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Jo?o W. Cangussu, Raymond A. DeCarlo, Aditya P. Mathur, "Using Sensitivity Analysis to Validate a State Variable Model of the Software Test Process," IEEE Transactions on Software Engineering, vol. 29, no. 5, pp. 430443, May, 2003.  
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@article{ 10.1109/TSE.2003.1199072, author = {Jo?o W. Cangussu and Raymond A. DeCarlo and Aditya P. Mathur}, title = {Using Sensitivity Analysis to Validate a State Variable Model of the Software Test Process}, journal ={IEEE Transactions on Software Engineering}, volume = {29}, number = {5}, issn = {00985589}, year = {2003}, pages = {430443}, doi = {http://doi.ieeecomputersociety.org/10.1109/TSE.2003.1199072}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Software Engineering TI  Using Sensitivity Analysis to Validate a State Variable Model of the Software Test Process IS  5 SN  00985589 SP430 EP443 EPD  430443 A1  Jo?o W. Cangussu, A1  Raymond A. DeCarlo, A1  Aditya P. Mathur, PY  2003 KW  Software process KW  feedback control KW  sensitivity analysis KW  modeling KW  software test process KW  state model. VL  29 JA  IEEE Transactions on Software Engineering ER   
Abstract—We report on the sensitivity analysis of a state variable model (Model S) proposed earlier. Model S captures the dominant behavior of the system test phase of the software test process. Sensitivity analysis is a mathematical methodology to compute changes in the system behavior due to changes in system parameters or variables. This is particularly important when parameters are calibrated using noisy or small data sets. Nevertheless, by mathematically quantifying the effects of parameter variations on the behavior of the model, and thereby the STP, one can easily and quickly evaluate the effect of such variations on the process performance without having to perform extensive simulations. In all cases studied, Model S behaved according to empirical observations which serves to validate the model. It is also shown that sensitivity analysis can suggest structural improvements in a model when the model does not behave as expected.
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