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The Importance of Rating Level Selection for Input Variables to Determine Accurate Estimations in Parametric Mathematical Models

Abstract The use of parametric models is one of the main cost estimation methods in software engineering. It is thought that precision in these models depends on three fundamental factors: the mathematical equations applied; the calibration of the numerical values, which may take the input variables used by the model; and a correct rating level selection for each input variable for each project. This study shows that for accurate estimations, the third factor, the correct rating level for input variables of a model, may have as much or more importance than the other two. This is a study of the numerical relation of the errors that may be introduced in the estimations through each of the three factors.