Software project effort estimation using genetic programming

Author

Shan, Y. ; McKay, R.J. ; Lokan, C.J. ; Essam, D.L.

Author_Institution

Sch. of Comput. Sci., Univ. of New South Wales, Canberra, ACT, Australia

Volume

2

fYear

2002

fDate

29 June-1 July 2002

Firstpage

1108

Abstract

Knowing the estimated cost of a software project early in the development cycle is a valuable asset for management. In this paper, an evolutionary computation method, grammar guided genetic programming (GGGP), is used to fit models, with the aim of improving the prediction of software development costs. Valuable results are obtained, significantly better than those obtained by simple linear regression. In this research, GGGP, because of its flexibility and the ability of incorporating background knowledge, also shows great potential in being applied in other software engineering modeling problems.

Keywords

evolutionary computation; genetic algorithms; grammars; project management; software cost estimation; software development management; GGGP background knowledge; GGGP evolutionary computation methods; grammar guided genetic programming; linear regression; model fitting; software cost prediction; software development cycle management; software engineering modeling; software project cost estimation; software project effort estimation; Asset management; Australia; Computer science; Costs; Drives; Genetic programming; Predictive models; Project management; Software development management; Software engineering;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications, Circuits and Systems and West Sino Expositions, IEEE 2002 International Conference on

Print_ISBN

0-7803-7547-5

Type

conf

DOI

10.1109/ICCCAS.2002.1178979

Filename

1178979