1. A method to predict performance of a software project, comprising: a) determining, using a computer, performance estimates associated with at least one design count, the design count comprising at least one function point count; b) collecting actual performance data; c) comparing the performance estimates with the actual performance data; d) determining, using the computer, new performance estimates for a remainder of the software project in response to the actual performance data collected; and e) refining a design of the software project based on the new performance estimates, wherein determining the performance estimate comprises, in a single-variate implementation, using a least-squares single variate line of the form y=a+bx to determine the performance estimate, where y is a predicted value, a and b are least squares curve fit values, and x is a design count, wherein a and b are chosen to minimize a square of the error, and are defined by: a = ( ∑ y ) ⁢ ( ∑ x 2 ) - ( ∑ x ) ⁢ ( ∑ xy ) n ⁢ ∑ x 2 - ( ∑ x ) 2 ⁢ ⁢ and b = n ⁢ ∑ xy - ( ∑ x ) ⁢ ( ∑ y ) n ⁢ ∑ x 2 - ( ∑ x ) 2 , where ∑ ⁢ … = ∑ i = 1 n ⁢ ⁢ … ⁢ ⁢ i , y=measured values, x is the design count, and n=number of data points for which there are measured data and design counts and n is greater than zero.