Abstract
There are many sophisticated parametric models for estimating the size, cost, and schedule of software projects. In general, the predictive accuracy of these models is no better than within 25 percent of actual cost or schedule, about one half of the time (Thibodeau, 1981; IIT Research Institute, 1988). Several authors assert that a model's predictive accuracy can be improved by calibrating (adjusting) its default parameters to a specific environment (Kemerer, 1987; Van Genuchten and Koolen, 1991; Andolfi et al. 1996). This paper reports the results of a long-term project that tests this assertion.
