| Título | A genetic algorithm based framework for software effort prediction |
| Tipo de publicación | Journal Article |
| Year of Publication | 2017 |
| Autores | Murillo-morera, J, Quesada-López, C, Castro-Herrera, C, Jenkins, M |
| Journal | Journal of Software Engineering Research and Development |
| Volumen | 5 |
| Pagination | 4 |
| Date Published | May |
| ISSN | 2195-1721 |
| 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. |
| URL | https://doi.org/10.1186/s40411-017-0037-x |
| DOI | 10.1186/s40411-017-0037-x |
