×

Sequential Bayesian technique: an alternative approach for software reliability estimation. (English) Zbl 1192.68151

Summary: This paper proposes a sequential Bayesian approach similar to Kalman filter for estimating reliability growth or decay of software. The main advantage of proposed method is that it shows the variation of the parameter over a time, as new failure data become available. The usefulness of the method is demonstrated with some real life data.

MSC:

68N99 Theory of software

References:

[1] Chatterjee S, Misra R B, Alam S S 1997 Joint effect of test effort and learning factor on software reliability and optimal release policy. Int. J. Sys. Sci. 28(4): 391–396 · Zbl 0882.68044 · doi:10.1080/00207729708929399
[2] Chatterjee S, Misra R B, Alam S S 1998 A generalized shock model for software reliability. Comput. Elect. Eng.-An Int. J. 24: 363–368 · doi:10.1016/S0045-7906(98)00005-6
[3] Chatterjee S, Misra R B, Alam S S 2004 N-version programming with imperfect debugging. Comput. Elect. Eng. 30(6): 453–463 · Zbl 1076.68020 · doi:10.1016/S0045-7906(04)00025-4
[4] Dai Y S, Xie M, Poh K L 2005 Modelling and analysis of correlated software failures of multiple types. IEEE Trans. Rel. 54(1): 100–106 · doi:10.1109/TR.2004.841709
[5] Fakhre-Zakeri I, Slud E 1995 Mixture models for reliability of software with imperfect debugging: Identifiably of parameters. IEEE Trans. Rel. 44: 104–113 · doi:10.1109/24.376529
[6] Goel A L, Okumoto K 1979 A time-dependent error detection rate model for software reliability and other performance measure. IEEE Trans. Rel. R-28: 206–211 · Zbl 0409.68009 · doi:10.1109/TR.1979.5220566
[7] Gokhale S S, Lyu M R, Trivedi K S 2006 Incorporating fault debugging activities into software reliability models: A simulation approach. IEEE Trans. Rel. 55(2): 281–292 · doi:10.1109/TR.2006.874911
[8] Jelinski Z, Moranda P B 1972 Software reliability research statistical computer performance evaluation. W Freiberger, Ed. Academic, NY, 465–484
[9] Littlewood B, Verrall J L 1973 A bayesian reliability growth model for computer software. Appl. Statist. 22: 332–346 · doi:10.2307/2346781
[10] Musa J D 1975 A theory of software reliability and its application. IEEE Trans. Software Eng. SE-1: 312–327
[11] Musa J D 1979 Software reliability data, New York: DACS, RADC/ISISI · Zbl 0409.68007
[12] Musa J D, Iannino A, Okumoto K 1987 Software reliability measurement. Prediction, Application, McGraw-Hill Int. Ed.
[13] Park D H, Lee C H 2003 Markovian imperfect software debugging model and its performance measures. Stochastic Analysis And Applications 21(4): 849–864 · Zbl 1029.68029 · doi:10.1081/SAP-120022866
[14] Pham H 1996 A software cost model with imperfect debugging random life cycle and penalty cost. Int. J. Sys. Sci. 27: 455–463 · Zbl 0852.68006 · doi:10.1080/00207729608929237
[15] Schick G J, Wolverton R W 1978 An analysis of competing software reliability model. IEEE Trans. Software Eng. SE-4: 104–120 · doi:10.1109/TSE.1978.231481
[16] Shooman M L 1968 Probabilistic reliability: An engineering approach. (NY: McGraw Hill) · Zbl 0725.62089
[17] Shooman M L 1972 Probabilistic models for software reliability prediction. Statistical Computer Performance Evaluation, W Freiberger, Ed. Academic, NY, 485–502
[18] Singpurwalla N D, Soyer R 1985 Assessing (Software) reliability growth using a random co-efficient autoregressive process and its ramifications. IEEE Trans. Software Eng. SE-11: 1456–1464 · doi:10.1109/TSE.1985.231889
[19] Soman K P, Misra K B 1993 On Bayesian estimation of system reliability. Microelectronic Reliability 33: 1455–1459 · doi:10.1016/0026-2714(93)90099-K
[20] Sumita U, Shantikumar J G 1986 A software reliability model with multiple-error introduction & removal. IEEE Trans. Rel. R-35: 459–462 · doi:10.1109/TR.1986.4335507
[21] Xie M 1987 A shock model for software reliability. Microelectronic Reliability 27: 717–724 · doi:10.1016/0026-2714(87)90018-7
[22] Xie M 1991 Software reliability modelling. World Scientific Press
[23] Xia G, Zeephongsekul P, Kumar S 1993 Optimal software release policy with a learning factor for imperfect debugging. Microelectronic Reliability 33: 81–86 · doi:10.1016/0026-2714(93)90047-3
[24] Xie M, Dai Y S, Poh K L 2004 Distributed system availability in the case of imperfect debugging process. International Journal of Industrial Engineering-Theory Applications and Practice 11(4): 396–405
[25] Yamada S, Ohba M, Osaki S 1983 S-shaped reliability growth modelling for software error detection. IEEE. Trans. Rel. R-32: 475–478 · doi:10.1109/TR.1983.5221735
[26] Yamada S, Ohba M, Osaki S 1984 S-shaped software reliability growth models and their applications. IEEE. Trans. Rel. R-33: 289–291 · doi:10.1109/TR.1984.5221826
[27] Yamada S, Ohteria H, Narihisa H 1986 Software reliability growth models with testing-effort. IEEE. Trans. Rel. R-35: 19–23 · doi:10.1109/TR.1986.4335332
[28] Yamada S, Hishitani J, Osaki S 1993 Software reliability growth with a weibull test-effort: A model application. IEEE. Trans. Rel. R-42: 100–105 · Zbl 0775.90200 · doi:10.1109/24.210278
[29] Zeephongsekul P, Xia G, Kumar S 1994 Software-reliability growth model: Primary failures generate secondary-faults under imperfect debugging. IEEE Trans. Rel. 43: 408–413 · doi:10.1109/24.326435
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.