-812:5(447-469)Online publication date: 1-Oct-2007
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Song QShepperd MCartwright MMair C(2006)Software Defect Association Mining and Defect Correction Effort PredictionIEEE Transactions on Software Engineering10.1109/TSE.2006.159941732:2(69-82)Online publication date: 1-Feb-2006
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Padberg FRagg TSchoknecht R(2004)Using Machine Learning for Estimating the Defect Content After an InspectionIEEE Transactions on Software Engineering10.1109/TSE.2004.126573330:1(17-28)Online publication date: 1-Jan-2004
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Ragg TPadberg FSchoknecht R(2002)Applying Machine Learning to Solve an Estimation Problem in Software InspectionsProceedings of the International Conference on Artificial Neural Networks10.5555/646259.684311(516-521)Online publication date: 28-Aug-2002
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Padberg FTracz WMagee JYoung M(2002)Empirical interval estimates for the defect content after an inspectionProceedings of the 24th International Conference on Software Engineering10.1145/581339.581350(58-68)Online publication date: 19-May-2002
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Stringfellow CAndrews A(2002)An Empirical Method for Selecting Software Reliability Growth ModelsEmpirical Software Engineering10.1023/A:10205151051757:4(319-343)Online publication date: 1-Dec-2002
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Miller JMacdonald FFerguson J(2002)ASSISTing Management Decisions in the Software Inspection ProcessInformation Technology and Management10.1023/A:10131128263303:1-2(67-83)Online publication date: 1-Jan-2002
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