Modeling and analysis of software fault detectability and removability with time variant fault exposure ratio,fault removal efficiency,and change point
Restricted accessResearch articleFirst published online April, 2019
Modeling and analysis of software fault detectability and removability with time variant fault exposure ratio,fault removal efficiency,and change point
Software reliability growth models have been proposed to assess and predict the reliability growth of software, remaining number of faults, and failure rate. In previous studies, software faults have been mainly categorized into two categories based on its severity in removal process: simple faults and hard faults. In reality, fault detectability is one of the crucial factors which can influence the reliability growth of software. The detectability of a software fault depends on how frequently the instructions containing faults are executed. However, fault removability of a software fault depends on fault removal efficiency of debugging team. The main motive of this article is to incorporate the fault detectability in software reliability assessment. Fault exposure ratio is an essential factor for software reliability modeling that controls the per-fault hazard rate. It is strongly dependent on fault detectability. In this article, the effect of fault detectability, fault removability, fault exposure ratio, and fault removal efficiency has been considered simultaneously in software reliability growth modeling. Moreover, a logistic fault exposure ratio has been introduced. The effect of change point is incorporated in the proposed software reliability growth model. Two illustrative examples with software testing data have been presented.
PhamH. System software reliability. London: Springer Science & Business Media, 2007.
4.
LyuMR. Handbook of software reliability engineering, vol. 222. Los Alamitos, CA: IEEE Computer Society Press, 1996.
5.
XieM. Software reliability modelling, vol. 1. Singapore: World Scientific, 1991.
6.
MusaJD. A theory of software reliability and its application. IEEE T Software Eng1975; 1: 312–327.
7.
YamadaSTokunoKOsakiS. Imperfect debugging models with fault introduction rate for software reliability assessment. Int J Syst Sci1992; 23(12): 2241–2252.
8.
ChatterjeeSMisraRAlamS. Prediction of software reliability using an auto regressive process. Int J Syst Sci1997; 28(2): 211–216.
9.
GoelAL. Software reliability models: assumptions, limitations, and applicability. IEEE T Software Eng1985; 11(12): 1411–1423.
10.
TamuraYKawakamiMYamadaS. Reliability modeling and analysis for open source cloud computing. Proc IMechE, Part O: J Risk and Reliability2013; 227(2): 179–186.
11.
FentonNNeilMMarquezD. Using Bayesian networks to predict software defects and reliability. Proc IMechE, Part O: J Risk and Reliability2008; 222(4): 701–712.
12.
InoueSYamadaS. Generalized discrete software reliability modeling with effect of program size. IEEE T Syst Man Cy A2007; 37(2): 170–179.
13.
YamadaS. Software reliability modeling: fundamentals and applications, vol. 5. Tokyo, Japan: Springer, 2014.
14.
YamadaSTamuraY. OSS reliability measurement and assessment. Cham: Springer, 2016.
15.
InoueSYamadaS. Test environment-dependent discrete binomial-type software reliability models. Int J Reliab Qual Saf Eng2017; 24(1): 1750003.
16.
MusaJD. Rationale for fault exposure ratio k. ACM SIGSOFT1991; 16(3): 79.
17.
MalaiyaYKVon MayrhauserASrimaniPK. An examination of fault exposure ratio. IEEE T Software Eng1993; 19(11): 1087–1094.
18.
MalaiyaYKVon MayrhauserASrimaniPK. The nature of fault exposure ratio. In: Proceedings of the 3rd international symposium on software reliability engineering, Research Triangle Park, NC, 7–10 October 1992, pp.23–32. New York: IEEE.
19.
NaixinLMalaiyaYK. Fault exposure ratio estimation and applications. In: Proceedings of the 7th international symposium on software reliability engineering, White Plains, NY, 30 October–2 November 1996, pp.372–381. New York: IEEE.
ZhangXTengXPhamH. Considering fault removal efficiency in software reliability assessment. IEEE T Syst Man Cy A2003; 33(1): 114–120.
22.
GoelALOkumotoK. A Markovian model for reliability and other performance measures of software systems. In: Proceedings of the AFIPS, New York, 4–7 June 1979, p.769. New York: IEEE.
23.
LiuHWYangXZQuFet al. A general NHPP software reliability growth model with fault removal efficiency. Iran J Electr Comput Eng2005; 4(2): 144–149.
24.
ZhaoM. Change-point problems in software and hardware reliability. Commun Stat A: Theor1993; 22(3): 757–768.
25.
ChangI. An analysis of software reliability with change-point models. Taipei, Taiwan: National Science Council, 1997.
26.
ShyurHJ. A stochastic software reliability model with imperfect-debugging and change-point. J Syst Software2003; 66(2): 135–141.
27.
InoueSYamadaS. Optimal software release policy with change-point. In: Proceedings of the IEEE international conference on industrial engineering and engineering management (IEEM 2008), Singapore, 8–11 December 2008, pp.531–535. New York: IEEE.
28.
ChatterjeeSNigamSSinghJBet al. Effect of change point and imperfect debugging in software reliability and its optimal release policy. Math Comp Model Dyn2012; 18(5): 539–551.
29.
ChatterjeeSShuklaA. Change point–based software reliability model under imperfect debugging with revised concept of fault dependency. Proc IMechE, Part O: J Risk and Reliability2016; 230(6): 579–597.
30.
InoueSHayashidaSYamadaS. Extended hazard rate models for software reliability assessment with effect at change-point. Int J Reliab Qual Saf Eng2013; 20(2): 1350009.
31.
MinaminoYInoueSYamadaS. NHPP-based change-point modeling for software reliability assessment and its application to software development management. Ann Oper Res2016; 244(1): 85–101.
32.
ChatterjeeSShuklaA. An ideal software release policy for an improved software reliability growth model incorporating imperfect debugging with fault removal efficiency and change point. Asia Pac J Oper Res2017; 34(3): 1740017.
33.
XieMLiXNgS. Risk-based software release policy under parameter uncertainty. Proc IMechE, Part O: J Risk and Reliability2011; 225(1): 42–49.
34.
SchneidewindNF. Analysis of error processes in computer software. ACM SIGPLAN Notices1975; 10: 337–346.
35.
XieMZhaoM. The Schneidewind software reliability model revisited. In: Proceedings of the 3rd international symposium on software reliability engineering, Research Triangle Park, NC, 7–10 October 1992, pp.184–192. New York: IEEE.
36.
KareerNKapurPGroverP. An S-shaped software reliability growth model with two types of errors. Microelectron Reliab1990; 30(6): 1085–1090.
37.
ZhangXPhamH. Comparisons of nonhomogeneous Poisson process software reliability models and its applications. Int J Syst Sci2000; 31(9): 1115–1123.
38.
YamadaSOhbaMOsakiS. S-shaped reliability growth modeling for software error detection. IEEE T Reliab1983; 32(5): 475–484.
39.
GoelALOkumotoK. Time-dependent error-detection rate model for software reliability and other performance measures. IEEE T Reliab1979; 28(3): 206–211.
40.
InoueSYamadaS. Discrete software reliability assessment with discretized NHPP models. Comput Math Appl2006; 51(2): 161–170.
41.
KillickREckleyI. Changepoint: an R package for changepoint analysis. J Stat Softw2014; 58(3): 1–19.
