| Título | A genetic algorithm based framework for software effort prediction |
| Tipo de publicación | Artículo de revista académica |
| Año de publicación | 2017
|
| Autores | |
| Revista académica | Journal of Software Engineering Research and Development
|
| Volumen | 5
|
| Número de páginas | 4
|
| Fecha de publicación | May
|
| Numero ISSN | 2195-1721
|
| URL | https://doi.org/10.1186/s40411-017-0037-x
|
| DOI | 10.1186/s40411-017-0037-x
|
| Palabras clave | Effort prediction model
Empirical study
function points
Genetic approach
ISBSG dataset
Learning schemes
Machine learning
Software effort estimation
|
| Resumen | Several prediction models have been proposed in the literature using different techniques obtaining different results in different contexts. The need for accurate effort predictions for projects is one of the most critical and complex issues in the software industry. The automated selection and the combination of techniques in alternative ways could improve the overall accuracy of the prediction models. |
