Can Genetic Programming improve Software Effort Estimation? A Comparative Evaluation

Burgess C.J.; Lefley M.; Zhang Du; Tsai Jeffrey J.P.

Can Genetic Programming improve Software Effort Estimation? A Comparative Evaluation

Burgess C.J.
, Lefley M.
, Zhang Du
, Tsai Jeffrey J.P.

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

Abstract

Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently attention has turned to a variety of machine learning methods. This paper attempts to critically evaluate the potential of genetic programming (GP) in software effort estimation when compared with previously published approaches. The comparison is based on the well-known Desharnais data set of 81 software projects derived from a Canadian software house in the late 1980s. It shows that GP can offer some significant improvements in accuracy and has the potential to be a valid additional tool for software effort estimation.

Translated title of the contribution	Can Genetic Programming improve Software Effort Estimation? A Comparative Evaluation
Original language	English
Title of host publication	Machine Learning Applications In Software Engineering: Series on Software Engineering and Knowledge Engineering
Publisher	World Scientific Publishing Co.
Volume	16
ISBN (Print)	9812560947
Publication status	Published - 2005

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Other page information: 95-105
Other identifier: 2000240

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title = "Can Genetic Programming improve Software Effort Estimation? A Comparative Evaluation",

abstract = " Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently attention has turned to a variety of machine learning methods. This paper attempts to critically evaluate the potential of genetic programming (GP) in software effort estimation when compared with previously published approaches. The comparison is based on the well-known Desharnais data set of 81 software projects derived from a Canadian software house in the late 1980s. It shows that GP can offer some significant improvements in accuracy and has the potential to be a valid additional tool for software effort estimation. ",

author = "Burgess C.J. and Lefley M. and Zhang Du and \{Jeffrey J.P.\}, Tsai",

note = "Other page information: 95-105 Other identifier: 2000240",

year = "2005",

language = "English",

isbn = "9812560947",

volume = "16",

booktitle = "Machine Learning Applications In Software Engineering: Series on Software Engineering and Knowledge Engineering",

publisher = "World Scientific Publishing Co.",

address = "Singapore",

}

Can Genetic Programming improve Software Effort Estimation? A Comparative Evaluation. / C.J., Burgess; M., Lefley; Du, Zhang et al.
Machine Learning Applications In Software Engineering: Series on Software Engineering and Knowledge Engineering. Vol. 16 World Scientific Publishing Co., 2005.

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

TY - CHAP

T1 - Can Genetic Programming improve Software Effort Estimation? A Comparative Evaluation

AU - C.J., Burgess

AU - M., Lefley

AU - Du, Zhang

AU - Jeffrey J.P., Tsai

N1 - Other page information: 95-105 Other identifier: 2000240

PY - 2005

Y1 - 2005

N2 - Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently attention has turned to a variety of machine learning methods. This paper attempts to critically evaluate the potential of genetic programming (GP) in software effort estimation when compared with previously published approaches. The comparison is based on the well-known Desharnais data set of 81 software projects derived from a Canadian software house in the late 1980s. It shows that GP can offer some significant improvements in accuracy and has the potential to be a valid additional tool for software effort estimation.

AB - Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently attention has turned to a variety of machine learning methods. This paper attempts to critically evaluate the potential of genetic programming (GP) in software effort estimation when compared with previously published approaches. The comparison is based on the well-known Desharnais data set of 81 software projects derived from a Canadian software house in the late 1980s. It shows that GP can offer some significant improvements in accuracy and has the potential to be a valid additional tool for software effort estimation.

M3 - Chapter in a book

SN - 9812560947

VL - 16

BT - Machine Learning Applications In Software Engineering: Series on Software Engineering and Knowledge Engineering

PB - World Scientific Publishing Co.

ER -

Can Genetic Programming improve Software Effort Estimation? A Comparative Evaluation

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