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| S. Brocklehurst, P.Y. Chan, B. Littlewood, J. Snell, "Recalibrating Software Reliability Models," IEEE Transactions on Software Engineering, vol. 16, no. 4, pp. 458-470, April, 1990. | |||
| BibTex | x | ||
| @article{ 10.1109/32.54297, author = {S. Brocklehurst and P.Y. Chan and B. Littlewood and J. Snell}, title = {Recalibrating Software Reliability Models}, journal ={IEEE Transactions on Software Engineering}, volume = {16}, number = {4}, issn = {0098-5589}, year = {1990}, pages = {458-470}, doi = {http://doi.ieeecomputersociety.org/10.1109/32.54297}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Software Engineering TI - Recalibrating Software Reliability Models IS - 4 SN - 0098-5589 SP458 EP470 EPD - 458-470 A1 - S. Brocklehurst, A1 - P.Y. Chan, A1 - B. Littlewood, A1 - J. Snell, PY - 1990 KW - software reliability models recalibrating; simulation results; predictive accuracy; u-plot; software reliability. VL - 16 JA - IEEE Transactions on Software Engineering ER - | |||
There is no universally applicable software reliability growth model which can be trusted to give accurate predictions of reliability in all circumstances. A technique of analyzing predictive accuracy called the u-plot 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 very 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 their accuracy in a particular application can be judged using the earlier methods. The generality of this approach suggests its use whenever a software reliability model is used. Indeed, although this work arose from the need to address the poor performance of software reliability models, it is likely to have applicability in other areas such as reliability growth modeling for hardware.
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