Three empirical studies on predicting software maintainability using ensemble methods

Verfasser / Beitragende:
[Mahmoud Elish, Hamoud Aljamaan, Irfan Ahmad]
Ort, Verlag, Jahr:
2015
Enthalten in:
Soft Computing, 19/9(2015-09-01), 2511-2524
Format:
Artikel (online)
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024 7 0 |a 10.1007/s00500-014-1576-2  |2 doi 
035 |a (NATIONALLICENCE)springer-10.1007/s00500-014-1576-2 
245 0 0 |a Three empirical studies on predicting software maintainability using ensemble methods  |h [Elektronische Daten]  |c [Mahmoud Elish, Hamoud Aljamaan, Irfan Ahmad] 
520 3 |a 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. 
540 |a Springer-Verlag Berlin Heidelberg, 2015 
690 7 |a Computational intelligence  |2 nationallicence 
690 7 |a Ensemble techniques  |2 nationallicence 
690 7 |a Homogenous ensemble  |2 nationallicence 
690 7 |a Heterogeneous ensemble  |2 nationallicence 
690 7 |a Software maintenance  |2 nationallicence 
690 7 |a Prediction  |2 nationallicence 
690 7 |a Empirical studies  |2 nationallicence 
700 1 |a Elish  |D Mahmoud  |u Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia  |4 aut 
700 1 |a Aljamaan  |D Hamoud  |u Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia  |4 aut 
700 1 |a Ahmad  |D Irfan  |u Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia  |4 aut 
773 0 |t Soft Computing  |d Springer Berlin Heidelberg  |g 19/9(2015-09-01), 2511-2524  |x 1432-7643  |q 19:9<2511  |1 2015  |2 19  |o 500 
856 4 0 |u https://doi.org/10.1007/s00500-014-1576-2  |q text/html  |z Onlinezugriff via DOI 
898 |a BK010053  |b XK010053  |c XK010000 
900 7 |a Metadata rights reserved  |b Springer special CC-BY-NC licence  |2 nationallicence 
908 |D 1  |a research-article  |2 jats 
949 |B NATIONALLICENCE  |F NATIONALLICENCE  |b NL-springer 
950 |B NATIONALLICENCE  |P 856  |E 40  |u https://doi.org/10.1007/s00500-014-1576-2  |q text/html  |z Onlinezugriff via DOI 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Elish  |D Mahmoud  |u Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Aljamaan  |D Hamoud  |u Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Ahmad  |D Irfan  |u Information and Computer Science Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia  |4 aut 
950 |B NATIONALLICENCE  |P 773  |E 0-  |t Soft Computing  |d Springer Berlin Heidelberg  |g 19/9(2015-09-01), 2511-2524  |x 1432-7643  |q 19:9<2511  |1 2015  |2 19  |o 500