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Software-reliability growth with a Weibull test-effort: a model and application
World Abstracts on Microelectronics and Reliability based on non-optimal intervals between maintenances, periodic major maintenance can reduce costs in some cases.
A model for the integrity assessment of ageing repairable systems. G. U. CREVECOEUR. I E E E Transactions on Reliability, 42(1), 148 (1993). The failure rate of mechanical repairable systems that deteriorate with time due to ageing can usually be visualized by a bathtub curve. The paper shows that: o an equation, that is valid in other respects for describing creep curves, can easily be deduced from functional forms of the failure rate of mechanical repairable systems; • creeping pieces can be considered repairable systems that evolve under an applied load as combining positive and negative feed-back loops. More generally, this can be extended to mechanical repairable systems, the negative feed-back loops corresponding to repair and overhaul operations. The equation describing creep curves reflects the ageing of mechanical repairable systems. A critical time at which the system can no longer be restored to full performance in spite of repair and/or replacement of sub-parts can then be predicted. An application example is given using published failure data corresponding to a submarine main-propulsion diesel engine. The proposed model should apply every time that mechanical system ageing is expressed by a bathtub curve.
Transient behaviour and parametric sensitivity in failure prone systems. F. ARCHETTIand A. SCHIOMACHEN. Computers and Electrical Engineering, 19(2), 65 (1993). In this paper we present a new approach for the numerical evaluation of the transient behaviour and the sensitivity analysis of Petri net (PN) based models of manufacturing systems. In particular, the influence of the mean time between failure (MTBF) and the mean time to repair (MTTR) in a transfer line is evaluated. The computational results of the proposed case study are presented along with an analysis of the blocking/routing control policies which govern the system. Software-reliability growth with a Weibull test-effort: a model and application. SHIGERU YAMADA, JUN HISHITANI and SHUNJIOSAKI. I E E E Transactions on Reliability, 42(1), 100 (1993). Software reliability measurement during the testing phase is essential for examining the degree of quality or reliability of a developed software system. We develop a software-reliability growth model incorporating the amount of test-effort expended during the software testing phase. The time-dependent behavior of test-effort expenditures is described by a Weibull curve. Assuming that the error detection rate to
1415
the amount of test-effort spent during the testing phase is proportional to the current error content, the model is formulated by a nonhomogeneous Poisson process. Using the model, the method of data analysis for software reliability measurement is developed. This model is applied to the prediction of additional test-effort expenditures to achieve the objective number of errors detected by software testing and the determination of the optimum time to stop software-testing for release.
Estimating defects in commercial software during operational use. GARRISONQ. KENNEY. I E E E Transactions on Reliability, 42(1), 107 (1993). Available software-reliability growth models are not suitable during the operational phase for wide-distribution commercial software because data on execution time (or an equivalent) are not readily available. The usage of this type of software typically grows over time to thousands of individual systems, and obtaining detailed data on execution time between failures on all of the using systems is not reasonable. This paper develops a new model that accounts for the usage growth of commercial software during the operational phase and that incorporates a factor to estimate (from field-failure reports) the usage growth. The model can estimate: (!) the number of remaining unique defects in wide-distribution commercial software during the operational phase, and (2) the anticipated arrival times of customer-reported failures attributable to these unique defects. The model is based on the Weibull distribution which arises from the assumption that field usage of commercial software increases as a power function of time. The model was fit to the actual failure times for two commercial software products--one that runs on 105 systems and the other on 104 systems. The model fits the general shape of the arrival distribution for the actual defect discovery times, but there are minor peaks in the example data that are not explained by the model. Some of the minor modes correspond to peak defect discovery times for subsequent releases of the software. Graphical techniques for analyzing failure data with the percentile residual-life function. ROBERT L. LAUNER. I E E E Transactions on Reliability, 42(1), 71 (1993). Graphical data-analysis techniques are presented for studying the empirical behavior of the hazard (failure) rate based on a relationship between the maximum and minimum of the o-percentile residual life function to the minimum and maximum, respectively, of the hazard rate. Knowledge of these critical points is useful in controlling the system percentile life through burn-in. The graphical techniques are illustrated with a previously published data-set consisting of the empirical hazard rate of aircraft-engine components.
A model for the integrity assessment of ageing repairable systems. G. U. CREVECOEUR. I E E E Transactions on Reliability, 42(1), 148 (1993). The failure rate of mechanical repairable systems that deteriorate with time due to ageing can usually be visualized by a bathtub curve. The paper shows that: o an equation, that is valid in other respects for describing creep curves, can easily be deduced from functional forms of the failure rate of mechanical repairable systems; • creeping pieces can be considered repairable systems that evolve under an applied load as combining positive and negative feed-back loops. More generally, this can be extended to mechanical repairable systems, the negative feed-back loops corresponding to repair and overhaul operations. The equation describing creep curves reflects the ageing of mechanical repairable systems. A critical time at which the system can no longer be restored to full performance in spite of repair and/or replacement of sub-parts can then be predicted. An application example is given using published failure data corresponding to a submarine main-propulsion diesel engine. The proposed model should apply every time that mechanical system ageing is expressed by a bathtub curve.
Transient behaviour and parametric sensitivity in failure prone systems. F. ARCHETTIand A. SCHIOMACHEN. Computers and Electrical Engineering, 19(2), 65 (1993). In this paper we present a new approach for the numerical evaluation of the transient behaviour and the sensitivity analysis of Petri net (PN) based models of manufacturing systems. In particular, the influence of the mean time between failure (MTBF) and the mean time to repair (MTTR) in a transfer line is evaluated. The computational results of the proposed case study are presented along with an analysis of the blocking/routing control policies which govern the system. Software-reliability growth with a Weibull test-effort: a model and application. SHIGERU YAMADA, JUN HISHITANI and SHUNJIOSAKI. I E E E Transactions on Reliability, 42(1), 100 (1993). Software reliability measurement during the testing phase is essential for examining the degree of quality or reliability of a developed software system. We develop a software-reliability growth model incorporating the amount of test-effort expended during the software testing phase. The time-dependent behavior of test-effort expenditures is described by a Weibull curve. Assuming that the error detection rate to
1415
the amount of test-effort spent during the testing phase is proportional to the current error content, the model is formulated by a nonhomogeneous Poisson process. Using the model, the method of data analysis for software reliability measurement is developed. This model is applied to the prediction of additional test-effort expenditures to achieve the objective number of errors detected by software testing and the determination of the optimum time to stop software-testing for release.
Estimating defects in commercial software during operational use. GARRISONQ. KENNEY. I E E E Transactions on Reliability, 42(1), 107 (1993). Available software-reliability growth models are not suitable during the operational phase for wide-distribution commercial software because data on execution time (or an equivalent) are not readily available. The usage of this type of software typically grows over time to thousands of individual systems, and obtaining detailed data on execution time between failures on all of the using systems is not reasonable. This paper develops a new model that accounts for the usage growth of commercial software during the operational phase and that incorporates a factor to estimate (from field-failure reports) the usage growth. The model can estimate: (!) the number of remaining unique defects in wide-distribution commercial software during the operational phase, and (2) the anticipated arrival times of customer-reported failures attributable to these unique defects. The model is based on the Weibull distribution which arises from the assumption that field usage of commercial software increases as a power function of time. The model was fit to the actual failure times for two commercial software products--one that runs on 105 systems and the other on 104 systems. The model fits the general shape of the arrival distribution for the actual defect discovery times, but there are minor peaks in the example data that are not explained by the model. Some of the minor modes correspond to peak defect discovery times for subsequent releases of the software. Graphical techniques for analyzing failure data with the percentile residual-life function. ROBERT L. LAUNER. I E E E Transactions on Reliability, 42(1), 71 (1993). Graphical data-analysis techniques are presented for studying the empirical behavior of the hazard (failure) rate based on a relationship between the maximum and minimum of the o-percentile residual life function to the minimum and maximum, respectively, of the hazard rate. Knowledge of these critical points is useful in controlling the system percentile life through burn-in. The graphical techniques are illustrated with a previously published data-set consisting of the empirical hazard rate of aircraft-engine components.