Effort estimation using analogy

The staff resources or effort required for a software project are notoriously difficult to estimate in advance. To date most work has focused upon algorithmic cost models such as COCOMO and Function Points. These can suffer from the disadvantage of the need to calibrate the model to each individual measurement environment coupled with very variable accuracy levels even after calibration. An alternative approach is to use analogy for estimation. We demonstrate that this method has considerable promise in that we show it to out perform traditional algorithmic methods for six different datasets. A disadvantage of estimation by analogy is that it requires a considerable amount of computation. The paper describes an automated environment known as ANGEL that supports the collection, storage and identification of the most analogous projects in order to estimate the effort for a new project. ANGEL is based upon the minimisation of Euclidean distance in n-dimensional space. The software is flexible and can deal with differing datasets both in terms of the number of observations (projects) and in the variables collected. Our analogy approach is evaluated with six distinct datasets drawn from a range of different environments and is found to outperform other methods. It is widely accepted that effective software effort estimation demands more than one technique. We have shown that estimating by analogy is a candidate technique and that with the aid of an automated environment is an eminently practical technique