  Feature selection has been recently used in the area of software engineering
for improving the accuracy and robustness of software cost models. The idea
behind selecting the most informative subset of features from a pool of
available cost drivers stems from the hypothesis that reducing the
dimensionality of datasets will significantly minimise the complexity and time
required to reach to an estimation using a particular modelling technique. This
work investigates the appropriateness of attributes, obtained from empirical
project databases and aims to reduce the cost drivers used while preserving
performance. Finding suitable subset selections that may cater improved
predictions may be considered as a pre-processing step of a particular
technique employed for cost estimation (filter or wrapper) or an internal
(embedded) step to minimise the fitting error. This paper compares nine
relatively popular feature selection methods and uses the empirical values of
selected attributes recorded in the ISBSG and Desharnais datasets to estimate
software development effort.
