Burr 커버리지 함수에 기초한 ENHPP소프트웨어 신뢰성장모형에 관한 연구

다국어 초록 (Multilingual Abstract)

Accurate predictions of software release times, and estimation of the reliability and availability of a software product require quantification of a critical element of the software testing process : test coverage. This model called Enhanced non-homogeneous poission process(ENHPP). In this paper, exponential coverage and S-shaped model was reviewed, proposes the Kappa coverage model, which maked out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on SSE statistics and Kolmogorov distance, for the sake of efficient model, was employed. From the analysis of mission time, the result of this comparative study shows the excellent performance of Burr coverage model rather than exponential coverage and S-shaped model using NTDS data. This analysis of failure data compared with the Kappa coverage model and the existing model(using arithmetic and Laplace trend tests, bias tests) is presented.

참고문헌 (Reference)

1 김희철, "지수화 지수 분포에 의존한 NHPP 소프트웨어 신뢰성장 모형에 관한 연구" 한국컴퓨터정보학회 11 (11): 8-18, 2006

2 김희철, "일반화 감마 분포를 이용한 NHPP소프트웨어 신뢰도 모형에 관한 연구" 한국컴퓨터정보학회 10 (10): 27-36, 2005

3 S. S. Gokhale, "Unification of Finite Failure Non-Homogeneous Poisson Process Models through Test Coverage" 1996

4 A. L. Goel, "Time-Dependent Error-Detection Rate Models for Software Reliability and Other Performance Measures" 28 (28): 206-211, 1979

5 R. Jacoby, "Test Coverage Dependant Software Reliability Estimation by the HGD Model" 1992

6 S. D. Dubey, "Statistical treatment of certain life testing and reliability problems" 73-0155, 1973

7 Lawless, J. F, "Statistical Models and Methods for Lifetime Data" John Wiley & Sons 1981

8 Yamada, S., "S-Shaped Reliability Growth Modeling for Software Error Detection" 32 (32): 475-485, 1983

9 S. Selvin, "Moden Applied Biostatistical Methods Using S-Plus" Oxford University Press 141-184, 1998

10 S. Brocklehurst, "Handbook of Software Reliability Engineering, In chapter Techniques for Prediction Analysis and Recalibration" 119-166,

11 K. Kanoun, "Handbook of Software Reliability Engineering" Trend Analysis 401-437,

12 I. W. Burr, "Cumulative frequency functions" 13 : 215-232, 1942

13 J, A. Austin, "Control chart constants for largest and smallest in sampling from a normal distribution using the generalized Burrestimation" 15 : 931-933, 1971

14 김희철, "Burr 분포를 이용한 NHPP소프트웨어 신뢰성장모형에 관한 연구" 한국정보통신학회 11 (11): 514-522, 2007

15 S. K. Upadhyay, "Bayesian analysis of generalized four-parameter Burr destribution via Gibbs sampler" LXII (LXII): 115-135, 2004

16 Kuo, L., "Bayesian Computation of Software Reliability" 91 : 763-773, 1996

17 P. K. Tadikamamlla, "Alook at the Burr and related distributions" 48 : 337-344, 1980

18 J.R. Horgan, "Achieving Software Quality with Testing Coverage Measure" 60-69, 1994

19 S. S. Gokhale, "A time/ structure based software reliability model" 8 : 85-121, 1999

20 H. Pham, "A General Imperfect-Software-Debugging Model with S-Shaped Fault-Detection Rate" 48 (48): 169-175, 1999

21 T. A., Mazzuchi, "A Bayes Empirical Bayes Model for Software Reliability" 37 : 248-254, 1988