Includes bibliographical references and index.

Part i. System reliability and optimization1 multi-state k-out-of-n systemsming j. Zuo, jinsheng huang and way kuo1.1 introduction1.2 relevant concepts in binary reliability theory1.3 binary k-out-of-n models1.3.1 the k-out-of-n:g system with independently and identically distributed components1.3.2 reliability evaluation using minimal path or cut sets1.3.3 recursive algorithms1.3.4 equivalence between a k-out-of-n:g system and an (n - k + 1)-out-of-n:f system1.3.5 the dual relationship between the k-out-of-n g and f systems1.4 relevant concepts in multi-state reliability theory1.5 a simple multi-state k-out-of-n: g model1.6 a generalized multi-state k-out-of-n:g system model1.7 properties of generalized multi-state k-out-of-n:g systems1.8 equivalence and duality in generalized multi-state k-out-of-n systems2 reliability of systems with multiple failure modeshoang pham2.1 introduction2.2 the series system2.3 the parallel system2.3.1 cost optimization2.4 the parallel-series system2.4.1 the profit maximization problem2.4.2 optimization problem2.5 the series-parallel system2.5.1 maximizing the average system profit2.5.2 consideration of type i design error2.6 the k-out-of-n systems2.6.1 minimizing the average system cost2.7 fault-tolerant systems2.7.1 reliability evaluation2.7.2 redundancy optimization2.8 weighted systems with three failure modes3 reliabilities of consecutive-k systemsjen-chun chang and frank k. Hwang3.1 introduction3.1.1 background3.1.2 notation3.2 computation of reliability3.2.1 the recursive equation approach3.2.2 the markov chain approach3.2.3 asymptotic analysis3.3 invariant consecutive systems3.3.1 invariant consecutive-2systems3.3.2 invariant consecutive-k systems3.3.3 invariant consecutive-kg system.3.4 component importance and the component replacement problem3.4.1 the birnbaum importance3.4.2 partial birnbaum importance3.4.3 the optimal component replacement3.5 the weighted-consecutive-k-out-of-n system.3.5.1 the linear weighted-consecutive-k-out-of-n system3.5.2 the circular weighted-consecutive-k-out-of-n system3.6 window systems3.6.1 the f -within-consecutive-k-out-of-n system3.6.2 the 2-within-consecutive-k-out-of-n system3.6.3 the b-fold-window system3.7 network systems3.7.1 the linear consecutive-2 network system3.7.2 the linear consecutive-k network system3.7.3 the linear consecutive-k flow network system3.8 conclusion4 multi-state system reliability analysis and optimizationg. Levitin and a. Lisnianski4.1 introduction4.1.1 notation4.2 multi-state system reliability measures4.3 multi-state system reliability indices evaluation based on the universal generating function4.4 determination of u-function of complex multi-state system using composition operators4.5 importance and sensitivity analysis of multi-state systems4.6 multi-state system structure optimization problems4.6.1 optimization technique4.6.1.1 genetic algorithm4.6.1.2 solution representation and decoding procedure4.6.2 structure optimization of series-parallel system with capacity-based performance measure 4.6.2.1 problem formulation4.6.2.2 solution quality evaluation4.6.3 structure optimization of multi-state system with two failure modes4.6.3.1 problem formulation4.6.3.2 solution quality evaluation4.6.4 structure optimization for multi-state system with fixed resource requirements and unreliable sources4.6.4.1 problem formulation4.6.4.2 solution quality evaluation4.6.4.3 the output performance distribution of a system containing identical elements in the main producing subsystem4.6.4.4 the output performance distribution of a system containing different elements in the main producing subsystem4.6.5 other problems of multi-state system optimization5 combinatorial reliability optimizationc. S. Sung, y. K. Cho and s. H. Song5.1 introduction5.2 combinatorial reliability optimization problems of series structure5.2.1 optimal solution approaches5.2.1.1 partial enumeration method5.2.1.2 branch-and-bound method5.2.1.3 dynamic programming5.2.2 heuristic solution approach5.3 combinatorial reliability optimization problems of a non-series structure5.3.1 mixed series-parallel system optimization problems5.3.2 general system optimization problems5.4 combinatorial reliability optimization problems with multiple-choice constraints5.4.1 one-dimensional problems5.4.2 multi-dimensional problems5.5 summarypart ii. Statistical reliability theory6 modeling the observed failure ratem. S. Finkelstein6.1 introduction6.2 survival in the plane6.2.1 one-dimensional case6.2.2 fixed obstacles6.2.3 failure rate process6.2.4 moving obstacles6.3 multiple availability6.3.1 statement of the problem6.3.2 ordinary multiple availability6.3.3 accuracy of a fast repair approximation6.3.4 two non-serviced demands in a row6.3.5 not more than n non-serviced demands6.3.6 time redundancy6.4 modeling the mixture failure rate6.4.1 definitions and conditional characteristics6.4.2 additive model6.4.3 multiplicative model6.4.4 some examples6.4.5 inverse problem7 concepts of stochastic dependence in reliability analysisc. D. Lai and m. Xie7.1 introduction7.2 important conditions describing positive dependence7.2.1 six basic conditions7.2.2 the relative stringency of the conditions7.2.3 positive quadrant dependent in expectation7.2.4 associated random variables7.2.5 positively correlated distributions7.2.6 summary of interrelationships7.3 positive quadrant dependent concept 7.3.1 constructions of positive quadrant dependent bivariate distributions7.3.2 applications of positive quadrant dependence concept to reliability7.3.3 effect of positive dependence on the mean lifetime of a parallel system7.3.4 inequality without any aging assumption 7.4 families of bivariate distributions that are positive quadrant dependent7.4.1 positive quadrant dependent bivariate distributions with simple structures7.4.2 positive quadrant dependent bivariate distributions with more complicated structures7.4.3 positive quadrant dependent bivariate uniform distributions7.4.3.1 generalized farlie-gumbel-morgenstern family of copulas7.5 some related issues on positive dependence7.5.1 examples of bivariate positive dependence stronger than positive quadrant dependent condition7.5.2 examples of negative quadrant dependence7.6 positive dependence orderings7.7 concluding remarks8 statistical reliability change-point estimation modelsming zhao8.1 introduction8.2 assumptions in reliability change-point models8.3 some specific change-point models8.3.1 jelinski-moranda de-eutrophication model with a change point8.3.1.1 model review8.3.1.2 model with one change point8.3.2 weibull change-point model8.3.3 littlewood model with one change point8.4 maximum likelihood estimation8.5 application8.6 summary9 concepts and applications of stochastic aging in reliabilityc. D. Lai and m. Xie9.1 introduction9.2 basic concepts for univariate reliability classes9.2.1 some acronyms and the notions of aging9.2.2 definitions of reliability classes9.2.3 interrelationships9.3 properties of the basic concepts9.3.1 properties of increasing and decreasing failure rates9.3.2 property of increasing failure rate on average9.3.3 properties of nbu, nbuc, and nbue9.4 distributions with bathtub-shaped failure rates9.5 life classes characterized by the mean residual lifetime9.6 some further classes of aging9.7 partial ordering of life distributions9.7.1 relative aging9.7.2 applications of partial orderings9.8 bivariate reliability classes9.9 tests of stochastic aging9.9.1 a general sketch of tests9.9.2 summary of tests of aging in univariate case9.9.3 summary of tests of bivariate aging9.10 concluding remarkson aging10 class of nbu-t0 life distributiondong ho park10.1 introduction10.2 characterization of nbu-t0class10.2.1 boundary members of nbu-t0 and nwu-t010.2.2 preservation of nbu-t0 and nwu-t0 properties under reliability operations10.3 estimation of nbu-t0 life distribution10.3.1 reneau-samaniego estimator10.3.2 chang-rao estimator10.3.2.1 positively biased estimator10.3.2.2 geometric mean estimator10.4 tests for nbu-t0 life distribution10.4.1 tests for nbu-t0 alternatives using complete data10.4.1.1 hollander-park-proschan test10.4.1.2 ebrahimi-habibullah test10.4.1.3 ahmad test10.4.2 tests for nbu-t0 alternatives using incomplete datapart iii. Software reliability11 software reliability models: a selective survey and new directionssiddhartha r. Dalal11.1 introduction11.2 static models11.2.1 phase-based model: gaffney and davis11.2.2 predictive development life cycle model: dalal and ho 11.3 dynamic models: reliability growth models for testing and operational use11.3.1 a general class of models11.3.2 assumptions underlying the reliability growth models11.3.3 caution in using reliability growth models11.4 reliability growth modeling with covariates11.5 when to stop testing software11.6 challenges and conclusions12 software reliability modelingjames ledoux12.1 introduction12.2 basic concepts of stochastic modeling12.2.1 metrics with regard to the first failure12.2.2 stochastic process of times of failure12.3 black-box software reliability models12.3.1 self-exciting point processes12.3.1.1 counting statistics for a self-exciting point process12.3.1.2 likelihood function for a self-exciting point process12.3.1.3 reliability and mean time to failure functions 12.3.2 classification of software reliability models12.3.2.1 0-memory self-exciting point process12.3.2.2 non-homogeneous poisson process model: e(t; ht , f0) = f (t; f0) and is deterministic12.3.2.3 1-memory self-exciting point process with e(t; ht , f0) = f (n(t), t - tn(t), f0)12.3.2.4 m y2-memory12.4 white-box modeling12.5 calibration of model12.5.1 frequentist procedures12.5.2 bayesian procedure12.6 current issues12.6.1 black-box modeling12.6.1.1 imperfect debugging12.6.1.2 early prediction of software reliability12.6.1.3 environmental factors12.6.1.4 conclusion12.6.2 white-box modeling12.6.3 statistical issues13 software availability theory and its applicationskoichi tokuno and shigeru yamada13.1 introduction13.2 basic model and software availability measures13.3 modified models13.3.1 model with two types of failure13.3.2 model with two types of restoration13.4 applied models13.4.1 model with computation performance13.4.2 model for hardware-software system13.5 concluding remarks14 software rejuvenation: modeling and applicationstadashi dohi, katerina goseva-popstojanova, kalyanaraman vaidyanathan, kishor s. Trivedi and shunji osaki14.1 introduction14.2 modeling-based estimation14.2.1 examples in telecommunication billing applications14.2.2 examples in a transaction-based software system14.2.3 examples in a cluster system14.3 measurement-based estimation14.3.1 time-based estimation14.3.2 time and workload-based estimation14.4 conclusion and future work15 software reliability management: techniques and applicationsmitsuhiro kimura and shigeru yamada15.1 introduction15.2 death process model for software testing management15.2.1 model description15.2.1.1 mean number of remaining software faults/testing cases15.2.1.2 mean time to extinction15.2.2 estimation method of unknown parameters15.2.2.1 case of 0