A genetic programming approach to software cost modeling and estimation - Papatheocharous, Efi; Iasonos, Angela; Andreou, Andreas S. (My University, 2010)
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.
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