Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/3845
Title: Feature subset selection for software cost modelling and estimation
Authors: Papatheocharous, Efi 
Papadopoulos, Harris
Andreou, Andreas S. 
Keywords: Cost drivers
Cost estimations
Data sets
Empirical values
Feature selection methods
Feature subset selection
Fitting error
Modelling techniques
Pre-processing step
Project database
Software cost
Software cost models
Software development effort
Subset selection
Cost reduction
Estimation
Feature extraction
Set theory
Software engineering
Cost estimating
Issue Date: 2010
Publisher: CRL
Source: Engineering Intelligent Systems, 2010, Volume 18, Issue 3-4, Pages 233-246
Abstract: 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. 2010 CRL Publishing Ltd.
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/3845
ISSN: 14728915
Rights: © 2010 CRL Publishing Ltd
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