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L.A. Tomek, J.K. Muppala, K.S. Trivedi, "Modeling Correlation in Software Recovery Blocks," IEEE Transactions on Software Engineering, vol. 19, no. 11, pp. 10711086, November, 1993.  
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@article{ 10.1109/32.256854, author = {L.A. Tomek and J.K. Muppala and K.S. Trivedi}, title = {Modeling Correlation in Software Recovery Blocks}, journal ={IEEE Transactions on Software Engineering}, volume = {19}, number = {11}, issn = {00985589}, year = {1993}, pages = {10711086}, doi = {http://doi.ieeecomputersociety.org/10.1109/32.256854}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Software Engineering TI  Modeling Correlation in Software Recovery Blocks IS  11 SN  00985589 SP1071 EP1086 EPD  10711086 A1  L.A. Tomek, A1  J.K. Muppala, A1  K.S. Trivedi, PY  1993 KW  software recovery blocks; software faulttolerance technique; recovery blocks; functional specification; successive acceptance tests; correct module outputs; pairwise correlation; betabinomial density; Stochastic Reward Network; Stochastic Petri Net Package; SPNP; Markov models; software reliability; stochastic modeling; stochastic Petri nets; correlation; fault tolerant computing; Petri nets; software reliability; statistical analysis; system recovery VL  19 JA  IEEE Transactions on Software Engineering ER   
The authors examine the problem of accurately modeling the software faulttolerance technique based on recovery blocks. Analysis of some systems have investigated the correlation between software modules, which may be due to a portion of the functional specification that is common to all software modules, or to the inherent hardness of some problems. Three types of dependence which can be captured using measurements are considered. These are correlation between software modules for a single input, correlation between successive acceptance tests on correct module outputs and incorrect module outputs, and correlation between subsequent inputs. The authors' technique is quite general and can be applied to other types of correlation. In accounting for dependence, they use the intensity distribution introduced by D.E. Eckhardt and L.D. Lee (1985). A method of generating the intensity distribution that is based on the pairwise correlation between modules is discussed. This method is contrasted with the assumption of independent modules as well as the use of the betabinomial density introduced by V.F. Nicola and A. Goyai (1990). The effects of dependencies were studied using a Stochastic Reward Network (SRN) that incorporates all of the above dependencies and a modeling tool called Stochastic Petri Net Package (SPNP).
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