The Binomial Sensitivity Factor Hyper-Geometric Distribution SoftwareReliability Growth Model for Imperfect Debugging Environment 


Vol. 7,  No. 4, pp. 1103-1111, Apr.  2000
10.3745/KIPSTE.2000.7.4.1103


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  Abstract

The hyper-geometric distribution software reliability growth model (HGDM) usually assumes that all the software faults detected are perfectly removed without introducing new faults. However, since new faults can be introduced during the test-and-debug phase, the perfect debugging assumption should be relaxed. In this context, Hou, Kuo and Chang [7] developed a modified HGDM for imperfect debugging environment, assuming that the learning factor is constant. In this paper we extend the existing imperfect debugging HGDM for two respects introduction of random sensitivity factor and allowance of variable learning factor. Then the statistical characteristics of the suggested model are studied and its applications to two real data sets are demonstrated.

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[IEEE Style]

S. H. Kim, J. Y. Park, J. H. Park, "The Binomial Sensitivity Factor Hyper-Geometric Distribution SoftwareReliability Growth Model for Imperfect Debugging Environment," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 7, no. 4, pp. 1103-1111, 2000. DOI: 10.3745/KIPSTE.2000.7.4.1103.

[ACM Style]

Seong Hee Kim, Joong Yang Park, and Jae Heung Park. 2000. The Binomial Sensitivity Factor Hyper-Geometric Distribution SoftwareReliability Growth Model for Imperfect Debugging Environment. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 7, 4, (2000), 1103-1111. DOI: 10.3745/KIPSTE.2000.7.4.1103.

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