@conference { ISI:000380431500135,
	title = {RBFN Networks-based Models for Estimating Software Development Effort: A Cross-validation Study},
	booktitle = {2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI)},
	year = {2015},
	note = {IEEE Symposium Series Computational Intelligence, Cape Town, SOUTH AFRICA, DEC 07-10, 2015},
	pages = {976-983},
	publisher = {IEEE; IEEE Computational Intelligence Soc; IEEE BigData},
	organization = {IEEE; IEEE Computational Intelligence Soc; IEEE BigData},
	abstract = {Software effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. Several estimation techniques have been developed in this regard and it is difficult to determine which model gives more accurate estimation on which dataset. Among all proposed methods, the Radial Basis Function Neural (RBFN) networks models have presented promising results in software effort estimation. The main objective of this research is to evaluate the RBFN networks construction based on both hard and fuzzy C-means clustering algorithms using cross-validation approach. The objective of this replication study is to investigate if the RBFN-based models learned from the training data are able to estimate accurately the efforts of yet unseen data. This evaluation uses two historical datasets, namely COCOMO81 and ISBSG R8.},
	isbn = {978-1-4799-7560-0},
	doi = {10.1109/SSCI.2015.142},
	author = {Idri, Ali and Hassani, Aya and Abran, Alain}
}
@conference { ISI:000380431500128,
	title = {RBFN Networks-based Models for Estimating Software Development Effort: A Cross-validation Study},
	booktitle = {2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI)},
	year = {2015},
	note = {IEEE Symposium Series Computational Intelligence, Cape Town, SOUTH AFRICA, DEC 07-10, 2015},
	pages = {925-932},
	publisher = {IEEE; IEEE Computational Intelligence Soc; IEEE BigData},
	organization = {IEEE; IEEE Computational Intelligence Soc; IEEE BigData},
	abstract = {Software effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. Several estimation techniques have been developed in this regard and it is difficult to determine which model gives more accurate estimation on which dataset. Among all proposed methods, the Radial Basis Function Neural (RBFN) networks models have presented promising results in software effort estimation. The main objective of this research is to evaluate the RBFN networks construction based on both hard and fuzzy C-means clustering algorithms using cross-validation approach. The objective of this replication study is to investigate if the RBFN-based models learned from the training data are able to estimate accurately the efforts of yet unseen data. This evaluation uses two historical datasets, namely COCOMO81 and ISBSG R8.},
	isbn = {978-1-4799-7560-0},
	doi = {10.1109/SSCI.2015.136},
	author = {Idri, Ali and Hassani, Aya and Abran, Alain}
}
