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 proceedingChapter 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 contributionCan Genetic Programming improve Software Effort Estimation? A Comparative Evaluation
Original languageEnglish
Title of host publicationMachine Learning Applications In Software Engineering: Series on Software Engineering and Knowledge Engineering
PublisherWorld Scientific Publishing Co.
Volume16
ISBN (Print)9812560947
Publication statusPublished - 2005

Bibliographical note

Other page information: 95-105
Other identifier: 2000240

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