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 |
Bibliographical note
Other page information: 95-105Other identifier: 2000240