@article {739,
	title = {A genetic algorithm based framework for software effort prediction},
	journal = {Journal of Software Engineering Research and Development},
	volume = {5},
	year = {2017},
	month = {May},
	pages = {4},
	abstract = {<p>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.</p>},
	keywords = {Effort prediction model, Empirical study, function points, Genetic approach, ISBSG dataset, Learning schemes, Machine learning, Software effort estimation},
	issn = {2195-1721},
	doi = {10.1186/s40411-017-0037-x},
	url = {https://doi.org/10.1186/s40411-017-0037-x},
	author = {Murillo-morera, Juan and Quesada-L{\'o}pez, Christian and Castro-Herrera, Carlos and Jenkins, Marcelo}
}
