University of Bahrain
Scientific Journals

Assuring Software Reuse Success Using Ensemble Machine Learning Algorithms

Show simple item record Hammad, Mustafa Amin, Mariam 2023-01-29T06:47:47Z 2023-01-29T06:47:47Z 2023-01-29
dc.identifier.issn 2210-142X
dc.description.abstract Software reuse is a critical practice that helps software developers to increase their productivity. Also, it reduces the developing effort and project budget. Howsoever, some factors may lead to a software reuse failure. Software development companies have to consider these factors to prevent project failure due to software reuse. These factors are not only related to the technical aspects of the project, but they cover the companies’ managerial decisions too. This work incorporates ensemble machine learning to predict successful software reuse experience. To the best of our knowledge, this the first work that used ensemble learning to predict successful software reuse. Also, a feature selection technique was used to extract the essential attributes from the dataset. The empirical study showed remarkable results that scored an accuracy of 100% en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Software Reuse, Ensemble Leaning, Stacking, Bagging, Voting, Wrapper Subset Evaluation en_US
dc.title Assuring Software Reuse Success Using Ensemble Machine Learning Algorithms en_US
dc.type Article en_US
dc.volume 13 en_US
dc.issue 1 en_US
dc.pagestart 69 en_US
dc.pageend 81 en_US
dc.contributor.authoraffiliation Department of Software Engineering, Mutah University, Al-karak, Jordan en_US
dc.contributor.authoraffiliation Department of Computer Science, University of Bahrain, Sakheer, Kingdom of Bahrain en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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