A Hybrid Method for Software Cost Estimation and Risk Assessment

Current cost estimation techniques have a number of drawbacks. For example, developing algorithmic models requires extensive past project data. Also, off-the-shelf models have been found to be inaccurate without calibration. Approaches based on experienced estimators depend on estimators' availability and are not easily repeatable, as well as not being much more accurate than algorithmic techniques. In this paper we present a method for cost estimation that combines aspects of algorithmic and experential approaches. We find through a case study that cost estimates using this approach have average ARE of 0.09, and also the results can be used for risk assessment.