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%
