A practical mathematic model for in time correction–based testability growth test is proposed in this article. This model can make up the gap between the testability growth test practice and theory. The process of testability design defects identification and correction is formulated, based on which the testability growth test plan optimization method is given with the minimum test cost criterion. A Bayes approach is studied to track the testability growth from the test data. Then the tracked results are used to learn the system correction skill and to project the subsequent testability growth test. Simulation results show that the models and methods presented in this paper are reasonable and can efficiently manage the in time correction–based testability growth test planning, tracking, and projecting problem.
LiTMXuCQQiuJ. The assessment and foundation of bell-shaped testability growth effort functions dependent system testability growth models based on NHPP. Math Prob Eng2015; 1–18.
9.
Department of Defense. Reliability growth management. Washington, DC: Department of Defense, 1981.
10.
HallJB.Methodology for evaluating reliability growth programs of discrete systems. PhD Thesis, University of Maryland, College Park, MD, 2008.
11.
DuaneJT.Learning curve approach to reliability monitoring. IEEE T Aerosp1964; 2: 563–566.
FriesASenA.A survey of discrete reliability-growth models. IEEE T Reliab1996; 45(4): 582–604.
14.
CoitDW.Economic allocation of test times for subsystem-level reliability growth testing. IIE Trans1998; 30(12): 1143–1151.
15.
QuigleyJWallsL.Cost-benefit modelling for reliability growth. J Oper Res Soc2003; 54: 1234–1241.
16.
BrownSM.Development and validation of methodology for fix effectiveness projection during product development. PhD Thesis, University of Maryland, College Park, MD, 2009.
17.
CrowLH.Planning a reliability growth program utilizing historical data. In: Proceedings of reliability and maintainability symposium annual, Lake Buena Vista, FL, 24–27 January 2011, pp.1–6. New York: IEEE.
18.
GibsonGJCrowLH.Reliability fix effectiveness factor estimation. In: Proceedings of reliability and maintainability symposium annual, Atlanta, GA, 24–26 January 1989, pp.171–177. New York: IEEE.
19.
AD-B302442:2005. AMSAA maturity projection model based on stein estimation.
20.
JohnBH.Methodology for evaluating reliability growth programs of discrete systems. PhD Thesis, University of Maryland, College Park, MD, 2008.
21.
PattipatiKRRaghavanVShakeriM.TEAMS: testability engineering and maintenance system. In: Proceedings of the American control conference, Baltimore, MD, 29 June–1 July 1994, pp.1989–1995. New York: IEEE.
22.
ZhangSGPattipatiKRHuZ. Optimal selection of imperfect tests for fault detection and isolation. IEEE T Syst Man Cyber: Syst2013; 43(6): 1370–1384.
23.
WangC.Testability demonstration test planning based on sequential posterior odds test method. Proc IMechE, Part H: J Risk and Reliability2014; 228(2): 189–199.
24.
SmithAFM. A Bayesian note on reliability growth during a developmental testing program. IEEE T Reliab1977; 26(5): 346–347.
25.
FardNSDietrichDL.A Bayes reliability growth model for a developmental testing program. IEEE T Reliab1987; 36(5): 568–572.
26.
XingYYWuXYJiangP. Dynamic Bayesian evaluation method for system reliability growth based on in-time correction. IEEE T Reliab2010; 59(2): 582–604.
27.
PaulCMiaoWW.On the choice of a beta prior distribution for binomial sampling. J Probab Stat Sci2012; 10(2): 219–228.
28.
DyerDChiouP.An information theoretic approach to incorporating prior information in binomial sampling. Comm Stat Theor Meth1984; 13(17): 2051–2083.
29.
MartzHFWallerRA.Basics of Bayesian reliability estimation from attribute test data. Technical report, October 1975. New Mexico: Los Alamos Scientific Lab.
30.
VladimirPSMartzHF.Bayes reliability estimation using multiple sources of prior information: binomial sampling. IEEE T Reliab1994; 43(1): 138–144.
