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A requirement exists for a sealed window assembly for G.A.S. payloads. Bristol Aerospace Limited has designed and developed a synthetic fused silica window assembly for the National Research Council of Canada's G.A.S. payload 'PHOTONS' G-494. The details of the design and development of this window assembly are given in this paper.
In spite of much research effort, there is no universally applicable software reliability growth model which can be trusted to give accurate predictions of reliability in all circumstances. Further, it is not even possible to decide a priori which of the many models is most suitable in a particular context. In an attempt to resolve this problem, techniques were developed whereby, for each program, the accuracy of various models can be analyzed. A user is thus enabled to select that model which is giving the most accurate reliability predictions for the particular program under examination. One of these ways of analyzing predictive accuracy, called the u-plot, in fact allows a user to estimate the relationship between the predicted reliability and the true reliability. It is shown how this can be used to improve reliability predictions in a completely general way by a process of recalibration. Simulation results show that the technique gives improved reliability predictions in a large proportion of cases. However, a user does not need to trust the efficacy of recalibration, since the new reliability estimates produced by the technique are truly predictive and so their accuracy in a particular application can be judged using the earlier methods. The generality of this approach would therefore suggest that it be applied as a matter of course whenever a software reliability model is used.
Recent work by Eckhardt and Lee (1985) shows that independently developed program versions fail dependently (specifically, simultaneous failure of several is greater than would be the case under true independence). The present authors show there is a precise duality between input choice and program choice in this model and consider a generalization in which different versions can be developed using diverse methodologies. The use of diverse methodologies is shown to decrease the probability of the simultaneous failure of several versions. Indeed, it is theoretically possible to obtain versions which exhibit better than independent failure behavior. The authors try to formalize the notion of methodological diversity by considering the sequence of decision outcomes that constitute a methodology. They show that diversity of decision implies likely diversity of behavior for the different verions developed under such forced diversity. For certain one-out-of-n systems the authors obtain an optimal method for allocating diversity between versions. For two-out-of-three systems there seem to be no simple optimality results which do not depend on constraints which cannot be verified in practice.
In spite of much research effort, there is no universally applicable software reliability growth model which can be trusted to give accurate predictions of reliability in all circumstances. Further, it is not even possible to decide a priori which of the many models is most suitable in a particular context. In an attempt to resolve this problem, techniques were developed whereby, for each program, the accuracy of various models can be analyzed. A user is thus enabled to select that model which is giving the most accurate reliability predicitons for the particular program under examination. One of these ways of analyzing predictive accuracy, called the u-plot, in fact allows a user to estimate the relationship between the predicted reliability and the true reliability. It is shown how this can be used to improve reliability predictions in a completely general way by a process of recalibration. Simulation results show that the technique gives improved reliability predictions in a large proportion of cases. However, a user does not need to trust the efficacy of recalibration, since the new reliability estimates prodcued by the technique are truly predictive and so their accuracy in a particular application can be judged using the earlier methods. The generality of this approach would therefore suggest that it be applied as a matter of course whenever a software reliability model is used.