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Feature selection has been recently used in the area of software engineeringfor improving the accuracy and robustness of software cost models. The ideabehind selecting the most informative subset of features from a pool ofavailable cost drivers stems from the hypothesis that reducing thedimensionality of datasets will significantly minimise the complexity and timerequired to reach to an estimation using a particular modelling technique. Thiswork investigates the appropriateness of attributes, obtained from empiricalproject databases and aims to reduce the cost drivers used while preservingperformance. Finding suitable subset selections that may cater improvedpredictions may be considered as a pre-processing step of a particulartechnique employed for cost estimation (filter or wrapper) or an internal(embedded) step to minimise the fitting error. This paper compares ninerelatively popular feature selection methods and uses the empirical values ofselected attributes recorded in the ISBSG and Desharnais datasets to estimatesoftware development effort.

Feature Subset Selection for Software Cost Modelling and Estimation