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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 difficult to calibrate but inaccurate without calibration. Informal 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 experiential approaches (referred to as COBRA, COst estimation, Benchmarking, and Risk Assessment). We find through a case study that cost estimates using COBRA show an average ARE of 0.09, and show that the results are easily usable for benchmarking and risk assessment purposes. 1 Introduction Project and program managers require accurate and reliable cost estimates to allocate and control project resources, and to make realistic bids on external contracts. ...
Briand et al. (1998) studied this question.