caa a22 4500 605468796 CHVBK 20210128100318.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s00500-014-1576-2 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1576-2 Three empirical studies on predicting software maintainability using ensemble methods [Elektronische Daten] [Mahmoud Elish, Hamoud Aljamaan, Irfan Ahmad] More accurate prediction of software maintenance effort contributes to better management and control of software maintenance. Several research studies have recently investigated the use of computational intelligence models for software maintainability prediction. The performance of these models, however, may vary from dataset to dataset. Consequently, ensemble methods have become increasingly popular as they take advantage of the capabilities of their constituent computational intelligence models toward a dataset to come up with more accurate or at least competitive prediction accuracy compared to individual models. This paper investigates and empirically evaluates different homogenous and heterogeneous ensemble methods in predicting software maintenance effort and change proneness. Three major empirical studies were designed and conducted taken into consideration different design such as the types of the investigated ensembles methods, types of prediction problems, used datasets, and other experimental setup. Overall empirical evidence obtained from the three studies confirms that some ensemble methods provide more accurate or at least competitive prediction accuracy compared to individual models across datasets, and thus they are more reliable. Springer-Verlag Berlin Heidelberg, 2015 Computational intelligence nationallicence Ensemble techniques nationallicence Homogenous ensemble nationallicence Heterogeneous ensemble nationallicence Software maintenance nationallicence Prediction nationallicence Empirical studies nationallicence Elish Mahmoud Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia aut Aljamaan Hamoud Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia aut Ahmad Irfan Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia aut Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2511-2524 1432-7643 19:9<2511 2015 19 500 https://doi.org/10.1007/s00500-014-1576-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1576-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Elish Mahmoud Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia aut NATIONALLICENCE 700 1- Aljamaan Hamoud Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia aut NATIONALLICENCE 700 1- Ahmad Irfan Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2511-2524 1432-7643 19:9<2511 2015 19 500