Please use this identifier to cite or link to this item:
|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
Artificial neural network models
Software cost estimations
Software development effort
|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|
Show full item record
checked on Jul 23, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.