Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/3844
Title: On the problem of attribute selection for software cost estimation: Input backward elimination using artificial neural networks
Authors: Papatheocharous, Efi 
Andreou, Andreas S. 
Keywords: Artificial neural network
Software project
Artificial neural network models
Attribute selection
Backward elimination
Complex software
Connection weights
Cost drivers
Garson's algorithm
Input dimensions
Network weights
Nonlinear interactions
Prediction performance
Saliency measure
Software cost estimations
Software development effort
Software systems
Algorithms
Costs
Estimation
Project management
Software design
Neural networks
Issue Date: 2010
Publisher: Springer Berlin Heidelberg
Source: 6th IFIP WG 12.5 International Conference, Larnaca, Cyprus, 6-7 October, 2010.
Abstract: Many parameters affect the cost evolution of software projects. In the area of software cost estimation and project management the main challenge is to understand and quantify the effect of these parameters, or 'cost drivers', on the effort expended to develop software systems. This paper aims at investigating the effect of cost attributes on software development effort using empirical databases of completed projects and building Artificial Neural Network (ANN) models to predict effort. Prediction performance of various ANN models with different combinations of inputs is assessed in an attempt to reduce the models' input dimensions. The latter is performed by using one of the most popular saliency measures of network weights, namely Garson's Algorithm. The proposed methodology provides an insight on the interpretation of ANN which may be used for capturing nonlinear interactions between variables in complex software engineering environments.
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/3844
ISSN: 18684238
DOI: 10.1007/978-3-642-16239-8_38
Rights: IFIP International Federation for Information Processing
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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