Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/3843
Title: A genetic programming approach to software cost modeling and estimation
Authors: Papatheocharous, Efi 
Iasonos, Angela
Andreou, Andreas S. 
Keywords: Candidate solution
Cost estimations
Data sets
Dependent variables
Explanatory variables
Project characteristics
Regression equation
Software cost
Software cost estimations
Software project
Costs
Estimation
Genetic programming
Information systems
Mathematical operators
Cost benefit analysis
Issue Date: 2010
Publisher: SciTePress
Source: 2th International Conference on Enterprise Information Systems, Funchal, Portugal, 8-12 June 2010
Abstract: This paper investigates the utilization of Genetic Programming (GP) as a method to facilitate better software cost modeling and estimation. The aim is to produce and examine candidate solutions in the form of representations that utilize operators and operands, which are then used in algorithmic cost estimation. These solutions essentially constitute regression equations of software cost factors, used to effectively estimate the dependent variable, that is, the effort spent for developing software projects. The GP application generates representative rules through which the usefulness of various project characteristics as explanatory variables, and ultimately as predictors of development effort is investigated. The experiments conducted are based on two publicly available empirical datasets typically used in software cost estimation and indicate that the proposed approach provides consistent and successful results.
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/3843
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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