A Bayesian Software Estimating Model Using a Generalized g-Prior Approach
Sunita Chulani · 1999
Created to provide a software cost estimation model suited for a rapidly evolving environment, the COCOMO II model is the result of a 1994 research effort to update the 1981 COnstructive COst MOdel and its 1987 Ada version. Boehm et al [3, 15] provided the initial definition and rationale for this model. The model’s inputs include Source Lines of Code and/or Function Points as the sizing parameter, adjusted for both reuse and breakage; a set of 17 multiplicative effort multipliers and a set of 5 exponential scale factors [see appendix A]. They based their initial calibration of the model on expert judgement. Soon after the initial publication of this model, the Center for Software Engineering (CSE) began an effort to empirically validate COCOMO II [14]. By January 1997, they had a dataset consisting of 83 completed projects collected from several Commercial, Aerospace, Government and FFRDC organizations. CSE used this dataset to calibrate the COCOMO II.1997 model parameters. Because of uncertainties in the data and / or respondents ’ misinterpretations of the rating scales, CSE developed a pragmatic calibration procedure for combining sample estimates with expert judgement. Specifically, the above model calibration for the COCOMO II.1997 parameters assigned a 10 % weight to the regression estimates while expert-judgement estimates received a weight of 90%. This calibration procedure yielded effort predictions within 30 % of the actuals 52 % of the time. CSE continued the data collection effort and the database grew from 83 datapoints in 1997 to 161 datapoints in 1998. Using this data and a Bayesian approach that can assign differential weights to the parameters based on the precision of the data, we provide an alternative calibration