@article { ISI:000392285600072,
	title = {Improved estimation of software development effort using Classical and Fuzzy Analogy ensembles},
	journal = {APPLIED SOFT COMPUTING},
	volume = {49},
	year = {2016},
	month = {DEC},
	pages = {990-1019},
	abstract = {Delivering an accurate estimate of software development effort plays a decisive role in successful management of a software project. Therefore, several effort estimation techniques have been proposed including analogy based techniques. However, despite the large number of proposed techniques, none has outperformed the others in all circumstances and previous studies have recommended generating estimation from ensembles of various single techniques rather than using only one solo technique. Hence, this paper proposes two types of homogeneous ensembles based on single Classical Analogy or single Fuzzy Analogy for the first time. To evaluate this proposal, we conducted an empirical study with 100/60 variants of Classical/Fuzzy Analogy techniques respectively. These variants were assessed using standardized accuracy and effect size criteria over seven datasets. Thereafter, these variants were clustered using the Scott-Knott statistical test and ranked using four unbiased errors measures. Moreover, three linear combiners were used to combine the single estimates. The results show that there is no best single Classical/Fuzzy Analogy technique across all datasets, and the constructed ensembles (Classical/Fuzzy Analogy ensembles) are often ranked first and their performances are, in general, higher than the single techniques. Furthermore, Fuzzy Analogy ensembles achieve better performance than Classical Analogy ensembles and there is no best Classical/Fuzzy ensemble across all datasets and no evidence concerning the best combiner. (C) 2016 Elsevier B.V. All rights reserved.},
	issn = {1568-4946},
	doi = {10.1016/j.asoc.2016.08.012},
	author = {Idri, Ali and Hosni, Mohamed and Abran, Alain}
}
@article { ISI:000378664500010,
	title = {Systematic literature review of ensemble effort estimation},
	journal = {JOURNAL OF SYSTEMS AND SOFTWARE},
	volume = {118},
	year = {2016},
	month = {AUG},
	pages = {151-175},
	abstract = {The need to overcome the weaknesses of single estimation techniques for prediction tasks has given rise to ensemble methods in software development effort estimation (SDEE). An ensemble effort estimation (EEE) technique combines several of the single/classical models found in the SDEE literature. However, to the best of our knowledge, no systematic review has yet been performed with a focus on the use of EEE techniques in SDEE. The purpose of this review is to analyze EEE techniques from six viewpoints: single models used to construct ensembles, ensemble estimation accuracy, rules used to combine single estimates, accuracy comparison of EEE techniques with single models, accuracy comparison between EEE techniques and methodologies used to construct ensemble methods. We performed a systematic review of EEE studies published between 2000 and 2016, and we selected 24 of them to address the questions raised in this review. We found that EEE techniques may be separated into two types: homogeneous and heterogeneous, and that the machine learning single models are the most frequently employed in constructing EEE techniques. We also found that EEE techniques usually yield acceptable estimation accuracy, and in fact are more accurate than single models. (C) 2016 Elsevier Inc. All rights reserved.},
	issn = {0164-1212},
	doi = {10.1016/j.jss.2016.05.016},
	author = {Idri, Ali and Hosni, Mohamed and Abran, Alain}
}
@conference { ISI:000391107900013,
	title = {Systematic Mapping Study of Ensemble Effort Estimation},
	booktitle = {ENASE: PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON EVALUATION OF NOVEL SOFTWARE APPROACHES TO SOFTWARE ENGINEERING},
	year = {2016},
	note = {11th International Conference on Evaluation of Novel Software Approaches to Software Engineering, Rome, ITALY, APR 27-28, 2016},
	pages = {132-139},
	abstract = {Ensemble methods have been used recently for prediction in data mining area in order to overcome the weaknesses of single estimation techniques. This approach consists on combining more than one single technique to predict a dependent variable and has attracted the attention of the software development effort estimation (SDEE) community. An ensemble effort estimation (EEE) technique combines several existing single/classical models. In this study, a systematic mapping study was carried out to identify the papers based on EEE techniques published in the period 2000-2015 and classified them according to five classification criteria: research type, research approach, EEE type, single models used to construct EEE techniques, and rule used the combine single estimates into an EEE technique. Publication channels and trends were also identified. Within the 16 studies selected, homogeneous EEE techniques were the most investigated. Furthermore, the machine learning single models were the most frequently employed to construct EEE techniques and two types of combiner (linear and non-linear) have been used to get the prediction value of an ensemble.},
	isbn = {978-989-758-189-2},
	doi = {10.5220/0005822701320139},
	author = {Idri, Ali and Hosni, Mohamed and Abran, Alain},
	editor = {Maciaszek, L and Filipe, J}
}
