As one of the most important indexes to evaluate the quality of software, software reliability experiences
an increasing development in recent years. We investigate a software reliability growth model (SRGM). The
application of this model is to predict the occurrence of the software faults based on the non-homogeneous Poisson
process (NHPP). Unlike the independent assumptions in other models, we consider fault dependency. The testing
faults are divided into three classes in this model: leading faults, first-step dependent faults and second-step
dependent faults. The leading faults occurring independently follow an NHPP, while the first-step dependent
faults only become detectable after the related leading faults are detected. The second-step dependent faults can
only be detected after the related first-step dependent faults are detected. Then, the combined model is built
on the basis of the three sub-processes. Finally, an illustration based on real dataset is presented to verify the
proposed model.
PENG Rui (彭锐), MA Xiaoyang *(马晓洋), ZHAI Qingqing (翟庆庆), GAO Kaiye (高凯烨)
. Software Reliability Growth Model Considering First-Step and Second-Step Fault Dependency[J]. Journal of Shanghai Jiaotong University(Science), 2019
, 24(4)
: 477
-479
.
DOI: 10.1007/s12204-019-2097-z
[1] LYUM R. Handbook of software reliability engineering[M]. New York, USA: McGraw-Hill, 1996.
[2] CHANG Y C, LIU C T. A generalized JM model withapplications to imperfect debugging in software reliability[J]. Applied Mathematical Modelling, 2009, 33:3578-3588.
[3] SHATNAWI O. Discrete time modelling in softwarereliability engineering: A unified approach [J]. ComputerSystems Science and Engineering, 2009, 24(6):391-398.
[4] LIN C T, LI Y F. Rate-based queueing simulationmodel of open source software debugging activities[J]. IEEE Transactions on Software Engineering, 2014,40(11): 1075-1099.
[5] WANG L J, HU Q P, LIU J. Software reliability growthmodeling and analysis with dual fault detection andcorrection processes [J]. IIE Transactions, 2016, 48(4):359-370.
[6] TAMURA Y, YAMADA S. A flexible stochastic differentialequation model in distributed developmentenvironment [J]. European Journal of Operational Research,2006, 168: 143-152.
[7] ZHAO J, LIU H W, CUI G, et al. Software reliabilitygrowth model with change-point and environmentalfunction [J]. The Journal of Systems and Software,2006, 79(11): 1578-1587.
[8] SHATNAWI O. Measuring commercial software operationalreliability: An interdisciplinary modelling approach[J]. Maintenance and Reliability, 2014, 16(4):585-594.
[9] PENG R, LI Y F, ZHANG J G, et al. A risk-reductionapproach for optimal software release time determinationwith the delay incurred cost [J]. InternationalJournal of Systems Science, 2015, 46(9): 1628-1637.
[10] GOˇSEVA-POPSTOJANOVA K, TRIVEDI K S. Failurecorrelation in software reliability models [J]. IEEETransactions on Software Engineering, 2000, 49(1):37-47.
[11] KAPUR P K, YOUNES S. Software reliability growthmodel with error dependency [J]. Microelectronics Reliability,1995, 35(2): 273-278.
[12] HUANG C Y, LIN C T. Software reliability analysisby considering fault dependency and debugging timelag [J]. IEEE Transactions on Reliability, 2006, 55(3):436-450.
[13] MUSA J D, IANNINO A, OKUMONO K. Softwarereliability: Measurement, prediction and application[M]. New York, USA: McGraw-Hill, 1987.
